Climate Change, Variability and Prediction: Recent Publications
Published (Present to 2007)
Best, Martin, Adrian P Lock, Gianpaolo Balsamo, Eric Bazile, Isabelle Beau, Joan Cuxart, Michael Ek, Kirsten L Findell, Ann M Fridlind, Albert A M Holtslag, Wenyan Huang, Maria A Jiménez, Sanjiv Kumar, David Lawrence, and Sergey Malyshev, et al., March 2025: Rolling DICE to advance knowledge of land–atmosphere interactions. Quarterly Journal of the Royal Meteorological Society, doi:10.1002/qj.4944. [ Abstract ]
The Diurnal Land–Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land-surface and atmospheric boundary-layer modelling groups. A simple three-stage methodology is designed to assess land–atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand-alone models to variations in driving data is explored. For this initial study, a 3-day clear-sky period in the mid-west United States over, an assumed simple, predominantly grass surface was simulated using data from the CASES-99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within land-surface models needs attention; (2) the height of atmospheric forcing for land-surface models is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land–atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity, and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture, and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary-layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the Global Land–Atmosphere Coupling Experiment.
Clark, Joseph P., Nathaniel C Johnson, Mingyu Park, Miguel Bernardez, and Thomas L Delworth, 2025: Predictable patterns of seasonal atmospheric river variability over North America during winter. Geophysical Research Letters, 52(7), doi:10.1029/2024GL112411. [ Abstract ]
Atmospheric rivers (ARs) are elongated areas of pronounced atmospheric water vapor transport that play an important role in the hydrological cycle over North America during winter. We investigate the sources of winter seasonal AR predictability over North America using average predictability time (APT) analysis. The skill of seasonal AR frequency predictions, in dynamical model forecasts provided by the Seamless System for Prediction and Earth System Research, is nearly entirely attributable to three physically interpretable APT modes that together represent about 19% of the total seasonal AR frequency variance. These three modes represent the AR response to the El Niño-Southern Oscillation, anthropogenic forcing and equatorial heating over the eastern flank of the western Pacific warm pool, respectively. We further show that these three modes, calculated from AR frequency, explain nearly all winter seasonal precipitation forecast skill over North America.
Gu, Binglan, Sha Zhou, Bofu Yu, Kirsten L Findell, and Benjamin R Lintner, January 2025: Multifaceted changes in water availability with a warmer climate. npj Climate and Atmospheric Science, 8, 31, doi:10.1038/s41612-025-00913-4. [ Abstract ]
Climate warming alters spatial and seasonal patterns of surface water availability (P-E), affecting runoff and terrestrial water storage. However, a comprehensive assessment of these changes across various hydroclimates remains lacking. We develop a multi-model ensemble approach to classify global terrestrial hydroclimate into four distinct regimes based on the mean and seasonality of P-E. P-E is projected to become increasingly variable across space and time. Wet regions with low and high seasonality are likely to experience more concentrated increases in wet-season runoff by up to 20%, highlighting potential increases in flood-related vulnerability. Low-seasonality regions exhibit faster wet-season increases and more rapid dry-season decreases in soil moisture, heightening the likelihood of water scarcity and drought. Conversely, dry regions with high seasonality are less sensitive to climate change. These findings underscore the multifaceted impacts of climate change on global water resources, necessitating the need for tailored adaptation strategies for different hydroclimate regimes.
Atmospheric vapor pressure deficit (VPD) measures the difference between saturation vapor pressure and actual vapor pressure, and its variability is closely related to fire activity in the western United States (US). Here, we assess the forecast skill of monthly VPD variability using a state-of-the-art dynamical forecast system and statistical predictions, such as the persistence forecast and model-analog forecasts. In the model-analog framework, we select analog states resembling the observed initial conditions from the model space, and the subsequent evolution of those initial model-analogs yields forecast ensembles. Dynamical forecasts demonstrate skillful predictions of VPD variability in the western US, exceeding the persistence forecast skill, which indicates additional sources of VPD predictability within the climate system. To quantify the contribution of different climate variables to VPD prediction, we develop a weighted model-analog forecast and evaluate its skill in comparison to VPD-only and unweighted forecasts. Our findings suggest that sea surface temperature is a critical source of VPD predictability over the western US. The optimally weighted model-analog exhibits forecast skill for VPD variability comparable to that of the dynamical forecast system.
Minobe, Shoshiro, Erik Behrens, Kirsten L Findell, Norman G Loeb, Benoit Meyssignac, and Rowan Sutton, April 2025: Global and regional drivers for exceptional climate extremes in 2023-2024: Beyond the new normal. npj Climate and Atmospheric Science, 8, 138, doi:10.1038/s41612-025-00996-z. [ Abstract ]
Climate records have been broken with alarming regularity in recent years, but the events of 2023–2024 were exceptional even when accounting for recent climatic trends. Here we quantify these events across multiple variables and show how excess energy accumulation in the Earth system drove the exceptional conditions. Key factors were the positive decadal trend in Earth’s Energy Imbalance (EEI), persistent La Niña conditions beginning in 2020, and the switch to El Niño in 2023. Between 2022 and 2023, the heating from EEI was over 75% larger than during the onset of similar recent El Niño events. We show further how regional processes shaped distinct patterns of record-breaking sea surface temperatures in individual ocean basins. If the recent trend in EEI is maintained, we argue that natural fluctuations such as ENSO cycles will increasingly lead to amplified, record-breaking impacts, with 2023–2024 serving as a glimpse of future climate extremes.
The seasonal prediction skill of tropical cyclone (TC) activity is evaluated using the Seamless System for Prediction and Earth System Research (SPEAR), a modeling system developed at the Geophysical Fluid Dynamics Laboratory (GFDL) for experimental real-time seasonal forecasts. Compared with previous GFDL seasonal prediction models, SPEAR demonstrates improved skill in predicting TC activity for the western North Pacific, while exhibiting comparable or slightly degraded skill for the eastern North Pacific and North Atlantic. These changes in prediction skill do not always align with changes in prediction skill in large-scale variables, particularly over the North Atlantic. This study highlights that changes in the model’s response of TCs to large-scale variables, as well as the changes in the amplitude of interannual variations in TC genesis frequency, are crucial for the changes in TC prediction skill. Using the predicted sea surface temperatures from SPEAR as lower boundary conditions, the High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR-S) was employed to predict intense TCs, demonstrating skillful predictions of major hurricanes that are comparable to the previous HiFLOR coupled model predictions.
Aerosol effects on precipitation are crucial factors in climate change, yet they remain poorly understood, representing a large source of uncertainty in climate models. In the GFDL Earth System Model 4 (ESM4), simulated historical century-scale trends of global land precipitation demonstrate significant drying biases compared to observations, even when imposing observed historical variations of sea surface temperature and sea ice concentrations (LongAMIP simulations). These biases manifest as overestimated decreasing trends in precipitation over tropical-subtropical land and underestimated increases in higher latitudes. In this study, we investigate the “fast response” of land precipitation to historical anthropogenic aerosol emissions and its contributions to the model trend biases, by conducting idealized ESM4 LongAMIP experiments with emissions of either black carbon (BC) or sulfate (SO4) aerosol precursors set to near-pre-industrial levels (1850). Local aerosol effects, occurring through alteration of atmospheric energy balance and circulation, emerge as critical drivers of excessive precipitation declines in the LongAMIP runs in some regions: (1) Over East Asia, a negative SO4 effect and a positive BC effect contribute to the simulated negative trend bias in LongAMIP. (2) For regions of Africa, the negative fast response to BC and SO4 partially contributes to the overestimated precipitation decline. (3) Over west-central North America, the negative fast response to BC in the model contributes toward underestimating a modest observed increasing precipitation trend. However, over South Asia, eastern North America and Northwest Eurasia, the fast responses of precipitation to aerosols cannot account for the LongAMIP model bias (in the opposite direction), indicating the dominant influence of other factors.
Baker, Rachel E., Wenchang Yang, Gabriel A Vecchi, and Saki Takahashi, July 2024: Increasing intensity of enterovirus outbreaks projected with climate change. Nature Communications, 15, 6466, doi:10.1038/s41467-024-50936-3. [ Abstract ]
Pathogens of the enterovirus genus, including poliovirus and coxsackieviruses, typically circulate in the summer months suggesting a possible positive association between warmer weather and transmission. Here we evaluate the environmental and demographic drivers of enterovirus transmission, as well as the implications of climate change for future enterovirus circulation. We leverage pre-vaccination era data on polio in the US as well as data on two enterovirus A serotypes in China and Japan that are known to cause hand, foot, and mouth disease. Using mechanistic modeling and statistical approaches, we find that enterovirus transmission appears positively correlated with temperature although demographic factors, particularly the timing of school semesters, remain important. We use temperature projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate future outbreaks under late 21st-century climate change for Chinese provinces. We find that outbreak size increases with climate change on average, though results differ across climate models depending on the degree of wintertime warming. In the worst-case scenario, we project peak outbreaks in some locations could increase by up to 40%.
Eusebi, Ryan, Gabriel A Vecchi, Ching-Yao Lai, and Mingjing Tong, January 2024: Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning. Communications Earth and Environment, 5, 8, doi:10.1038/s43247-023-01144-2. [ Abstract ]
Tropical cyclones are responsible for large-scale loss of life and property1,2,3,4, motivating accurate risk assessment and forecasting. These objectives require accurate reconstructions of storms’ wind and pressure fields which assimilate real-time observations5,6,7,8,9, but current methods used for these reconstructions remain computationally expensive and limited10. Here, we show that a physics-informed neural network11,12 can be a promising and computationally efficient algorithm for tropical cyclone data assimilation. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a physics-informed neural network is able to reconstruct full realistic 2- and 3-dimensional wind and pressure fields which capture key features of the cyclone. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results highlight how recent advances in deep learning can augment data assimilation schemes. The methods are also general and can be applied to other flow problems.
Findell, Kirsten L., and Zun Yin, et al., February 2024: Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output. Geoscientific Model Development, 17(4), doi:10.5194/gmd-17-1869-20241869–1883. [ Abstract ]
Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics.
Gu, Peng, Zhengyu Liu, and Thomas L Delworth, April 2024: Strong oceanic forcing on decadal surface temperature variability over global ocean. Geophysical Research Letters, 51(8), doi:10.1029/2023GL107401. [ Abstract ]
Sea surface temperature (SST) variability on decadal timescales has been associated with global and regional climate variability and impacts. The mechanisms that drive decadal SST variability, however, remain highly uncertain. Many previous studies have examined the role of atmospheric variability in driving decadal SST variations. Here we assess the strength of oceanic forcing in driving decadal SST variability in observations and state-of-the-art climate models by analyzing the relationship between surface heat flux and SST. We find a largely similar pattern of decadal oceanic forcing across all ocean basins, characterized by oceanic forcing about twice the strength of the atmospheric forcing in the mid- and high latitude regions, but comparable or weaker than the atmospheric forcing in the subtropics. The decadal oceanic forcing is hypothesized to be associated with the wind-driven oceanic circulation, which is common across all ocean basins.
Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction.
Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth’s surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.
Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.
The East/Japan Sea (EJS), a marginal sea of the Northwestern Pacific, is one of the ocean regions showing the most rapid warming and greatest increases in ocean heatwaves over the last several decades. Predictability and skillful prediction of the summer season EJS variability are crucial, given the increasing severity of ocean temperature events impacting fisheries and reinforcing climate conditions like the East Asian rainy season, which in turn affects adjacent high-population density areas over East Asia. We use observations and the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system to investigate the summertime EJS Sea Surface Temperature (SST) predictability and prediction skill. The observations and seasonal prediction system show that the summer season EJS SST can be closely linked to the previous winter air-sea coupling and predictable 8–9 months in advance. The SPEAR seasonal prediction system demonstrates skillful forecast of EJS SST events from summer to late fall, with added skill for long-lead forecasts initialized in winter. We find that winter large-scale atmospheric circulations linked to Barents Sea variability can induce persistent surface wind anomalies and corresponding northward Ekman heat transport over the East China Sea. The ocean advection anomalies that enter the EJS in prior seasons appear to play a role in developing anomalous SST during summer, along with instantaneous atmospheric forcing, as the source of long-lead predictability. Our findings provide potential applications of large-scale ocean-atmosphere interactions in understanding and predicting seasonal variability of East Asian marginal seas.
The Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long-term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25-km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic-scale events, including atmospheric rivers (AR) and tropical cyclone (TC)-related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25-km GFDL-SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC-related events, especially those undergoing extratropical transitions. In the future, both AR-related and TC-related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5-8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC-related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC-related extreme precipitation over the NEUS.
Kortum, Grace, Gabriel A Vecchi, Tsung-Lin Hsieh, and Wenchang Yang, March 2024: Influence of weather and climate on multidecadal trends in Atlantic hurricane genesis and tracks. Journal of Climate, 37(5), doi:10.1175/JCLI-D-23-0088.11501–1522. [ Abstract ]
This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location.
The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi-decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse-resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream.
To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time-evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station-based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.
A key consideration for evaluating climate projections is uncertainty in future radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare observations with specified climate scenarios, it remains less obvious how to detect and attribute regional pattern changes with plausible future mitigation scenarios. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use a feature attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. Despite substantial atmospheric internal variability, the neural network learns to identify “fingerprint” patterns, including significant localized regions of change, that associate specific patterns of climate change with radiative forcing scenarios in each year of the simulations. We illustrate this using output from additional ensembles with sharp reductions in future greenhouse gases and highlight specific regions (in this example, the subpolar North Atlantic and Central Africa) that are critical for associating the new simulations with changes in radiative forcing scenarios. Overall, this framework suggests that explainable machine learning could provide one strategy for detecting a regional climate response to future mitigation efforts.
Stakeholders need high-resolution urban climate information for city planning and adaptation to climate risks. Climate models have too coarse a spatial resolution to properly represent cities at the relevant scale, and downscaled products often fail to account for urban effects. We propose here a methodological framework for producing high-resolution urban databases that are used to drive the SURFEX-TEB land surface and urban canopy models. A historical simulation is carried out over the period 1991–2020, based on a reanalysis of the city of Philadelphia (Pennsylvania, USA). The simulation is compared with observations outside and inside the city, as well as with a field campaign. The results show good agreement between the model and observations, with average summer biases of only −1 °C and + 0.8 °C for daily minimum and maximum temperatures outside the city, and almost none inside. The simulation is used to calculate the maximum daily heat index (HIX) and to study emergency heat alerts. The HIX is slightly overestimated and, consequently, the model simulates too many heat events if not bias corrected. Overall, HIX conditions at Philadelphia International Airport are found to be suitable proxies for city-wide summer conditions, and therefore are appropriate to use for emergency heat declarations.
Lee, Jiwoo, Peter J Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A Ullrich, Kenneth R Sperber, Karl E Taylor, Yann Y Planton, Eric Guilyardi, Paul J Durack, Céline Bonfils, Mark D Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F Wehner, Angeline G Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T Wittenberg, and John P Krasting, May 2024: Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3. Geoscientific Model Development, 17(9), doi:10.5194/gmd-17-3919-20243919–3948. [ Abstract ]
Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.
While the changes in ocean heat uptake in a warming climate have been well explored, the changes in response to climate mitigation efforts remain unclear. Using coupled climate model simulations, here we find that in response to a hypothesized reduction of greenhouse gases in the late 21st century, ocean heat uptake would significantly decline in all ocean basins except the North Atlantic, where a persistently weakened Atlantic meridional overturning circulation results in sustained heat uptake. These prolonged circulation anomalies further lead to interbasin heat exchanges, characterized by a sustained heat export from the Atlantic to the Southern Ocean and a portion of heat transfer from the Southern Ocean to the Indo-Pacific. Due to ocean heat uptake decline and interbasin heat export, the Southern Ocean experiences the strongest decline in ocean heat storage therefore emerging as the primary heat exchanger, while heat changes in the Indo-Pacific basin are relatively limited.
Precipitation changes in full response to CO2 increase are widely studied but confidence in future projections remains low. Mechanistic understanding of the direct radiative effect of CO2 on precipitation changes, independent from CO2-induced SST changes, is therefore necessary. Utilizing global atmospheric models, we identify robust summer precipitation decreases across North America in response to direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is likely shaped by climatological distribution of water vapor and clouds. This, coupled with local feedback processes, changes in convection, and moisture supply resulting from CO2-induced circulation changes, could determine North American hydroclimate changes. In central North America, increasing CO2 may decrease summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2-induced shift of subtropical highs generates wet advection, which might mitigate the drying effect from warming.
Menemenlis, Sofia, Gabriel A Vecchi, Kun Gao, James A Smith, and Kai-Yuan Cheng, July 2024: Extreme rainfall risk in Hurricane Ida's extratropical stage: An analysis with convection-permitting ensemble hindcasts. Journal of the Atmospheric Sciences, 81(7), doi:10.1175/JAS-D-23-0160.1. [ Abstract ]
The extratropical stage of Hurricane Ida (2021) brought extreme subdaily rainfall and devastating flooding to parts of eastern Pennsylvania, New Jersey, and New York. We investigate the predictability and character of this event using 31-member ensembles of perturbed initial condition hindcasts with the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth-to-Local Domains (T-SHiELD), a ∼13-km global weather forecast model with a ∼3-km nested grid. At lead times of up to 4 days, the ensembles are able to capture the most extreme observed hourly and daily rainfall accumulations but are negatively biased in the spatial extent of heavy precipitation. Large intraensemble differences in the magnitudes and locations of simulated extremes suggest that although impacts were highly localized, risks were widespread. In Ida’s tropical stage, interensemble spread in extreme hourly rainfall is well predicted by large-scale moisture convergence; by contrast, in Ida’s extratropical stage, the most extreme rainfall is governed by mesoscale processes that exhibit chaotic and diverse forms across the ensembles. Our results are relevant to forecasting and communication in advance of extratropical transition and imply that flood preparedness efforts should account for the widespread possibility of severe localized impacts.
The climate simulation frontier of a global storm-resolving model (GSRM; or k-scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The climate sensitivity, effective radiative forcing, and relative humidity changes are assessed in multiyear atmospheric GSRM simulations with perturbed sea-surface temperatures and/or carbon dioxide concentrations. Our comparisons to conventional climate model results can build confidence in the existing climate models or highlight important areas for additional research. This GSRM’s climate sensitivity is within the range of conventional climate models, although on the lower end as the result of neutral, rather than amplifying, shortwave feedbacks. Its radiative forcing from carbon dioxide is higher than conventional climate models, and this arises from a bias in climatological clouds and an explicitly simulated high-cloud adjustment. Last, the pattern and magnitude of relative humidity changes, simulated with greater fidelity via explicitly resolving convection, are notably similar to conventional climate models.
Antarctic sea ice exerts great influence on Earth’s climate by controlling the exchange of heat, momentum, freshwater, and gases between the atmosphere and ocean. Antarctic sea ice extent has undergone a multidecadal slight increase followed by a substantial decline since 2016. Here we utilize a 300-yr sea ice data assimilation reconstruction and two NOAA/GFDL and five CMIP6 model simulations to demonstrate a multidecadal variability of Antarctic sea ice extent. Stronger westerlies associated with the Southern Annular Mode (SAM) enhance the upwelling of warm and saline water from the subsurface ocean. The consequent salinity increase weakens the upper-ocean stratification, induces deep convection, and in turn brings more subsurface warm and saline water to the surface. This salinity-convection feedback triggered by the SAM provides favorable conditions for multidecadal sea ice decrease. Processes acting in reverse are found to cause sea ice increase, although it evolves slower than sea ice decrease.
Wave interference between transient waves and climatological stationary waves is a useful framework for diagnosing the magnitude of stationary waves. Here, we find that the wave interference over the North Pacific Ocean is an important driver of North American wintertime cold and heavy precipitation extremes in the present climate, but that this relationship is projected to weaken under increasing greenhouse gas emissions. When daily circulation anomalies are in-phase with the climatological mean state, the anomalous transport of heat and moisture causes the enhanced occurrence of cold extremes across the continental U.S. and a significant decrease of heavy precipitation extremes in the western U.S. In a future emissions scenario, the climatological stationary wave over the eastern North Pacific weakens and shifts spatially, which alters and generally weakens the relationship between wave interference and North American climate extremes. Our results underscore that the prediction of changes in regional wave interference is pivotal for understanding the future regional climate variability.
Planton, Yann Y., Jiwoo Lee, Andrew T Wittenberg, Peter J Gleckler, Eric Guilyardi, Shayne McGregor, and Michael J McPhaden, September 2024: Estimating uncertainty in simulated ENSO statistics. Journal of Advances in Modeling Earth Systems, 16(9), doi:10.1029/2023MS004147. [ Abstract ]
Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling-based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre-industrial control or historical runs. This suggests that pre-industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.
Deficiencies in upper ocean vertical mixing parameterizations contribute to tropical upper ocean biases in global coupled general circulation models, affecting their simulated ocean heat uptake and ENSO variability. To better understand these deficiencies, we develop a suite of ocean model experiments including both idealized single column models and realistic global simulations. The vertical mixing parameterizations are first evaluated using large eddy simulations as a baseline to assess uncertainties and evaluate their implied turbulent mixing. Global models are then developed following NOAA/GFDL's 0.25° nominal horizontal grid spacing OM4 (uncoupled) configuration of the MOM6 ocean model, with various modifications that target biases in the original model. We test several enhancements to the existing mixing schemes and evaluate them against observational constraints from Tropical Atmosphere Ocean moorings and Argo floats. In particular, we find that we can improve the diurnal variability of mixing in OM4 via modifications to its surface boundary layer mixing scheme, and can improve the net mixing in the upper thermocline by reducing the background vertical viscosity, allowing for more realistic, less diffuse currents. The improved OM4 model better represents the mixing, leading to improved diurnal deep-cycle variability, a more realistic time-mean tropical thermocline structure, and a better Pacific Equatorial Undercurrent.
Using a 1/12° regional model of the Northwest Atlantic Ocean (MOM6-NWA12), we downscale an ensemble of retrospective seasonal forecasts from a 1° global forecast model. To evaluate whether downscaling improved the forecast skill for surface temperature and salinity and bottom temperature, the global and downscaled forecasts are compared with each other and with a reference forecast of persistence using anomaly correlation. Both sets of forecasts are also evaluated on the basis of mean bias and ensemble spread. We find that downscaling significantly improved the forecast skill for monthly sea surface temperature anomalies in the Northeast US Large Marine Ecosystem, a region that global models have historically struggled to skillfully predict. The downscaled sea surface temperature (SST) predictions for this region were also more skillful than the persistence baseline across most initialization months and lead times. Although some of the SST prediction skill in this region stems from the recent rapid warming trend, prediction skill above persistence is generally maintained after removing the contribution of the trend, and patterns of skill suggestive of predictable processes are also preserved. While downscaling mainly improved the SST anomaly prediction skill in the Northeast US region, it improved bottom temperature and sea surface salinity anomaly skill across many of the marine ecosystems along the North American east coast. Although improvements in anomaly prediction via downscaling were ubiquitous, the effects of downscaling on prediction bias were mixed. Downscaling generally reduced the mean surface salinity biases found in the global model, particularly in regions with sharp salinity gradients (the Northern Gulf of Mexico and the Northeast US). In some cases, however, downscaling amplified the surface and bottom temperature biases found in the global predictions. We discuss several processes that are better resolved in the regional model and contribute to the improved skill, including the autumn reemergence of temperature anomalies and advection of water masses by coastal currents. Overall, the results show that a downscaled high-resolution model can produce improved seasonal forecast skill by representing fine-scale processes that drive predictability.
We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Land Model version 4.1 (LM4.1), which builds on component and coupled model developments over 2013–2019 for the coupled carbon-chemistry-climate Earth System Model Version 4.1 (ESM4.1) simulation as part of the sixth phase of the Coupled Model Intercomparison Project. Analysis of ESM4.1/LM4.1 is focused on biophysical and biogeochemical processes and interactions with climate. Key features include advanced vegetation dynamics and multi-layer canopy energy and moisture exchanges, daily fire, land use representation, and dynamic atmospheric dust coupling. We compare LM4.1 performance in the GFDL Earth System Model (ESM) configuration ESM4.1 to the previous generation component LM3.0 in the ESM2G configuration. ESM4.1/LM4.1 provides significant improvement in the treatment of ecological processes from GFDL's previous generation models. However, ESM4.1/LM4.1 likely overestimates the influence of land use and land cover change on vegetation characteristics, particularly on pasturelands, as it overestimates the competitiveness of grasses versus trees in the tropics, and as a result, underestimates present-day biomass and carbon uptake in comparison to observations.
Sillmann, Jana, Timothy H Raupach, and Kirsten L Findell, et al., December 2024: Climate extremes and risks: links between climate science and decision-making. Frontiers in Climate, 6, doi:10.3389/fclim.2024.1499765. [ Abstract ]
The World Climate Research Programme (WCRP) envisions a future where actionable climate information is universally accessible, supporting decision makers in preparing for and responding to climate change. In this perspective, we advocate for enhancing links between climate science and decision-making through a better and more decision-relevant understanding of climate impacts. The proposed framework comprises three pillars: climate science, impact science, and decision-making, focusing on generating seamless climate information from sub-seasonal, seasonal, decadal to century timescales informed by observed climate events and their impacts. The link between climate science and decision-making has strengthened in recent years, partly owing to undeniable impacts arising from disastrous weather extremes. Enhancing decision-relevant understanding involves utilizing lessons from past extreme events and implementing impact-based early warning systems to improve resilience. Integrated risk assessment and management require a comprehensive approach that encompasses good knowledge about possible impacts, hazard identification, monitoring, and communication of risks while acknowledging uncertainties inherent in climate predictions and projections, but not letting the uncertainty lead to decision paralysis. The importance of data accessibility, especially in the Global South, underscores the need for better coordination and resource allocation. Strategic frameworks should aim to enhance impact-related and open-access climate services around the world. Continuous improvements in predictive modeling and observational data are critical, as is ensuring that climate science remains relevant to decision makers locally and globally. Ultimately, fostering stronger collaborations and dedicated investments to process and tailor climate data will enhance societal preparedness, enabling communities to navigate the complexities of a changing climate effectively.
Sospedra-Alfonso, Reinel, William J Merryfield, Matthew Toohey, Claudia Timmreck, Jean-Paul Vernier, Ingo Bethke, Yiguo Wang, Roberto Bilbao, Markus G Donat, Pablo Ortega, Jason N S Cole, Woo-Sung Lee, Thomas L Delworth, David J Paynter, Fanrong Zeng, and Liping Zhang, et al., December 2024: Decadal prediction centers prepare for a major volcanic eruption. Bulletin of the American Meteorological Society, 105(12), doi:10.1175/BAMS-D-23-0111.1. [ Abstract ]
The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate Prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums, and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Atmospheric Processes and Their Role in Climate (APARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022–26 from their original initialization dates and, in most cases, also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this APARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.
Wei, Xinyue, and Rong Zhang, November 2024: Weakening of the AMOC and strengthening of Labrador Sea deep convection in response to external freshwater forcing. Nature Communications, 15, 10357, doi:10.1038/s41467-024-54756-3. [ Abstract ]
The Atlantic Meridional Overturning Circulation (AMOC) is a key player in climate. Here, we employ an ensemble of water hosing experiments to examine mechanisms of AMOC weakening and its subsequent impact on the Labrador Sea open-ocean deep convection. The subpolar AMOC decline in response to the external freshwater flux released over the southern Nordic Sea is dominated by that across the eastern subpolar North Atlantic, and the largest subpolar AMOC decline is at the relatively dense level around
. The AMOC decline is associated with subsurface cooling in the subpolar North Atlantic and the decline in the deep ocean west–east density contrast across the subpolar basin. Contrary to previous studies showing that the AMOC decline is caused by subsurface warming through the shutdown of the Labrador Sea open-ocean deep convection, our results reveal a different response, i.e., a strengthening of the Labrador Sea open-ocean deep convection, which is not a cause of the AMOC decline. The strengthening of the Labrador Sea open-ocean deep convection is mainly due to the relatively stronger freshening in the deep Labrador Sea associated with the freshening/weakening of the Iceland-Scotland Overflow, and thus reduced vertical stratification in the central Labrador Sea.
Wootten, Adrienne M., Keith W Dixon, Dennis Adams-Smith, and Renee A McPherson, March 2024: False springs and spring phenology: Propagating effects of downscaling technique and training data. International Journal of Climatology, 44(6), doi:10.1002/joc.84382021-2040. [ Abstract ]
Projected changes to spring phenological indicators (such as first leaf and first bloom) are of importance to assessing the impacts of climate change on ecosystems and species. The risk of false springs (when a killing freeze occurs after plants of interest bloom), which can cause ecological and economic damage, is also projected to change across much of the United States. Given the coarse nature of global climate models, downscaled climate projections have commonly been used to assess local changes in spring phenological indices. Few studies that examine the influence of the sources of uncertainty sources in the downscaling approach on projections of phenological changes. This study examines the influence of sources of uncertainty on projections of spring phenological indicators and false spring risk using the South Central United States. The downscaled climate projections were created using three statistical downscaling techniques applied with three gridded observation datasets as training data and three global climate models. This study finds that projections of spring phenological indicators and false spring risk are primarily sensitive to the choice of global climate models. However, this study also finds that the formulation of the downscaling approach can cause errors representing the daily low-temperature distribution, which can cause errors in false spring risk by failing to capture the timing between the last spring freeze and the first bloom. One should carefully consider the downscaling approach used when using downscaled climate projections to assess changes to spring phenology and false spring risk.
Wu, Xian, Stephen G Yeager, Clara Deser, Antonietta Capotondi, Andrew T Wittenberg, and Michael J McPhaden, November 2024: Predictability of tropical Pacific decadal variability is dominated by oceanic Rossby waves. npj Climate and Atmospheric Science, 7, 292, doi:10.1038/s41612-024-00851-7. [ Abstract ]
Despite its pronounced global impacts, tropical Pacific decadal variability (TPDV) is poorly predicted by current climate models due to model deficiencies and a limited understanding of its underlying mechanisms. Using observational data and a hierarchy of model simulations including decadal hindcasts, we find that decadal isopycnal depth variability driven by oceanic Rossby waves in the tropical Pacific provides the most important source of predictability for TPDV. The predictability arising from initial isopycnal depth conditions is further amplified by tropical ocean-atmosphere coupling and variations in the strength of subtropical cells in the Pacific throughout the decadal forecasts. Regional initialization experiments that effectively isolate the impact of different ocean basins on TPDV predictability highlight the essential role of the tropical Pacific. This study enhances our understanding of the mechanisms governing TPDV predictability, offering crucial insights for improving the accuracy of decadal predictions.
Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO.
Yang, Wenchang, Elizabeth Wallace, Gabriel A Vecchi, Jeffrey P Donnelly, Julien Emile-Geay, Gregory J Hakim, Larry W Horowitz, Richard M Sullivan, Robert Tardif, Peter J Van Hengstum, and Tyler S Winkler, January 2024: Last millennium hurricane activity linked to endogenous climate variability. Nature Communications, 15, 816, doi:10.1038/s41467-024-45112-6. [ Abstract ]
Despite increased Atlantic hurricane risk, projected trends in hurricane frequency in the warming climate are still highly uncertain, mainly due to short instrumental record that limits our understanding of hurricane activity and its relationship to climate. Here we extend the record to the last millennium using two independent estimates: a reconstruction from sedimentary paleohurricane records and a statistical model of hurricane activity using sea surface temperatures (SSTs). We find statistically significant agreement between the two estimates and the late 20th century hurricane frequency is within the range seen over the past millennium. Numerical simulations using a hurricane-permitting climate model suggest that hurricane activity was likely driven by endogenous climate variability and linked to anomalous SSTs of warm Atlantic and cold Pacific. Volcanic eruptions can induce peaks in hurricane activity, but such peaks would likely be too weak to be detected in the proxy record due to large endogenous variability.
A key challenge with the wind energy utilization is that winds, and thus wind power, are highly variable on seasonal to interannual timescales because of atmospheric variability. There is a growing need of skillful seasonal wind energy prediction for energy system planning and operation. Here we demonstrate model’s capability in producing skillful seasonal wind energy prediction over the U.S. Great Plains during peak energy seasons (winter and spring), using seasonal prediction products from a climate model. The dominant source of that skillful prediction mainly comes from year-to-year variations of El Niño-Southern Oscillation in the tropical Pacific, which alters large-scale wind and storm track patterns over the United States. In the Southern Great Plains, the model can predict strong year-to-year wind energy changes with high skill multiple months in advance. Thus, this seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during peak energy production seasons.
The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.
Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.
Zhao, Ming, and Thomas R Knutson, June 2024: Crucial role of sea surface temperature warming patterns in near-term high-impact weather and climate projection. npj Climate and Atmospheric Science, 7, 130, doi:10.1038/s41612-024-00681-7. [ Abstract ]
Recent studies indicate that virtually all global climate models (GCMs) have had difficulty simulating sea surface temperature (SST) trend patterns over the past four decades. GCMs produce enhanced warming in the eastern Equatorial Pacific (EPAC) and Southern Ocean (SO) warming, while observations show intensified warming in the Indo-Pacific Warm Pool (IPWP) and slight cooling in the eastern EPAC and SO. Using Geophysical Fluid Dynamics Laboratory’s latest higher resolution atmospheric model and coupled prediction system, we show the model biases in SST trend pattern have profound implications for near-term projections of high-impact storm statistics, including the frequency of atmospheric rivers (AR), tropical storms (TS) and mesoscale convection systems (MCS), as well as for hydrological and climate sensitivity. If the future SST warming pattern continues to resemble the observed pattern from the past few decades rather than the GCM simulated/predicted patterns, our results suggest (1) a drastically different future projection of high-impact storms and their associated hydroclimate changes, especially over the Western Hemisphere, (2) a stronger global hydrological sensitivity, and (3) substantially less global warming due to stronger negative feedback and lower climate sensitivity. The roles of SST trend patterns over the EPAC, IPWP, SO, and the North Atlantic tropical cyclone Main Development Region (AMDR) are isolated, quantified, and used to understand the simulated differences. Specifically, SST trend patterns in the EPAC and AMDR are crucial for modeled differences in AR and MCS frequency, while those in the IPWP and AMDR are essential for differences in TS frequency over the North Atlantic.
Zhao, Sen, Fei-Fei Jin, Malte F Stuecker, Philip R Thompson, Jong-Seong Kug, Michael J McPhaden, Mark Cane, Andrew T Wittenberg, and Wenju Cai, June 2024: Explainable El Niño predictability from climate mode interactions. Nature, 630, doi:10.1038/s41586-024-07534-6. [ Abstract ]
The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1,2,3, yet, quantifying the sources of skilful predictions is a long-standing challenge4,5,6,7. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible8,9,10, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.
Camargo, Suzana J., Hiroyuki Murakami, Nadia Bloemendaal, Savin S Chand, Medha S Deshpande, Christian Dominguez-Sarmiento, Juan Jesús González-Alemán, and Thomas R Knutson, et al., September 2023: An update on the influence of natural climate variability and anthropogenic climate change on tropical cyclones. Tropical Cyclone Research and Review, 12(3), doi:10.1016/j.tcrr.2023.10.001216-239. [ Abstract ]
A substantial number of studies have been published since the Ninth International Workshop on Tropical Cyclones (IWTC-9) in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. These studies have reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scales. However, there are still substantial uncertainties owing to model uncertainty in simulating historical TC decadal variability in the Atlantic, and the limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers.
Couldrey, Matthew P., Jonathan M Gregory, Xiao Dong, Oluwayemi Garuba, Helmuth Haak, Aixue Hu, and William J Hurlin, et al., April 2023: Greenhouse-gas forced changes in the Atlantic meridional overturning circulation and related worldwide sea-level change. Climate Dynamics, 60, doi:10.1007/s00382-022-06386-y2003-2039. [ Abstract ]
The effect of anthropogenic climate change in the ocean is challenging to project because atmosphere-ocean general circulation models (AOGCMs) respond differently to forcing. This study focuses on changes in the Atlantic Meridional Overturning Circulation (AMOC), ocean heat content (ΔOHC), and the spatial pattern of ocean dynamic sea level (Δζ). We analyse experiments following the FAFMIP protocol, in which AOGCMs are forced at the ocean surface with standardised heat, freshwater and momentum flux perturbations, typical of those produced by doubling CO2. Using two new heat-flux-forced experiments, we find that the AMOC weakening is mainly caused by and linearly related to the North Atlantic heat flux perturbation, and further weakened by a positive coupled heat flux feedback. The quantitative relationships are model-dependent, but few models show significant AMOC change due to freshwater or momentum forcing, or to heat flux forcing outside the North Atlantic. AMOC decline causes warming at the South Atlantic-Southern Ocean interface. It does not strongly affect the global-mean vertical distribution of ΔOHC, which is dominated by the Southern Ocean. AMOC decline strongly affects Δζ in the North Atlantic, with smaller effects in the Southern Ocean and North Pacific. The ensemble-mean Δζ and ΔOHC patterns are mostly attributable to the heat added by the flux perturbation, with smaller effects from ocean heat and salinity redistribution. The ensemble spread, on the other hand, is largely due to redistribution, with pronounced disagreement among the AOGCMs.
Changes in tropical (30 S–30 N) land hydroclimate following CO2-induced global warming are organized according to climatological aridity index (AI) and daily soil moisture (SM) percentiles. The transform from geographical space to this novel process-oriented phase space allows for interpretation of local, daily mechanistic relationships between key hydroclimatic variables in the context of time-mean and/or global-mean energetic constraints and the wet-get-wetter/dry-get-drier paradigm. Results from 16 CMIP models show coherent patterns of change in the AI/SM phase space that are aligned with the established soil-moisture/evapotranspiration regimes. We introduce an active-rain regime as a special case of the energy-limited regime. Rainfall shifts toward larger rain totals in this active-rain regime, with less rain on other days, resulting in an overall SM reduction. Consequently, the regimes where SM constrains evapotranspiration become more frequently occupied, and corresponding hydroclimatic changes align with the position of the critical SM value in the AI/SM phase space.
Findell, Kirsten L., et al., January 2023: Explaining and predicting earth system change: A World Climate Research Programme call to action. Bulletin of the American Meteorological Society, 104(1), doi:10.1175/BAMS-D-21-0280.1E325-E339. [ Abstract ]
The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.
High-resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high-resolution (approximately 3 km grid-spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic-scale pattern and thereby to the steering flow. Our results suggest that optimizing small-scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (∼10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high-resolution models, and its often overlooked role in affecting larger-scale circulations and hurricane track prediction.
The response of tropical cyclone (TC) frequency to sea surface warming is uncertain in climate models. We hypothesize that one source of uncertainty is the anomalies of large-scale atmospheric radiation in response to climate change, and whose influence on TC frequency is investigated. Given two atmospheric models with opposite TC frequency responses to uniform sea surface warming, we interchange their atmospheric radiation anomalies in experiments with prescribed radiative heating rates. The largest model discrepancy occurs in the western North Pacific, where the TC frequency tends to increase with anomalous large-scale ascent caused by prescribed positive radiation anomalies, while the TC frequency tends to decrease with anomalous large-scale descent caused by prescribed negative radiation anomalies. The model spread in TC frequency response is approximated by the model spread in the frequency response of pre-TC vortices (seeds), which is explained by changes in the large-scale circulation using a downscaling formula known as the seed propensity index. We further generalize the index to predict the influence of large-scale radiation anomalies on TC seed frequency. The results show that model spread in TC and seed frequency response can be reduced when constraining the large-scale radiation anomalies.
Skillful prediction of wintertime cold extremes on seasonal time scales is beneficial for multiple sectors. This study demonstrates that North American cold extremes, measured by the frequency of cold days in winter, are predictable several months in advance in the Geophysical Fluid Dynamics Laboratory’s SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Three predictable components of cold extremes over the North American continent are found to be skillfully predicted on seasonal scales. One is a trend-like component, which shows a continent-wide decrease in the frequency of cold extremes and is primarily attributable to external radiative forcing. This trend-like component is predictable at least 9 months ahead. The second predictable component displays a dipole structure over North America, with negative signs in the northwest and positive signs in the southeast. This dipole component is predictable with significant correlation skill for 2 months and is a response to the central Pacific ENSO (El Niño-Southern Oscillation) as revealed from SPEAR AMIP-style simulations. The third component with the largest loadings over Canada and the northern US shows significant correlations with snow anomalies over mid-to-high latitudes of the North American continent. Predictions using only the three predictable components yield higher/comparable skill relative to the SPEAR raw forecasts.
Jiang, Feng, Wenjun Zhang, Fei-Fei Jin, Malte F Stuecker, Axel Timmermann, Michael J McPhaden, Julien Boucharel, and Andrew T Wittenberg, July 2023: Resolving the tropical Pacific/Atlantic interaction conundrum. Geophysical Research Letters, 50(13), doi:10.1029/2023GL103777. [ Abstract ]
Understanding the interaction between the tropical Pacific and Atlantic Oceans has challenged the climate community for decades. Typically, boreal summer Atlantic Niño events are followed by vigorous Pacific events of opposite sign around two seasons later. However, incorporating the equatorial Atlantic information to variabilities internal to the Pacific lends no significant additional predictive skill for the subsequent El Niño-Southern Oscillation (ENSO). Here we resolve this conundrum in a physically consistent frame, in which the nascent onset of a Pacific event rapidly induces an opposite-signed summer equatorial Atlantic event and the lead correlation of Atlantic over Pacific is a statistical artifact of ENSO's autocorrelation. This Pacific-to-Atlantic impact is limited to a short window around late spring due to seasonally-amplified Atlantic atmosphere-ocean coupling. This new frame reconciles the discrepancies between the observed and multi-model simulated inter-basin relationship, providing a major advance in understanding seasonally-modulated inter-basin climate connections as well as their predictability.
The Kuroshio-Oyashio Extension (KOE) is the North Pacific oceanic frontal zone where air-sea heat and moisture exchanges allow strong communication between the ocean and atmosphere. Using satellite observations and reanalysis datasets, we show that the KOE surface heat flux variations are very closely linked to Kuroshio Extension (KE) sea surface height (SSH) variability on both seasonal and decadal time scales. We investigate seasonal oceanic and atmospheric anomalies associated with anomalous KE upper ocean temperature, as reflected in SSH anomalies (SSHa). We show that the ocean-induced seasonal changes in air-sea coupled processes, which are accompanied by KE upper-ocean temperature anomalies, lead to significant ocean-to-atmosphere heat transfer during November-December-January (i.e., NDJ). This anomalous NDJ KOE upward heat transfer has recently grown stronger in the observational record, which also appears to be associated with the enhanced KE decadal variability. Highlighting the role of KOE heat fluxes as a communicator between the upper-ocean and the overlying atmosphere, our findings suggest that NDJ KOE heat flux variations could be a useful North Pacific climate indicator.
Johnson, Benjamin O., and Thomas L Delworth, March 2023: The role of the Gulf of California in the North American monsoon. Journal of Climate, 36(6), doi:10.1175/JCLI-D-22-0365.11541-1559. [ Abstract ]
The role of the Gulf of California (GoC) in the North American monsoon (NAM) is investigated using a global climate model with 50-km horizontal atmospheric resolution and prescribed SSTs. Specifically, two 135-yr simulations are compared to quantify the influence of the GoC on the NAM: in the first simulation a realistic representation of the GoC is incorporated, while in the second simulation the GoC is replaced with land surface. The results suggest that the GoC has a significant impact on circulation, with cooler surface air temperatures and lower surface friction allowing for south-southeasterly surface flow along the entire length of the GoC, in turn increasing low-level moisture fluxes into the NAM region. Cooler air over the GoC also leads to lower heights at 700–500 hPa, with a corresponding cyclonic moisture flux anomaly, further increasing moisture fluxes into the NAM region. Correspondingly, precipitation is substantially higher over the NAM region and even east of the Continental Divide in areas such as New Mexico and the U.S. Great Plains. July/August precipitation with a realistic GoC is generally 25%–50% greater in northwestern Mexico than the land-filled case, with precipitation 50% greater in much of the southwestern United States. Due to enhanced surface evaporation, areas with increased precipitation also tend to have lower surface temperatures, higher sea level pressure, and lower mid- to upper-tropospheric heights, thus altering the large-scale circulation. These results highlight the importance of the GoC in the NAM and demonstrate the necessity of resolving the GoC in climate simulations.
Extreme precipitation is among the most destructive natural disasters. Simulating changes in regional extreme precipitation remains challenging, partially limited by climate models’ horizontal resolution. Here, we use an ensemble of high-resolution global climate model simulations to study September–November extreme precipitation over the Northeastern United States, where extremes have increased rapidly since the mid-1990s. We show that a model with 25 km horizontal resolution simulates much more realistic extreme precipitation than comparable models with 50 or 100 km resolution, including frequency, amplitude, and temporal variability. The 25 km model simulated trends are quantitatively consistent with observed trends over recent decades. We use the same model for future projections. By the mid-21st century, the model projects unprecedented rainfall events over the region, driven by increasing anthropogenic radiative forcing and distinguishable from natural variability. Very extreme events (>150 mm/day) may be six times more likely by 2100 than in the early 21st century.
Joshi, Rajat, and Rong Zhang, September 2023: Impacts of the North Atlantic biases on the upper troposphere/lower stratosphere over the extratropical North Pacific. npj Climate and Atmospheric Science, 6, 151, doi:10.1038/s41612-023-00482-4. [ Abstract ]
The winter upper troposphere/lower stratosphere temperature/vertical motion response over the extratropical North Pacific induced by North Atlantic changes is not well understood. Here, using robust diagnostic calculations conducted in a fully coupled high-resolution climate model, we correct the North Atlantic ocean circulation biases and show that during wintertime, the North Atlantic cold surface temperature biases lead to a warmer upper troposphere/lower stratosphere over the extratropical North Pacific. In the upper troposphere/lower stratosphere over the extratropical North Pacific, this winter warming temperature response is linked to the vertical motion response through a simple leading order thermodynamic relationship between changes in the horizontal advection and adiabatic heating. The upper troposphere/lower stratosphere vertical motion response, which is also associated with the North Atlantic induced Walker circulation response over the tropical North Pacific, can provide a rough estimation of the upper troposphere/lower stratosphere warming response over the extratropical North Pacific.
This study examines the potential impacts of large-scale atmospheric circulations that are forced by sea surface temperatures (SST) on global tropical cyclone (TC) formation. Using the Geophysical Fluid Dynamics Laboratory (GFDL) global atmosphere and land surface model, version 4 (AM4), under different SST distributions, it is found that the east–west clustering of global TC formation is mainly governed by large-scale circulations in response to given SSTs, instead of direct ocean surface fluxes associated with zonal SST anomalies. Our zonally homogeneous SST simulations in the presence of realistic surface coverage show that TC clusters still emerge as a result of the breakdown of zonal circulations related to land–sea distribution, which produce specific “hotspots” for global TC formation. Sensitivity experiments with different climate warming scenarios and model physics confirm the persistence of these TC clusters in the absence of all zonal SST variations. These robust results offer new insights into the effects of large-scale circulation and terrain forcing on TC clusters beyond the traditional view of direct SST impacts, which are based on the direct alignment of the warmest SST regions and TC clusters. In addition, our experiments also capture internal variability of the global TC frequency, with an average fluctuation of 6–8 TCs at several dominant frequencies of ∼3, 6, and 9 years, even in the absence of all SST interannual variability and ocean coupling. This finding reveals an intrinsic “noise” level of the global TC frequency that one has to take into account when examining the past and future trends in TC activity and their related significance or detectability.
Lee, Sang-Ki, Hosmay Lopez, Franz Philip Tuchen, Dongmin Kim, Gregory R Foltz, and Andrew T Wittenberg, August 2023: On the genesis of the 2021 Atlantic Niño. Geophysical Research Letters, 50(16), doi:10.1029/2023GL104452. [ Abstract ]
An extreme Atlantic Niño developed in the boreal summer of 2021 with peak-season sea surface temperature anomalies exceeding 1°C in the eastern equatorial region for the first time since global satellite measurements began in the early 1970s. Here, we show that the development of this outlier event was preconditioned by a series of oceanic Rossby waves that reflected at the South American coast into downwelling equatorial Kelvin waves. In early May, an intense week-long westerly wind burst (WWB) event, driven by the Madden-Julian Oscillation (MJO), developed in the western and central equatorial Atlantic and greatly amplified one of the reflected Kelvin waves, directly initiating the 2021 Atlantic Niño. MJO-driven WWBs are fundamental to the development of El Niño in the Pacific but are a previously unidentified driver for Atlantic Niño. Their importance for the 2021 event suggests that they may serve as a useful predictor/precursor for future Atlantic Niño events.
Liu, Zhengyu, Peng Gu, and Thomas L Delworth, January 2023: Strong red noise ocean forcing on Atlantic multidecadal variability assessed from surface heat flux: Theory and application. Journal of Climate, 36(1), doi:10.1175/JCLI-D-22-0063.153-78. [ Abstract ]
The role of ocean forcing on Atlantic multidecadal variability (AMV) is assessed from the (downward) heat flux–SST relation in the framework of a new stochastic climate theory forced by red noise ocean forcing. Previous studies suggested that atmospheric forcing drives SST variability from monthly to interannual time scales, with a positive heat flux–SST correlation, while heat flux induced by ocean processes can drive SST variability at decadal and longer time scales, with a negative heat flux–SST correlation. Here, first, we develop a theory to show how the sign of heat flux–SST correlation is affected by atmospheric and oceanic forcing with time scale. In particular, a red noise ocean forcing is necessary for the sign reversal of heat flux–SST correlation. Furthermore, this sign reversal can be detected equivalently in three approaches: the low-pass correlation at lag zero, the unfiltered correlation at long (heat flux) lead, and the real part of the heat flux–SST coherence. Second, we develop a new scheme in combination with the theory to assess the magnitude and time scale of the red noise ocean forcing for AMV in the GFDL SPEAR model (Seamless System for Prediction and Earth System Research) and observations. In both the model and observations, the ocean forcing on AMV is in general comparable with the atmospheric forcing, with a 90% probability greater than the atmospheric forcing in observations. In contrast to the white noise atmospheric forcing, the ocean forcing has a persistence time comparable or longer than a year, much longer than the SST persistence of ∼3 months. This slow ocean forcing is associated implicitly with slow subsurface ocean dynamics.
The frequency and intensity of heat extremes over the United States have increased since the mid-20th century and are projected to increase with additional anthropogenic greenhouse gas forcing. We define heat extremes as summertime (June–August) daily maximum 2m temperatures that exceed historical records. We examine characteristics of historical and near-future heat extremes using observations and past and future projections using 100 ensemble members from three coupled global climate models large ensemble simulations. We find that the large ensembles capture the trend and variability of heat extremes over the period 2006–2020 relative to the 1991–2005 climatology but overestimate the frequency at which the heat extremes occur. In future warming scenarios, heat extremes continue to increase over the next 30 years, with high amplitude records in the Northwest and Central US. After 2050, we find there is a spread in the frequency of heat extremes that is dependent on the emissions scenario, with a high emissions until mid-century followed by a high mitigation scenario showing a decrease in heat extremes by the end of the century. Although the frequency of future heat extremes is likely overestimated in the large ensembles, they are still a powerful tool for researching extreme temperatures in the climate system.
Using a state-of-the-art coupled general circulation model, physical processes underlying Antarctic sea ice multidecadal variability and predictability are investigated. Our model simulations constrained by atmospheric reanalysis and observed sea surface temperature broadly capture a multidecadal variability in the observed sea ice extent (SIE) with a low sea ice state (late 1970s–1990s) and a high sea ice state (2000s–early 2010s), although the model overestimates the SIE decrease in the Weddell Sea around the 1980s. The low sea ice state is largely due to the deepening of the mixed layer and the associated deep convection that brings subsurface warm water to the surface. During the high sea ice period (post-2000s), the deep convection substantially weakens, so surface wind variability plays a greater role in the SIE variability. Decadal retrospective forecasts started from the above model simulations demonstrate that the Antarctic sea ice multidecadal variability can be skillfully predicted 6–10 years in advance, showing a moderate correlation with the observation. Ensemble members with a deeper mixed layer and stronger deep convection tend to predict a larger sea ice decrease in the 1980s, whereas members with a larger surface wind variability tend to predict a larger sea ice increase after the 2000s. Therefore, skillful simulation and prediction of the Antarctic sea ice multidecadal variability require accurate simulation and prediction of the mixed layer, deep convection, and surface wind variability in the model.
Schenkel, Benjamin A., Daniel Chavas, Ning Lin, Thomas R Knutson, Gabriel A Vecchi, and Alan Brammer, January 2023: North Atlantic tropical cyclone outer size and structure remain unchanged by the late twenty-first century. Journal of Climate, 36(2), doi:10.1175/JCLI-D-22-0066.1359-382. [ Abstract ]
There is a lack of consensus on whether North Atlantic tropical cyclone (TC) outer size and structure (i.e., change in outer winds with increasing radius from the TC) will differ by the late twenty-first century. Hence, this work seeks to examine whether North Atlantic TC outer wind field size and structure will change by the late twenty-first century using multiple simulations under CMIP3 SRES A1B and CMIP5 RCP4.5 scenarios. Specifically, our analysis examines data from the GFDL High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR) and two versions of the GFDL hurricane model downscaling climate model output. Our results show that projected North Atlantic TC outer size and structure remain unchanged by the late twenty-first century within nearly all HiFLOR and GFDL hurricane model simulations. Moreover, no significant regional outer size differences exist in the North Atlantic within most HiFLOR and GFDL hurricane model simulations. No changes between the control and late-twenty-first-century simulations exist over the storm life cycle in nearly all simulations. For the simulation that shows significant decreases in TC outer size, the changes are attributed to reductions in storm lifetime and outer size growth rates. The absence of differences in outer size among most simulations is consistent with the process that controls the theoretical upper bound of storm size (i.e., Rhines scaling), which is thermodynamically invariant. However, the lack of complete consensus among simulations for many of these conclusions suggests nontrivial uncertainty in our results.
Smith, James A., Mary Lynn Baeck, Yibing Su, Maofeng Liu, and Gabriel A Vecchi, March 2023: Strange storms: Rainfall extremes from the remnants of Hurricane Ida (2021) in the northeastern US. Water Resources Research, 59(3), doi:10.1029/2022WR033934. [ Abstract ]
On 1 September 2021, the remnants of Hurricane Ida transformed into a lethal variant of tropical cyclone in which unprecedented short-duration rainfall from clusters of supercells produced catastrophic flooding in watersheds of the Northeastern US. Short-duration rainfall extremes from Ida are examined through analyses of polarimetric radar fields and rain gauge observations. Rainfall estimates are constructed from a polarimetric rainfall algorithm that is grounded in specific differential phase shift (KDP) fields. Rainfall accumulations at multiple locations exceed 1000-year values for 1–3 hr time scales. Radar observations show that supercells are the principal agents of rainfall extremes. Record flood peaks occurred throughout the eastern Pennsylvania—New Jersey region; the peak discharge of the Elizabeth River is one of the most extreme in the eastern US, based on the ratio of the peak discharge to the sample 10-year flood at the gaging station. As with other tropical cyclones that have produced record flooding in the Northeastern US, Extratropical Transition was a key element of extreme rainfall and flooding from Ida. Tropical and extratropical elements of the storm system contributed to extremes of atmospheric water balance variables and Convective Available Potential Energy, providing the environment for extreme short-duration rainfall from supercells.
Stephens, Graeme L., Jan Polcher, Xubin Zeng, Peter van Oevelen, Germán Poveda, Michael Bosilovich, Myoung-Hwan Ahn, Gianpaolo Balsamo, Qingyun Duan, Gabriele Hegerl, Christian Jakob, Benjamin Lamptey, L Ruby Leung, Maria Piles, Zhongbo Su, Paul A Dirmeyer, Kirsten L Findell, Anne Verhoef, Michael Ek, Tristan L'Ecuyer, Rémy Roca, Ali Nazemi, Francina Dominguez, Daniel Klocke, and Sandrine Bony, January 2023: The first 30 years of GEWEX. Bulletin of the American Meteorological Society, 104(1), doi:10.1175/BAMS-D-22-0061.1E126–E157. [ Abstract ]
The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.
Land–atmosphere (L–A) interactions encompass the co-evolution of the land surface and overlying planetary boundary layer, primarily during daylight hours. However, many studies have been conducted using monthly or entire-day mean time series due to the lack of subdaily data. It is unclear whether the inclusion of nighttime data alters the assessment of L–A coupling or obscures L–A interactive processes. To address this question, we generate monthly (M), entire-day mean (E), and daytime-only mean (D) data based on the ERA5 (5th European Centre for Medium-Range Weather Forecasts reanalysis) product and evaluate the strength of L–A coupling through two-legged metrics, which partition the impact of the land states on surface fluxes (the land leg) from the impact of surface fluxes on the atmospheric states (the atmospheric leg). Here we show that the spatial patterns of strong L–A coupling regions among the M-, D-, and E-based diagnoses can differ by more than 80 %. The signal loss from E- to M-based diagnoses is determined by the memory of local L–A states. The differences between E- and D-based diagnoses can be driven by physical mechanisms or averaging algorithms. To improve understanding of L–A interactions, we call attention to the urgent need for more high-frequency data from both simulations and observations for relevant diagnoses. Regarding model outputs, two approaches are proposed to resolve the storage dilemma for high-frequency data: (1) integration of L–A metrics within Earth system models, and (2) producing alternative daily datasets based on different averaging algorithms.
The Model-Analogs technique is used in the present study to assess the decadal sea surface temperature (SST) prediction skill over the Southern Ocean (SO). The Model-Analogs here is based on reanalysis products and model control simulations that have ∼1° ocean/ice (refined to 0.5° at high latitudes) components and 100 km atmosphere/land components. It is found that the model analog hindcasts show comparable skills with the initialized retrospective decadal hindcasts south of 50°S, with even higher skills over the Weddell Sea at longer lead years. The high SST skills primarily arise from the successful capture of SO deep convection states. This deep ocean memory and the associated decadal predictability are also clearly seen when we assess the Model-Analogs technique in a perfect model context. Within 30°S–50°S latitudinal band, the model analog hindcasts show low skills. When we include the externally forced signals estimated from the large ensemble simulations, the model analog hindcasts and initialized decadal hindcasts show identical skills. The Model-Analogs method therefore provides a great baseline for developing future decadal forecast systems. It is unclear whether such analog techniques would also be successful with models that explicitly resolve ocean mesoscale eddies or other small-scale processes. This area of research needs to be explored further.
Historical precipitation and temperature trends and variations over global land regions are compared with simulations of two climate models focusing on grid points with substantial observational coverage from the early twentieth century. Potential mechanisms for the differences between modeled and observed trends are investigated using subsets of historical forcings, including ones using only anthropogenic greenhouse gases or aerosols, and simulations forced with the observed sea surface temperature and sea ice distribution. For century-scale (1915–2014) precipitation trends, underestimated increasing or unrealistic decreasing trends are found in the models over the extratropical Northern Hemisphere. The temporal evolution of key discrepancies between the observations and simulations indicates that 1) for averages over 15°–45°N, while there is not a significant trend in observations, both models simulate reduced precipitation from 1940 to 2014, and 2) for 45°–80°N observations suggest sizable precipitation increases while models do not show a significant increase, particularly during ∼1950–80. The timing of differences between models and observations suggests a key role for aerosols in these dry trend biases over the extratropical Northern Hemisphere. Additionally, 3) for 15°S–15°N the observed multidecadal decrease over tropical west Africa (1950–80) is only roughly captured by simulations forced with observed sea surface temperature; additionally, 4) in the all-forcing runs, the model with higher global climate sensitivity simulates increasing trends of temperature and precipitation over lands north of 45°N that are significantly stronger than the lower-sensitivity model and more consistent with the observed increases. Thus, underestimated greenhouse gas–induced warming—particularly in the lower sensitivity model—may be another important factor, besides aerosols, contributing to the modeled biases in precipitation trends.
Zhang, Bosong, Brian J Soden, and Gabriel A Vecchi, February 2023: A vertically resolved analysis of radiative feedbacks on moist static energy variance in tropical cyclones. Journal of Climate, 36(4), doi:10.1175/JCLI-D-22-0199.11125-1141. [ Abstract ]
A vertically resolved moist static energy (MSE) variance budget framework is used to diagnose processes associated with the development of tropical cyclones (TCs) in a general circulation model (GCM) under realistic boundary conditions. Previous studies have shown that interactions between radiation and MSE promote TC development. Here, we examine the vertical contributions of radiation and its interactions with MSE by performing several mechanism-denial experiments in which synoptic-scale radiative interactions are suppressed either in the boundary layer or in the free troposphere. Partly suppressing radiative interactions results in a reduction in global TC frequency. However, the magnitude of reduction and structure of the feedback depend on the intensity and structure of the TCs in these mechanism-denial experiments, indicating that both the magnitude and the vertical location of radiative interactions can impact global TC frequency. Using instantaneous 6-hourly outputs, an explicit computation reveals distinct spatial patterns of the advection term: the vertical component is positive in the mid- to upper troposphere, which reflects an upward transport of MSE by deep convection, whereas the horizontal component is positive in the boundary layer. These results illustrate the impact of the vertical distribution of radiative interactions and vertically varied contribution of the advection term in the development of TCs.
Long-term sea-level rise and multiyear to decadal sea level variations pose substantial risks for flooding and erosion in coastal communities. Here we use observations and climate model predictions to show that sea level variations along the U.S. East Coast are skillfully predictable 3 to 10 years in advance. The most predictable component of sea level is a basin scale upward trend, predictable a decade in advance and primarily a response to increasing greenhouse gases. Significant additional predictability comes from multidecadal variations of the Atlantic Meridional Overturning Circulation (AMOC). While perfect model simulations show AMOC-related sea level predictability of 5-7 years, model biases and initialization uncertainties reduce the realized predictive skill to 3-5 years, depending on location. Overall, greenhouse gas warming and predictable AMOC variations lead to multiyear to decadal prediction skill for sea level along the U.S. East Coast. Such skill could have significant societal benefit for planning and adaptation.
Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from CryoSat-2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to SIC-only assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions.
Zhou, Sha, Bofu Yu, Benjamin R Lintner, Kirsten L Findell, and Yao Zhang, May 2023: Projected increase in global runoff dominated by land surface changes. Nature Climate Change, 13, doi:10.1038/s41558-023-01659-8442-449. [ Abstract ]
Increases in atmospheric CO2 concentration affect continental runoff through radiative and physiological forcing. However, how climate and land surface changes, and their interactions in particular, regulate changes in global runoff remains largely unresolved. Here we develop an attribution framework that integrates top-down empirical and bottom-up modelling approaches to show that land surface changes account for 73–81% of projected global runoff increases. This arises from synergistic effects of physiological responses of vegetation to rising CO2 concentration and responses of land surface—for example, vegetation cover and soil moisture—to radiatively driven climate change. Although climate change strongly affects regional runoff changes, it plays a minor role (19–27%) in the global runoff increase, due to cancellation of positive and negative contributions from different regions. Our findings highlight the importance of accurate model representation of land surface processes for reliable projections of global runoff to support sustainable management of water resources.
Barsugli, Joseph J., David R Easterling, Derek S Arndt, David A Coates, Thomas L Delworth, Martin P Hoerling, Nathaniel C Johnson, Sarah B Kapnick, Arun Kumar, Kenneth E Kunkel, Carl J Schreck, Russell S Vose, and Tao Zhang, March 2022: Development of a rapid response capability to evaluate causes of extreme temperature and drought events in the United States. Bulletin of the American Meteorological Society, 103(3), doi:10.1175/BAMS-D-21-0237.1S14-S20.
We use two coupled climate models, GFDL-CM4 and GFDL-ESM4, to investigate the physical response of the Southern Ocean to changes in surface wind stress, Antarctic meltwater, and the combined forcing of the two in a pre-industrial control simulation. The meltwater cools the ocean surface in all regions except the Weddell Sea, where the wind stress warms the near-surface layer. The limited sensitivity of the Weddell Sea surface layer to the meltwater is due to the spatial distribution of the meltwater fluxes, regional bathymetry, and large-scale circulation patterns. The meltwater forcing dominates the Antarctic shelf response and the models yield strikingly different responses along West Antarctica. The disagreement is attributable to the mean-state representation and meltwater-driven acceleration of the Antarctic Slope Current (ASC). In CM4, the meltwater is efficiently trapped on the shelf by a well resolved, strong, and accelerating ASC which isolates the West Antarctic shelf from warm offshore waters, leading to strong subsurface cooling. In ESM4, a weaker and diffuse ASC allows more meltwater to escape to the open ocean, the West Antarctic shelf does not become isolated, and instead strong subsurface warming occurs. The CM4 results suggest a possible negative feedback mechanism that acts to limit future melting, while the ESM4 results suggest a possible positive feedback mechanism that acts to accelerate melt. Our results demonstrate the strong influence the ASC has on governing changes along the shelf, highlighting the importance of coupling interactive ice sheet models to ocean models that can resolve these dynamical processes.
Tropical cyclone rapid intensification events often cause destructive hurricane landfalls because they are associated with the strongest storms and forecasts with the highest errors. Multi-decade observational datasets of tropical cyclone behavior have recently enabled documentation of upward trends in tropical cyclone rapid intensification in several basins. However, a robust anthropogenic signal in global intensification trends and the physical drivers of intensification trends have yet to be identified. To address these knowledge gaps, here we compare the observed trends in intensification and tropical cyclone environmental parameters to simulated natural variability in a high-resolution global climate model. In multiple basins and the global dataset, we detect a significant increase in intensification rates with a positive contribution from anthropogenic forcing. Furthermore, thermodynamic environments around tropical cyclones have become more favorable for intensification, and climate models show anthropogenic warming has significantly increased the probability of these changes.
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.
The Mediterranean is a projected hot spot for climate change, with significant warming and rainfall reductions. We use climate model ensembles to explore whether these Mediterranean rainfall declines could be reversed in response to greenhouse gas reductions. While the summer Mediterranean rainfall decline is reversed, winter rainfall continues to decline. The continued decline results from prolonged weakening of Atlantic Ocean poleward heat transport that combines with greenhouse gas reductions to cool the subpolar North Atlantic, inducing atmospheric circulation changes that favor continued Mediterranean drying. This is a potential “surprise” in the climate system, whereby changes in one component (Atlantic Ocean circulation) alter how another component (Mediterranean rainfall) responds to greenhouse gas reductions. Such surprises could complicate climate change mitigation efforts.
Findell, Kirsten L., Rowan Sutton, and Nico Caltabiano, July 2022: Explaining and predicting Earth system change: A World Climate Research Programme call to action. GEWEX Quarterly, 32(4), 5-7.
We describe the model performance of a new global coupled climate model configuration, CM4-MG2. Beginning with the Geophysical Fluid Dynamics Laboratory's fourth-generation physical climate model (CM4.0), we incorporate a two-moment Morrison-Gettelman bulk stratiform microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature-dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere-ocean-land-sea ice simulations, following Coupled Model Intercomparison Project Phase 6 protocols. CM4-MG2 generally captures CM4.0's baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward “double” Intertropical Convergence Zones south of the equator, and a stronger Madden-Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger-than-observed El Nino-Southern Oscillation. Compared to CM4.0, the climate sensitivity is reduced by about 10% in CM4-MG2.
Hermanson, Leon, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco J Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav Kharin, William J Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicolí, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam A Scaife, Mark A Collier, Terence O'Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang A Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Thomas L Delworth, Xiaosong Yang, and Fanrong Zeng, et al., April 2022: WMO global annual to decadal climate update: A prediction for 2021–25. Bulletin of the American Meteorological Society, 103(4), doi:10.1175/BAMS-D-20-0311.1E1117-E1129. [ Abstract ]
As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.
Hsieh, Tsung-Lin, Wenchang Yang, Gabriel A Vecchi, and Ming Zhao, April 2022: Model spread in the tropical cyclone frequency and seed propensity index across global warming and ENSO-like perturbations. Geophysical Research Letters, 49(7), doi:10.1029/2021GL097157. [ Abstract ]
The future projection of tropical cyclone frequency is highly uncertain. Recent multi-model studies showed that the model spread in tropical cyclones is correlated with the model spread in seeds, which are defined as convective weak vortices. However, it was unclear how the model spread is related to the large-scale circulation. Here we apply a downscaling theory recently developed using aquaplanet experiments to explain the seed frequency across four global atmospheric models having different parameterizations of convection and resolutions. The seed frequency has a larger model spread in response to uniform warming than to CO2 doubling or El Niño/La Niña-like sea surface temperature perturbations. Across all climate perturbations, the seed frequency is captured by the downscaling theory, expressed as a seed propensity index. The index highlights the connection between the tropical cyclone seeds and the climatological mean ascent pattern.
The extension of seasonal to interannual prediction of the physical climate system to include the marine ecosystem has a great potential to inform marine resource management strategies. Along the east coast of Africa, recent findings suggest that skillful Earth system model (ESM)-based chlorophyll predictions may enable anticipation of fisheries fluctuations. The mechanisms underlying skillful chlorophyll predictions, however, were not identified, eroding confidence in potential adaptive management steps. This study demonstrates that skillful chlorophyll predictions up to two years in advance arise from the successful simulation of westward-propagating off-equatorial Rossby waves in the Indian ocean. Upwelling associated with these waves supplies nutrients to the surface layer for the large coastal areas by generating north- and southward propagating waves at the east African coast. Further analysis shows that the off-equatorial Rossby wave is initially excited by wind stress forcing caused by El Niño/Southern Oscillation-Indian Ocean teleconnections.
This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.
The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.
Understanding the behavior of western boundary current systems is crucial for predictions of biogeochemical cycles, fisheries, and basin-scale climate modes over the midlatitude oceans. Studies indicate that anthropogenic climate change induces structural changes in the Kuroshio Extension (KE) system, including a northward migration of its oceanic jet. However, changes in the KE temporal variability remain unclear. Using large ensembles of a global coupled climate model, we show that in response to increasing greenhouse gases, the time scale of KE sea surface height (SSH) shifts from interannual scales toward decadal and longer scales. We attribute this increased low-frequency KE variability to enhanced mid-latitude oceanic Rossby wave activity induced by regional and remote atmospheric forcing, due to a poleward shift of midlatitude surface westerly with climatology and an increase in the tropical precipitation activity, which lead to stronger atmospheric teleconnections from El Niño to the midlatitude Pacific and the KE region. Greenhouse warming leads to both a positive (elongated) KE state that restricts ocean perturbations (e.g., eddy activity) and stronger wind-driven KE fluctuations, which enhances the contributions of decadal KE modulations relative to short-time scale intrinsic oceanic KE variations. Our spectral analyses suggest that anthropogenic forcing may alter the future predictability of the KE system.
The impacts of the El Niño-Southern Oscillation (ENSO) are expected to change under increasing greenhouse gas concentrations, but the large internal variability of ENSO and its teleconnections makes it challenging to detect such changes in a single realization of nature. In this study, we explore both the internal variability and radiatively forced changes of boreal wintertime ENSO teleconnection patterns through the analysis of 30-member initial condition ensembles of the Seamless System for Prediction and EArth System Research (SPEAR), a coupled global climate model developed by the NOAA Geophysical Fluid Dynamics Laboratory. We focus on the projected changes of the large-scale circulation, temperature, and precipitation patterns associated with ENSO for 1951–2100 under moderate and high emissions scenarios (SSP2-4.5 and SSP5-8.5). We determine the time of emergence of these changes from the noise of internal climate variability, by determining the time when the amplitude of the ensemble mean change in the running 30-year ENSO composites first exceeds the 1951-1980 composite anomaly amplitude by at least one ensemble standard deviation. Overall, the high internal variability of ENSO teleconnection patterns primarily limits their expected emergence to tropical and subtropical regions before 2100, where some regions experience robust changes in ENSO-related temperature, precipitation, and 500 hPa geopotential height patterns by the middle of the twenty-first century. The earliest expected emergence generally occurs over tropical South America and Southeast Asia, indicating that an enhanced risk of ENSO-related extreme weather in that region could be detected within the next few decades. For signals that are expected to emerge after 2050, both internal climate variability and scenario uncertainty contribute similarly to a time of emergence uncertainty on the order of a few decades. We further explore the diversity of ENSO teleconnections within the SPEAR large ensemble during the historical period, and demonstrate that historical relationships between tropical sea surface temperatures and ENSO teleconnections are skillful predictors of projected changes in the Northern Hemisphere El Niño 500 hPa geopotential height pattern.
In this paper, U.S. landfalling tropical cyclone (TC) activity is projected for the late twenty-first century using a two-step dynamical downscaling framework. A regional atmospheric model, is run for 27 seasons, to generate tropical storm cases. Each storm case is -resimulated (up to 15 days) using the higher-resolution Geophysical Fluid Dynamics Laboratory hurricane model. Thirteen CMIP3 or CMIP5 climate change scenarios are explored. Robustness of projections is assessed using statistical significance tests and comparing changes across models. The proportion of TCs making U.S. landfall increases for the warming scenarios, due, in part, to an increases in the percentage of TC genesis near the U.S. coast and a change in climatological steering flows favoring more U.S. landfall events. The increases in U.S. landfall proportion leads to an increase in U.S. landfalling category 4–5 hurricane frequency, averaging about + 400% across the models; 10 of 13 models/ensembles project an increase (which is statistically significant in three of 13 models). We have only tentative confidence in this latter increase, which occurs despite a robust decrease in Atlantic basin category 1–5 hurricane frequency, no robust change in Atlantic basin category 4–5 and U.S. landfalling category 1–5 hurricane frequency, and no robust change in U.S. landfalling hurricane intensities. Rainfall rates, averaged within a 100-km radius of the storms, are projected to increase by about 18% for U.S. landfalling TCs. Important caveats to the study include low correlation (skill) for interannual variability of modeled vs. observed U.S. TC landfall frequency and model bias of excessive TC genesis near and east of the U.S. east coast in present-day simulations.
Lee, Sukyoung, Michelle L L'Heureux, Andrew T Wittenberg, Richard Seager, Paul A O'Gorman, and Nathaniel C Johnson, October 2022: On the future zonal contrasts of equatorial Pacific climate: Perspectives from observations, simulations, and theories. npj Climate and Atmospheric Science, 5, 82, doi:10.1038/s41612-022-00301-2. [ Abstract ]
Changes in the zonal gradients of sea surface temperature (SST) across the equatorial Pacific have major consequences for global climate. Therefore, accurate future projections of these tropical Pacific gradients are of paramount importance for climate mitigation and adaptation. Yet there is evidence of a dichotomy between observed historical gradient trends and those simulated by climate models. Observational records appear to show a “La Niña-like” strengthening of the zonal SST gradient over the past century, whereas most climate model simulations project “El Niño-like” changes toward a weaker gradient. Here, studies of these equatorial Pacific climate trends are reviewed, focusing first on data analyses and climate model simulations, then on theories that favor either enhanced or weakened zonal SST gradients, and then on notable consequences of the SST gradient trends. We conclude that the present divergence between the historical model simulations and the observed trends likely either reflects an error in the model’s forced response, or an underestimate of the multi-decadal internal variability by the models. A better understanding of the fundamental mechanisms of both forced response and natural variability is needed to reduce the uncertainty. Finally, we offer recommendations for future research directions and decision-making for climate risk mitigation.
Lopez, Hosmay, Sang-Ki Lee, Dongmin Kim, Andrew T Wittenberg, and Sang-Wook Yeh, April 2022: Projections of faster onset and slower decay of El Niño in the 21st century. Nature Communications, 13, 1915, doi:10.1038/s41467-022-29519-7. [ Abstract ]
Future changes in the seasonal evolution of the El Niño—Southern Oscillation (ENSO) during its onset and decay phases have received little attention by the research community. This work investigates the projected changes in the spatio-temporal evolution of El Niño events in the 21st Century (21 C), using a multi-model ensemble of coupled general circulation models subjected to anthropogenic forcing. Here we show that El Niño is projected to (1) grow at a faster rate, (2) persist longer over the eastern and far eastern Pacific, and (3) have stronger and distinct remote impacts via teleconnections. These changes are attributable to significant changes in the tropical Pacific mean state, dominant ENSO feedback processes, and an increase in stochastic westerly wind burst forcing in the western equatorial Pacific, and may lead to more significant and persistent global impacts of El Niño in the future.
Moon, Il-Ju, Thomas R Knutson, Hye-Ji Kim, Alexander V Babanin, and Jin-Yong Jeong, November 2022: Why do eastern North Pacific hurricanes intensify more and faster than their western-counterpart typhoons with less ocean energy?Bulletin of the American Meteorological Society, 103(11), doi:10.1175/BAMS-D-21-0131.1E2604-E2627. [ Abstract ]
Tropical cyclones operate as heat engines, deriving energy from the thermodynamic disequilibrium between ocean surfaces and atmosphere. Available energy for the cyclones comes primarily from upper-ocean heat content. Here, we show that eastern North Pacific hurricanes reach a given intensity 15% faster on average than western North Pacific typhoons despite having half the available ocean heat content. Eastern North Pacific hurricanes also intensify on average 16% more with a given ocean energy (i.e., air–sea enthalpy flux) than western North Pacific typhoons. As efficient intensifiers, eastern Pacific hurricanes remain small during their intensification period, tend to stay at lower latitudes, and are affected by relatively lower vertical wind shear, a colder troposphere, and a drier boundary layer. Despite a shallower warm upper-ocean layer in the eastern North Pacific, average hurricane-induced sea surface cooling there is only slightly larger than in the western North Pacific due to the opposing influences of stronger density stratification, smaller size, and related wave-interaction effects. In contrast, western North Pacific typhoons encounter a more favorable oceanic environment for development, but several factors cause typhoons to greatly increase their size during intensification, resulting in a slow and inefficient intensification process. These findings on tropical cyclones’ basin-dependent characteristics contribute toward a better understanding of TC intensification.
The frequency of large-scale anomalous precipitation events associated with heavy precipitation has been increasing in Japan. However, it is unclear if the increase is due to anthropogenic warming or internal variability. Also, it is challenging to develop an objective methodology to identify anomalous events because of the large variety of anomalous precipitation cases. In this study, we applied a deep learning technique to objectively detect anomalous precipitation events in Japan for both observations and simulations using high-resolution climate models. The results show that the observed increases in anomalous heavy precipitation events in Western Japan during 1977–2015 were not made only by internal variability but the increases in anthropogenic forcing played an important role. Such events will continue to increase in frequency this century. The increases are attributable to the increasing frequency of tropical cyclones and enhanced frontal rainbands near Japan. These results highlight the mitigation challenge posed by the increasing occurrence of unprecedented precipitation events in the future.
Smith, Doug, Nathan P Gillett, Isla Simpson, Panos Athanasiadis, J Baehr, Ingo Bethke, Tarkan Bilge, Rémy Bonnet, Olivier Boucher, and Kirsten L Findell, et al., September 2022: Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). Frontiers in Climate, 4:955414, doi:10.3389/fclim.2022.955414. [ Abstract ]
Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change.
Thomas, Matthew, and Rong Zhang, June 2022: Two sources of deep decadal variability in the central Labrador Sea open-ocean convection region. Geophysical Research Letters, 49(11), doi:10.1029/2022GL098825. [ Abstract ]
The ventilation of the central Labrador Sea is important for the uptake of ocean tracers and carbon. Using historical ocean observations, we construct a simple multiple linear regression model that successfully reconstructs the decadal variability of the upper ∼2,000 m of the central Labrador Sea water properties based on observed indices that represent two different open-ocean ventilation mechanisms. The first mechanism is the modification of deep ocean properties through local decadal variability of the Labrador Sea deep convective mixing. The second, more novel, mechanism is the climatological convective vertical redistribution of upper central Labrador Sea temperature and salinity anomalies associated with the nonlocal large-scale subpolar Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation. The ventilated decadal central Labrador Sea signal subsequently spreads into the western subpolar North Atlantic. The results have important implications for predicting decadal ventilated signals in the Labrador Sea that are associated with the large-scale climate variability.
Quantifying the response of atmospheric rivers (ARs) to radiative forcing is challenging due to uncertainties caused by internal climate variability, differences in shared socioeconomic pathways (SSPs), and methods used in AR detection algorithms. In addition, the requirement of medium-to-high model resolution and ensemble sizes to explicitly simulate ARs and their statistics can be computationally expensive. In this study, we leverage the unique 50-km large ensembles generated by a Geophysical Fluid Dynamics Laboratory next-generation global climate model, Seamless system for Prediction and EArth system Research, to explore the warming response in ARs. Under both moderate and high emissions scenarios, increases in AR-day frequency emerge from the noise of internal variability by 2060. This signal is robust across different SSPs and time-independent detection criteria. We further examine an alternative approach proposed by Thompson et al. (2015), showing that unforced AR variability can be approximated by a first-order autoregressive process. The confidence intervals of the projected response can be analytically derived with a single ensemble member.
Verhoef, Anne, and Kirsten L Findell, July 2022: Report on the GEWEX 2022 GLASS Panel Meeting. GEWEX Quarterly, 32(4), 14-16.
Wei, Xinyue, and Rong Zhang, July 2022: A simple conceptual model for the self-sustained multidecadal AMOC variability. Geophysical Research Letters, 49(14), doi:10.1029/2022GL099800. [ Abstract ]
Multidecadal variability of Atlantic Meridional Overturning Circulation (AMOC) has been reconstructed by various proxies, simulated in climate models, and linked to multidecadal Arctic salinity variability. Here we construct a simple conceptual model to understand the two-way interactions of the Arctic with multidecadal AMOC variability through a delayed oscillator mechanism. We revise Stommel's Two-Box Model by including an advective time delay for the Arctic density/salinity anomalies to reach the subpolar North Atlantic and a coupled negative feedback between the AMOC and the freshwater flux entering the Arctic through atmosphere and/or sea ice responses. Self-sustained multidecadal AMOC oscillations exist in the revised Stommel's Two-Box model if the oceanic advective time delay is longer than the oscillation threshold, and the periods of the AMOC delayed oscillator depend crucially on this advective time delay. The coupled freshwater feedback provides additional delayed negative feedback and reduces the advective time delay threshold required for the oscillations.
The continuing decline of the summertime sea ice cover has reduced the sea ice path that must be traversed to Arctic destinations and through the Arctic between the Atlantic and Pacific Oceans, stimulating interest in trans–Arctic Ocean routes. Seasonal prediction of the sea ice cover along these routes could support the increasing summertime ship traffic taking advantage of recent low ice conditions. We introduce the minimum Arctic sea ice path (MIP) between Atlantic and Pacific Oceans as a shipping-relevant metric that is amenable to multidecadal hindcast evaluation. We show, using 1992–2017 retrospective predictions, that bias correction is necessary for the GFDL Seamless System for Prediction and Earth System Research (SPEAR) forecast system to improve upon damped persistence seasonal forecasts of summertime daily MIP between the Atlantic and Pacific Oceans both east and west of Greenland, corresponding roughly to the Northeast and Northwest Passages. Without bias correction, only the Northwest Passage MIP forecasts have lower error than a damped persistence forecast. Using the forecast ensemble spread to estimate a lower bound on forecast error, we find large opportunities for forecast error reduction, especially at lead times of less than 2 months. Most of the potential improvement remains after linear removal of climatological and trend biases, suggesting that significant error reduction might come from improved initialization and simulation of subannual variability. Using a different passive microwave sea ice dataset for calculating error than was used for data assimilation increases the raw forecast errors but not the trend anomaly forecast errors.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
Landfalling tropical cyclones (LTCs) are the most devastating disaster to affect the U.S., while the demonstration of skillful subseasonal (between 10 days and one season) prediction of LTCs is less promising. Understanding the mechanisms governing the subseasonal variation of TC activity is fundamental to improving its forecast, which is of critical interest to decision-makers and the insurance industry. This work reveals three localized atmospheric circulation modes with significant 10–30 days subseasonal variations: Piedmont Oscillation (PO), Great America Dipole (GAD), and the Subtropical High ridge (SHR) modes. These modes strongly modulate precipitation, TC genesis, intensity, track, and landfall near the U.S. coast. Compared to their strong negative phases, the U.S. East Coast has 19 times more LTCs during the strong positive phases of PO, and the Gulf Coast experiences 4–12 times more frequent LTCs during the positive phases of GAD and SHR. Results from the GFDL SPEAR model show a skillful prediction of 13, 9, and 22 days for these three modes, respectively. Our findings are expected to benefit the prediction of LTCs on weather timescale and also suggest opportunities exist for subseasonal predictions of LTCs and their associated heavy rainfalls.
Yang, Qidong, Chia-Ying Lee, Michael K Tippett, Daniel Chavas, and Thomas R Knutson, April 2022: Machine learning based hurricane wind reconstruction. Weather and Forecasting, 37(4), doi:10.1175/WAF-D-21-0077.1477-493. [ Abstract ]
Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.
The rapid day-to-day temperature swings associated with extratropical storm tracks can cause cascading infrastructure failure and impact human outdoor activities, thus research on seasonal prediction and predictability of extreme temperature swings is of huge societal importance. To measure the extreme surface air temperature (SAT) variations associated with the winter extratropical storm tracks, a Temperature Swing Index (TSI) is formulated as the standard deviation of 24-h-difference-filtered data of the 6-hourly SAT. The dominant term governing the TSI variability is shown to be proportional to the product of eddy heat flux and mean temperature gradient. The seasonal prediction skill of the winter TSI over North America was assessed using Geophysical Fluid Dynamics Laboratory's new seasonal prediction system. The locations with skillful TSI prediction show a geographic pattern that is distinct from the pattern of skillful seasonal mean SAT prediction. The prediction of TSI provides additional predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of the El Niño-Southern Oscillation (ENSO), North Pacific Oscillation (NPO), and Pacific/North American (PNA) teleconnection. Over the central United States, the correlation skill of TSI prediction reaches 0.75 with strong links to observed ENSO, NPO, and PNA, while the skill of seasonal SAT prediction is relatively low with a correlation of 0.36. As a first attempt of diagnosing the combined predictability of the first-order (the seasonal mean) and second-order (TSI) statistics for SAT, this study highlights the importance of the eddy-mean flow interaction perspective for understanding the seasonal climate predictability in the extra tropics. These results point toward providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers.
The local climatic impacts of historical expansion of irrigation are substantial, but the distant impacts are poorly understood, and their governing mechanisms generally have not been rigorously analyzed. Our experiments with an earth-system model suggest that irrigation in the Middle East and South Asia may enhance rainfall in a large portion of the Sahel-Sudan Savanna (SSS) to an extent comparable and opposite to its suppression by other anthropogenic climate drivers during the last several decades. The enhancement arises through a reduction in the meridional gradient of moist static energy from the Sahara Desert to the tropical rainforests. An implication of this study is that remote irrigation is a possible factor affecting the risk of drought and famine and, thus, future water security in the SSS region.
One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.
The current GFDL seasonal prediction system, the Seamless System for Prediction and Earth System Research (SPEAR), has shown skillful prediction of Arctic sea ice extent with atmosphere and ocean constrained by observations. In this study we present improvements in subseasonal and seasonal predictions of Arctic sea ice by directly assimilating sea ice observations. The sea ice initial conditions from a data assimilation (DA) system that assimilates satellite sea ice concentration (SIC) observations are used to produce a set of reforecast experiments (IceDA) starting from the first day of each month from 1992 to 2017. Our evaluation of daily sea ice extent prediction skill concludes that the SPEAR system generally outperforms the anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on daily gridcell-level sea ice fields. SIC DA improves prediction skill of SIC forecasts prominently in the June-, July-, August-, and September-initialized reforecasts. We evaluate two additional user-oriented metrics: the ice-free probability (IFP) and ice-free date (IFD). IFP is the probability of a grid cell experiencing ice-free conditions in a given year, and IFD is the first date on which a grid cell is ice free. A combined analysis of IFP and IFD demonstrates that the SPEAR model can make skillful predictions of local ice melt as early as May, with modest improvements from SIC DA.
The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent.
Zhou, Sha, A Park Williams, Benjamin R Lintner, Kirsten L Findell, Trevor F Keenan, Yao Zhang, and Pierre Gentine, September 2022: Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture–atmosphere feedbacks. Nature Communications, 13, 5756, doi:10.1038/s41467-022-33473-9. [ Abstract ]
Global warming is expected to cause wet seasons to get wetter and dry seasons to get drier, which would have broad social and ecological implications. However, the extent to which this seasonal paradigm holds over land remains unclear. Here we examine seasonal changes in surface water availability (precipitation minus evaporation, P–E) from CMIP5 and CMIP6 projections. While the P–E seasonal cycle does broadly intensify over much of the land surface, ~20% of land area experiences a diminished seasonal cycle, mostly over subtropical regions and the Amazon. Using land–atmosphere coupling experiments, we demonstrate that 63% of the seasonality reduction is driven by seasonally varying soil moisture (SM) feedbacks on P–E. Declining SM reduces evapotranspiration and modulates circulation to enhance moisture convergence and increase P–E in the dry season but not in the wet season. Our results underscore the importance of SM–atmosphere feedbacks for seasonal water availability changes in a warmer climate.
Zhu, Feng, Julien Emile-Geay, Kevin J Anchukaitis, Gregory J Hakim, Andrew T Wittenberg, Mariano S Morales, Matthew Toohey, and Jonathan King, February 2022: A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation. Nature Communications, 13, 747, doi:10.1038/s41467-022-28210-1. [ Abstract ]
The potential for explosive volcanism to affect the El Niño-Southern Oscillation (ENSO) has been debated since the 1980s. Several observational studies, based largely on tree-ring proxies, have since found support for a positive ENSO phase in the year following large eruptions. In contrast, recent coral data from the heart of the tropical Pacific suggest no uniform ENSO response to explosive volcanism over the last millennium. Here we leverage paleoclimate data assimilation to integrate both tree-ring and coral proxies into a reconstruction of ENSO state, and re-appraise this relationship. We find only a weak statistical association between volcanism and ENSO, and identify the selection of volcanic events as a key variable to the conclusion. We discuss the difficulties of conclusively establishing a volcanic influence on ENSO by empirical means, given the myriad factors affecting the response, including the spatiotemporal details of the forcing and ENSO phase preconditioning.
Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.
Callaghan, Max, Carl-Friedrich Schleussner, Shruti Nath, Quentin Lejeune, and Thomas R Knutson, et al., October 2021: Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature Climate Change, doi:10.1038/s41558-021-01168-6. [ Abstract ]
Increasing evidence suggests that climate change impacts are already observed around the world. Global environmental assessments face challenges to appraise the growing literature. Here we use the language model BERT to identify and classify studies on observed climate impacts, producing a comprehensive machine-learning-assisted evidence map. We estimate that 102,160 (64,958–164,274) publications document a broad range of observed impacts. By combining our spatially resolved database with grid-cell-level human-attributable changes in temperature and precipitation, we infer that attributable anthropogenic impacts may be occurring across 80% of the world’s land area, where 85% of the population reside. Our results reveal a substantial ‘attribution gap’ as robust levels of evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries. While gaps remain on confidently attributabing climate impacts at the regional and sectoral level, this database illustrates the potential current impact of anthropogenic climate change across the globe.
Chen, Han-Ching, Fei-Fei Jin, Sen Zhao, Andrew T Wittenberg, and Shaocheng Xie, December 2021: ENSO dynamics in the E3SM-1-0, CESM2, and GFDL-CM4 climate models. Journal of Climate, 34(23), doi:10.1175/JCLI-D-21-0355.19365-9384. [ Abstract ]
This study examines historical simulations of ENSO in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, provided by three leading U.S. modeling centers as part of the Coupled Model Intercomparison Project phase 6 (CMIP6). These new models have made substantial progress in simulating ENSO’s key features, including amplitude, time scale, spatial patterns, phase-locking, the spring persistence barrier, and recharge oscillator dynamics. However, some important features of ENSO are still a challenge to simulate. In the central and eastern equatorial Pacific, the models’ weaker-than-observed subsurface zonal current anomalies and zonal temperature gradient anomalies serve to weaken the nonlinear zonal advection of subsurface temperatures, leading to insufficient warm/cold asymmetry of ENSO’s sea surface temperature anomalies (SSTA). In the western equatorial Pacific, the models’ excessive simulated zonal SST gradients amplify their zonal temperature advection, causing their SSTA to extend farther west than observed. The models underestimate both ENSO’s positive dynamic feedbacks (due to insufficient zonal wind stress responses to SSTA) and its thermodynamic damping (due to insufficient convective cloud shading of eastern Pacific SSTA during warm events); compensation between these biases leads to realistic linear growth rates for ENSO, but for somewhat unrealistic reasons. The models also exhibit stronger-than-observed feedbacks onto eastern equatorial Pacific SSTAs from thermocline depth anomalies, which accelerates the transitions between events and shortens the simulated ENSO period relative to observations. Implications for diagnosing and simulating ENSO in climate models are discussed.
Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.
Ek, Michael, Kirsten L Findell, and Anne Verhoef, May 2021: 2020 GLASS Panel Meeting. GEWEX Quarterly, 31(2), 14-18. [ Abstract ]
Jakob, Christian, Peter Bauer, Sandrine Bony, Daniel Klocke, Kirsten L Findell, Anne Verhoef, Francina Dominguez, Ali Nazemi, and Jan Polcher, July 2021: The WCRP Digital Earths Lighthouse Activity–An opportunity for the GEWEX community. GEWEX Quarterly, 31(4), 7-9.
Jing, Renzhi, Ning Lin, Kerry A Emanuel, Gabriel A Vecchi, and Thomas R Knutson, December 2021: A comparison of tropical cyclone projections in a high-resolution global climate model and from downscaling by statistical and statistical-deterministic methods. Journal of Climate, 34(23), doi:10.1175/JCLI-D-21-0071.1. [ Abstract ]
In this study, we investigate the response of tropical cyclones (TCs) to climate change by using the Princeton environment-dependent probabilistic tropical cyclone (PepC) model and a statistical-deterministic method to downscale TCs using environmental conditions obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model, under the representative concentration pathway 4.5 (RCP4.5) emissions scenario for the North Atlantic Ocean basin. The downscaled TCs for the historical climate (1986–2005) are compared with those in the middle (2016–35) and late twenty-first century (2081–2100). The downscaled TCs are also compared with TCs explicitly simulated in HiFLOR. We show that, while significantly more storms are detected in HiFLOR toward the end of the twenty-first century, the statistical-deterministic model projects a moderate increase in TC frequency and PepC projects almost no increase in TC frequency. The changes in storm frequency in all three datasets are not significant in the mid-twenty-first century. All three project that storms will become more intense and the fraction of major hurricanes and category-5 storms will significantly increase in the future climates. However, HiFLOR projects the largest increase in intensity, and PepC projects the least. The results indicate that HiFLOR’s TC projection is more sensitive to climate change effects and that statistical models are less sensitive. Nevertheless, in all three datasets, storm intensification and frequency increase lead to relatively small changes in TC threat as measured by the return level of landfall intensity under the projected climate condition.
Kessler, William S., Sophie Cravatte, Peter G Strutton, Adrienne J Sutton, Arun Kumar, Yuhei Takaya, Harry Hendon, Kevin O'Brien, Neville Smith, Susan E Wijffels, Janet Sprintall, and Andrew T Wittenberg, et al., August 2021: Final Report of TPOS 2020 , GOOS-268, 83pp. [ Abstract ]
Available online at https://tropicalpacific.org/tpos2020-project-archive/reports/
Knutson, Thomas R., Maya V Chung, Gabriel A Vecchi, Jingru Sun, Tsung-Lin Hsieh, and Adam J Smith, March 2021: ScienceBrief Review: Climate change is probably increasing the intensity of tropical cyclones [Le Quéré, Corrine, Peter Liss, and Piers Forster (ed.)] In Critical Issues in Climate Change Science, doi:10.5281/zenodo.4570334. [ Abstract ]
Warming of the surface ocean from anthropogenic (human-induced) climate change is likely fuelling more powerful TCs. The destructive power of individual TCs through flooding is amplified by rising sea level, which very likely has a substantial contribution at the global scale from anthropogenic climate change. In addition, TC precipitation rates are projected to increase due to enhanced atmospheric moisture associated with anthropogenic global warming. The proportion of severe TCs (category 3 & 5) has increased, possibly due to anthropogenic climate change. This proportion of very intense TCs (category 4 & 5) is projected to increase, yet most climate model studies project the total number of TCs each year to decrease or remain approximately the same. Additional changes such as increasing rates of rapid intensification, the poleward migration of the latitude of maximum intensity, and a slowing of the forward motion of TCs have been observed in places, and these may be climate change signals emerging from natural variability. While there are challenges in attributing these past observed changes to anthropogenic forcing, models project that with global warming in coming decades some regions will experience increases in rapid intensification, a poleward migration of the latitude of maximum intensity or a slowing of the forward motion of TCs.
Knutson, Thomas R., and Jeff J Ploshay, January 2021: Sea level pressure trends: Model-based assessment of detection, attribution, and consistency with CMIP5 historical simulations. Journal of Climate, 34(1), doi:10.1175/JCLI-D-19-0997.1327-346. [ Abstract ]
Observed sea level pressure (SLP) trends for 1901–10, 1951–10, and 1981–2010 are assessed using two observed data sources (HadSLP2_lowvar and 20CRv3) compared to a CMIP5 multimodel ensemble. The CMIP5 simulations include runs with (i) no external forcing (Control runs), (ii) natural external forcing only (Natural-Forcing), or (iii) natural plus anthropogenic forcings combined (All-Forcings). We assess whether the CMIP5 All-Forcing ensemble is consistent with observations and whether there is model-based evidence for detectable anthropogenic influence for the observed SLP trends. For the 1901–2010 and 1951–2010 trends, a robustly detectable anthropogenic signal in both observational data products is a zonal band of SLP increase extending over much of the Southern Hemisphere extratropics (30°–50°S). In contrast, the HadSLP2_lowvar and 20CRv3 observed data products disagree on the sign of the century-scale trends in SLP over much of the low-latitude region 25°N–25°S. These differences will limit confident detection/attribution/consistency conclusions for lower-latitude regions, at least until the observational data product discrepancies are better reconciled. The Northern Hemisphere extratropics remains a difficult region for identifying any detectable anthropogenic influence for annual- or seasonal-mean SLP trends. Overall, our results highlight the difficulty in detecting and attributing anthropogenic signals in SLP for relatively short time scales. The observed 1981–2010 regional trends typically have a different pattern and magnitude from the simulated externally forced trends. Consequently, our results suggest that internal variability is likely the dominant driver of most observed 1981–2010 regional trend features, including the pronounced increase in SLP over the central and eastern equatorial Pacific.
Statistical downscaling (SD) methods used to refine future climate change projections produced by physical models have been applied to a variety of variables. We evaluate four empirical distributional type SD methods as applied to daily precipitation, which because of its binary nature (wet vs. dry days) and tendency for a long right tail presents a special challenge. Using data over the Continental U.S. we use a ‘Perfect Model’ approach in which data from a large‐scale dynamical model is used as a proxy for both observations and model output. This experimental design allows for an assessment of expected performance of SD methods in a future high‐emissions climate‐change scenario. We find performance is tied much more to configuration options rather than choice of SD method. In particular, proper handling of dry days (i.e., those with zero precipitation) is crucial to success. Although SD skill in reproducing day‐to‐day variability is modest (~15–25%), about half that found for temperature in our earlier work, skill is much greater with regards to reproducing the statistical distribution of precipitation (~50–60%). This disparity is the result of the stochastic nature of precipitation as pointed out by other authors. Distributional skill in the tails is lower overall (~30–35%), although in some regions and seasons it is small to non‐existent. Even when SD skill in the tails is reasonably good, in some instances, particularly in the southeastern United States during summer, absolute daily errors at some gridpoints can be large (~20 mm or more), highlighting the challenges in projecting future extremes.
Enhanced riverine delivery of terrestrial nitrogen (N) has polluted many freshwater and coastal ecosystems, degrading drinking water and marine resources. An emerging view suggests a contribution of land N memory effects—impacts of antecedent dry conditions on land N accumulation that disproportionately increase subsequent river N loads. To date, however, such effects have only been explored for several relatively small rivers covering a few episodes. Here we introduce an index for quantifying land N memory effects and assess their prevalence using regional observations and global terrestrial-freshwater ecosystem model outputs. Model analyses imply that land N memory effects are globally prevalent but vary widely in strength. Strong effects reflect large soil dissolved inorganic N (DIN) surpluses by the end of dry years. During the subsequent wetter years, the surpluses are augmented by soil net mineralization pulses, which outpace plant uptake and soil denitrification, resulting in disproportionately increased soil leaching and eventual river loads. These mechanisms are most prominent in areas with high hydroclimate variability, warm climates, and ecosystem disturbances. In 48 of the 118 basins analyzed, strong memory effects produce 43% (21%–88%) higher DIN loads following drought years than following average years. Such a marked influence supports close consideration of prevalent land N memory effects in water-pollution management efforts.
Lee, Sang-Ki, Hosmay Lopez, Dongmin Kim, Andrew T Wittenberg, and Arun Kumar, April 2021: A Seasonal Probabilistic Outlook for Tornadoes (SPOTter) in the contiguous United States based on the leading patterns of large-scale atmospheric anomalies. Monthly Weather Review, 149(4), doi:10.1175/MWR-D-20-0223.1901-919. [ Abstract ]
This study presents an experimental model for Seasonal Probabilistic Outlook for Tornadoes (SPOTter) in the contiguous United States for March, April, and May and evaluates its forecast skill. This forecast model uses the leading empirical orthogonal function modes of regional variability in tornadic environmental parameters (i.e., low-level vertical wind shear and convective available potential energy), derived from the NCEP Coupled Forecast System, version 2, as the primary predictors. A multiple linear regression is applied to the predicted modes of tornadic environmental parameters to estimate U.S. tornado activity, which is presented as the probability for above-, near-, and below-normal categories. The initial forecast is carried out in late February for March–April U.S. tornado activity and then is updated in late March for April–May activity. A series of reforecast skill tests, including the jackknife cross-validation test, shows that the probabilistic reforecast is overall skillful for predicting the above- and below-normal area-averaged activity in the contiguous United States for the target months of both March–April and April–May. The forecast model also successfully reforecasts the 2011 super-tornado-outbreak season and the other three most active U.S. tornado seasons in 1982, 1991, and 2008, and thus it may be suitable for an operational use for predicting future active and inactive U.S. tornado seasons. However, additional tests show that the regional reforecast is skillful for March–April activity only in the Ohio Valley and South and for April–May activity only in the Southeast and Upper Midwest. These and other limitations of the current model, along with the future advances needed to improve the U.S. regional-scale tornado forecast, are discussed.
Lee, Jiwoo, Yann Y Planton, Peter J Gleckler, Kenneth R Sperber, Eric Guilyardi, Andrew T Wittenberg, Michael J McPhaden, and Giuliana Pallotta, October 2021: Robust evaluation of ENSO in climate models: How many ensemble members are needed?Geophysical Research Letters, 48(20), doi:10.1029/2021GL095041. [ Abstract ]
Large ensembles of model simulations require considerable resources, and thus defining an appropriate ensemble size for a particular application is an important experimental design criterion. We estimate the ensemble size (N) needed to assess a model’s ability to capture observed El Niño-Southern Oscillation (ENSO) behavior by utilizing the recently developed International CLIVAR ENSO Metrics Package. Using the larger ensembles available from CMIP6 and the US CLIVAR Large Ensemble Working Group, we find that larger ensembles are needed to robustly capture baseline ENSO characteristics (N > 50) and physical processes (N > 50) than the background climatology (N ≥ 12) and remote ENSO teleconnections (N ≥ 6). While these results vary somewhat across metrics and models, our study quantifies how larger ensembles are required to robustly evaluate simulated ENSO behavior, thereby providing some guidance for the design of model ensembles.
Over the past century, human activities have resulted in substantial global changes that threaten the stability and functionality of coastal habitats. One of these impacts was through nutrient pollution of river runoffs, which have triggered harmful algal blooms and caused low-oxygen conditions in many coastal regions. However, it is challenging for models to simulate coastal impacts of increasing river nutrient loads, especially on a global scale and over a long period of time. Here we take advantage of some recent modeling advances to provide a global perspective on coastal ecosystem responses to increasing river nitrogen loads over the half-century between 1961 and 2010. Overall, we show that the global coastal ocean accumulated more nitrogen over time as river nitrogen loads increased. This caused the primary production of the global coastal system (i.e., the conversion of inorganic to organic materials through photosynthesis) to increase as well. However, we found that the sensitivity of each coastal ecosystem to comparable changes in nitrogen loads varied considerably. This variability was to a large extent related to two factors: the rate of exchange between coastal waters and the adjacent ocean waters, and whether nutrients are limited for phytoplankton to conduct photosynthesis in that system.
Lockwood, Joseph W., Carolina O Dufour, Stephen M Griffies, and Michael Winton, April 2021: On the role of the Antarctic Slope Front on the occurrence of the Weddell Sea polynya under climate change. Journal of Climate, 34(7), doi:10.1175/JCLI-D-20-0069.12529-2548. [ Abstract ]
This study investigates the occurrence of the Weddell Sea polynya under an idealized climate change scenario by evaluating simulations from climate models of different ocean resolutions. The GFDL-CM2.6 climate model, with roughly 3.8-km horizontal ocean grid spacing in the high latitudes, forms a Weddell Sea polynya at similar time and duration under idealized climate change forcing as under preindustrial forcing. In contrast, all convective models forming phase 5 of the Coupled Model Intercomparison Project (CMIP5) show either a cessation or a slowdown of Weddell Sea polynya events under climate warming. The representation of the Antarctic Slope Current and related Antarctic Slope Front is found to be key in explaining the differences between the two categories of models, with these features being more realistic in CM2.6 than in CMIP5. In CM2.6, the freshwater input driven by sea ice melt and enhanced runoff found under climate warming largely remains on the shelf region since the slope front restricts the lateral spread of the freshwater. In contrast, for most CMIP5 models, open-ocean stratification is enhanced by freshening since the absence of a slope front allows coastal freshwater anomalies to spread into the open ocean. This enhanced freshening contributes to the slowdown the occurrence of Weddell Sea polynyas. Hence, an improved representation of Weddell Sea shelf processes in current climate models is desirable to increase our ability to predict the fate of the Weddell Sea polynyas under climate change.
The recent multi-year 2015–2019 drought after a multi-decadal drying trend over Central America raises the question of whether anthropogenic climate change (ACC) played a role in exacerbating these events. While the occurrence of the 2015–2019 drought in Central America has been asserted to be associated with ACC, we lack an assessment of natural vs anthropogenic contributions. Here, we use five different large ensembles—including high-resolution ensembles (i.e., 0.5∘ horizontally)—to estimate the contribution of ACC to the probability of occurrence of the 2015–2019 event and the recent multi-decadal trend. The comparison of ensembles forced with natural and natural plus anthropogenic forcing suggests that the recent 40-year trend is likely associated with internal climate variability. However, the 2015–2019 rainfall deficit has been made more likely by ACC. The synthesis of the results from model ensembles supports the notion of a significant increase, by a factor of four, over the last century for the 2015–2019 meteorological drought to occur because of ACC. All the model results further suggest that, under intermediate and high emission scenarios, the likelihood of similar drought events will continue to increase substantially over the next decades.
Planton, Yann Y., Eric Guilyardi, and Andrew T Wittenberg, et al., February 2021: Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bulletin of the American Meteorological Society, 102(2), doi:10.1175/BAMS-D-19-0337.1E193-E217. [ Abstract ]
El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.
Power, Scott B., Matthieu Lengaigne, Antonietta Capotondi, Myriam Khodri, Jérôme Vialard, Beyrem Jebri, Eric Guilyardi, Shayne McGregor, Jong-Seong Kug, Matthew Newman, Michael J McPhaden, Gerald A Meehl, Doug Smith, Juila Cole, Julien Emile-Geay, Daniel Vimont, and Andrew T Wittenberg, et al., October 2021: Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects. Science, 374(6563), doi:10.1126/science.aay9165. [ Abstract ]
Climate variability in the tropical Pacific affects global climate on a wide range of time scales. On interannual time scales, the tropical Pacific is home to the El Niño–Southern Oscillation (ENSO). Decadal variations and changes in the tropical Pacific, referred to here collectively as tropical Pacific decadal variability (TPDV), also profoundly affect the climate system. Here, we use TPDV to refer to any form of decadal climate variability or change that occurs in the atmosphere, the ocean, and over land within the tropical Pacific. “Decadal,” which we use in a broad sense to encompass multiyear through multidecadal time scales, includes variability about the mean state on decadal time scales, externally forced mean-state changes that unfold on decadal time scales, and decadal variations in the behavior of higher-frequency modes like ENSO.
Seneviratne, Sonia I., Xuebin Zhang, Muhammad Adnan, Wafae Badi, Claudine Dereczynski, Alejandro Di Luca, Subimal Ghosh, Iskhaq Iskandar, James Kossin, Sophie Lewis, Friederike Otto, Izidine Pinto, Masaki Satoh, Sergio M Vicente-Serrano, Michael F Wehner, Botao Zhou, and Thomas R Knutson, et al., in press: Weather and Climate Extreme Events in a Changing Climate. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, , Cambridge University Press. 8/21.
Sonnewald, Maike, Redouane Lguensat, Aparna Radhakrishnan, Zoubero Sayibou, V Balaji, and Andrew T Wittenberg, 2021: Revealing the impact of global warming on climate modes using transparent machine learning and a suite of climate models In ICML 2021 Workshop on Tackling Climate Change with Machine Learning, . [ Abstract ]
https://www.climatechange.ai/papers/icml2021/13
Stevenson, Samantha, Andrew T Wittenberg, John Fasullo, Sloan Coats, and Bette Otto-Bliesner, January 2021: Understanding diverse model projections of future extreme El Niño. Journal of Climate, 34(2), doi:10.1175/JCLI-D-19-0969.1. [ Abstract ]
The majority of future projections in the Coupled Model Intercomparison Project (CMIP5) show more frequent exceedances of the 5 mm day−1 rainfall threshold in the eastern equatorial Pacific rainfall during El Niño, previously described in the literature as an increase in “extreme El Niño events”; however, these exceedance frequencies vary widely across models, and in some projections actually decrease. Here we combine single-model large ensemble simulations with phase 5 of the Coupled Model Intercomparison Project (CMIP5) to diagnose the mechanisms for these differences. The sensitivity of precipitation to local SST anomalies increases consistently across CMIP-class models, tending to amplify extreme El Niño occurrence; however, changes to the magnitude of ENSO-related SST variability can drastically influence the results, indicating that understanding changes to SST variability remains imperative. Future El Niño rainfall intensifies most in models with 1) larger historical cold SST biases in the central equatorial Pacific, which inhibit future increases in local convective cloud shading, enabling more local warming; and 2) smaller historical warm SST biases in the far eastern equatorial Pacific, which enhance future reductions in stratus cloud, enabling more local warming. These competing mechanisms complicate efforts to determine whether CMIP5 models under- or overestimate the future impacts of climate change on El Niño rainfall and its global impacts. However, the relation between future projections and historical biases suggests the possibility of using observable metrics as “emergent constraints” on future extreme El Niño, and a proof of concept using SSTA variance, precipitation sensitivity to SST, and regional SST trends is presented.
Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30% of the annual precipitation is determined by ARs that occur in less than 15% of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal (<5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales (>3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal-to-decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction.
Atlantic hurricanes are a major hazard to life and property, and a topic of intense scientific interest. Historical changes in observing practices limit the utility of century-scale records of Atlantic major hurricane frequency. To evaluate past changes in frequency, we have here developed a homogenization method for Atlantic hurricane and major hurricane frequency over 1851–2019. We find that recorded century-scale increases in Atlantic hurricane and major hurricane frequency, and associated decrease in USA hurricanes strike fraction, are consistent with changes in observing practices and not likely a true climate trend. After homogenization, increases in basin-wide hurricane and major hurricane activity since the 1970s are not part of a century-scale increase, but a recovery from a deep minimum in the 1960s–1980s. We suggest internal (e.g., Atlantic multidecadal) climate variability and aerosol-induced mid-to-late-20th century major hurricane frequency reductions have probably masked century-scale greenhouse-gas warming contributions to North Atlantic major hurricane frequency.
Wang, Chenggong, Brian J Soden, Wenchang Yang, and Gabriel A Vecchi, February 2021: Compensation between cloud feedback and aerosol-cloud interaction in CMIP6 models. Geophysical Research Letters, 48(4), doi:10.1029/2020GL091024. [ Abstract ]
The most recent generation of climate models (the 6th Phase of the Coupled Model Intercomparison Project) yields estimates of effective climate sensitivity (ECS) that are much higher than past generations due to a stronger amplification from cloud feedback. If plausible, these models require substantially larger greenhouse gas reductions to meet global warming targets. We show that models with a more positive cloud feedback also have a stronger cooling effect from aerosol-cloud interactions. These two effects offset each other during the historical period when both aerosols and greenhouse gases increase, allowing either more positive or neutral cloud feedback models to reproduce the observed global-mean temperature change. Since anthropogenic aerosols primarily concentrate in the Northern Hemisphere, strong aerosol-cloud interaction models produce an interhemispheric asymmetric warming. We show that the observed warming asymmetry during the mid to late 20th century is more consistent with low ECS (weak aerosol indirect effect) models.
An extended period of high temperatures across the state of Alaska in June and July, 2019 set multiple temperature records. Here, we examine the extent to which human-driven climate change played a role in increasing the likelihood of experiencing such an extreme event. Using global climate models, we determine that human-driven climate change increased the probability of experiencing such high temperatures in 2019, and that the likelihood of similar or more extreme events will increase into the coming century.
Wootten, Adrienne M., Keith W Dixon, Dennis Adams-Smith, and Renee A McPherson, February 2021: Statistically Downscaled Precipitation Sensitivity to Gridded Observation Data and Downscaling Technique. International Journal of Climatology, 41(2), doi:10.1002/joc.6716980-1001. [ Abstract ]
Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences. Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions.
Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi-decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR-A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR-A1 significantly outperforms CM2.1 in predicting multi-decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.
The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.
Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.
Zhang, Rong, and Matthew Thomas, June 2021: Horizontal circulation across density surfaces contributes substantially to the long-term mean northern Atlantic Meridional Overturning Circulation. Communications Earth and Environment, 2, 112, doi:10.1038/s43247-021-00182-y. [ Abstract ]
The Greenland Sea is often viewed as the northern terminus of the Atlantic Meridional Overturning Circulation. It has also been proposed that the shutdown of open-ocean deep convection in the Labrador or Greenland Seas would substantially weaken the Atlantic Meridional Overturning Circulation. Here we analyze Robust Diagnostic Calculations conducted in a high-resolution global coupled climate model constrained by observed hydrographic climatology to provide a holistic picture of the long-term mean Atlantic Overturning Circulation at northern high latitudes. Our results suggest that the Arctic Ocean, not the Greenland Sea, is the northern terminus of the mean Atlantic Overturning Circulation; open-ocean deep convection, in either the Labrador or Greenland Seas, contributes minimally to the mean Atlantic Overturning Circulation, hence it would not necessarily be substantially weakened by a shutdown of open-ocean deep convection; horizontal circulation across sloping isopycnals contributes substantially (more than 40%) to the maximum mean northeastern subpolar Atlantic Overturning Circulation.
Climate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These experiments apply the nudging technique to the model integration and/or initialization to estimate possible improvements from nearly perfect model conditions. The results suggest that reducing sea surface temperature (SST) errors remains important for better predicting TC activity at long forecast leads—even in a flux-adjusted model with reduced climatological biases. Other error sources also contribute to biases in simulated TC activity, with notable manifestations on regional scales. A novel finding is that the coupling and initialization of the land and atmosphere components can affect seasonal TC prediction skill. Simulated year-to-year variations in June land conditions over North America show a significant lead correlation with the North Atlantic large-scale environment and TC activity. Improved land–atmosphere initialization appears to improve the Atlantic TC predictions initialized in some summer months. For short-lead predictions initialized in June, the potential skill improvements attributable to land–atmosphere initialization might be comparable to those achievable with perfect SST predictions. Overall, this study delineates the SST and non-oceanic error sources in predicting TC activity and highlights avenues for improving predictions. The nudging-based evaluation framework can be applied to other models and help improve predictions of other weather extremes.
Zhang, Gan, Levi G Silvers, Ming Zhao, and Thomas R Knutson, March 2021: Idealized aquaplanet simulations of tropical cyclone activity: Significance of temperature gradients, Hadley circulation, and zonal asymmetry. Journal of the Atmospheric Sciences, 78(3), doi:10.1175/JAS-D-20-0079.1877-902. [ Abstract ]
Earlier studies have proposed many semiempirical relations between climate and tropical cyclone (TC) activity. To explore these relations, this study conducts idealized aquaplanet experiments using both symmetric and asymmetric sea surface temperature (SST) forcings. With zonally symmetric SST forcings that have a maximum at 10°N, reducing meridional SST gradients around an Earth-like reference state leads to a weakening and southward displacement of the intertropical convergence zone. With nearly flat meridional gradients, warm-hemisphere TC numbers increase by nearly 100 times due particularly to elevated high-latitude TC activity. Reduced meridional SST gradients contribute to a poleward expansion of the tropics, which is associated with a poleward migration of the latitudes where TCs form or reach their lifetime maximum intensity. However, these changes cannot be simply attributed to the poleward expansion of Hadley circulation. Introducing zonally asymmetric SST forcings tends to decrease the global TC number. Regional SST warming—prescribed with or without SST cooling at other longitudes—affects local TC activity but does not necessarily increase TC genesis. While regional warming generally suppresses TC activity in remote regions with relatively cold SSTs, one experiment shows a surprisingly large increase of TC genesis. This increase of TC genesis over relatively cold SSTs is related to local tropospheric cooling that reduces static stability near 15°N and vertical wind shear around 25°N. Modeling results are discussed with scaling analyses and have implications for the application of the “convective quasi-equilibrium and weak temperature gradient” framework.
Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.
Zhang, Bosong, Brian J Soden, Gabriel A Vecchi, and Wenchang Yang, November 2021: Investigating the causes and impacts of convective aggregation in a high resolution atmospheric GCM. Journal of Advances in Modeling Earth Systems, 13(11), doi:10.1029/2021MS002675. [ Abstract ]
A ∼50 km resolution atmospheric general circulation model (GCM) is used to investigate the impact of radiative interactions on spatial organization of convection, the model's mean state, and extreme precipitation events in the presence of realistic boundary conditions. Mechanism-denial experiments are performed in which synoptic-scale feedbacks between radiation and dynamics are suppressed by overwriting the model-generated atmospheric radiative cooling rates with its monthly varying climatological values. When synoptic-scale radiative interactions are disabled, the annual mean circulation and precipitation remain almost unchanged, however tropical convection becomes less aggregated, with an increase in cloud fraction and relative humidity in the free troposphere but a decrease in both variables in the boundary layer. Changes in cloud fraction and relative humidity in the boundary layer exhibit more sensitivity to the presence of radiative interactions than variations in the degree of aggregation. The less aggregated state is associated with a decrease in the frequency of extreme precipitation events, coincident with a decrease in the dynamical contribution to the magnitude of extreme precipitation. At regional scales, the spatial contrast in radiative cooling between dry and moist regions diminishes when radiative interactions are suppressed, reducing the upgradient transport of energy, degree of aggregation, and frequency of extreme precipitation events. However, the mean width of the tropical rain belt remains almost unaffected when radiative interactions are disabled. These results offer insights into how radiation-circulation coupling affects the spatial organization of convection, distributions of clouds and humidity, and weather extremes.
Baker, Rachel E., Wenchang Yang, and Gabriel A Vecchi, et al., July 2020: Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science, 369(6501), doi:10.1126/science.abc2535315-319. [ Abstract ]
Preliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver. Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology. We find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size. A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion. Our findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.
Bieli, M, Adam H Sobel, Suzana J Camargo, Hiroyuki Murakami, and Gabriel A Vecchi, April 2020: Application of the Cyclone Phase Space to Extratropical Transition in a Global Climate Model. Journal of Advances in Modeling Earth Systems, 12(4), doi:10.1029/2019MS001878. [ Abstract ]
The authors analyze the global statistics of tropical cyclones (TCs) undergoing extratropical transition (ET) in the Forecast‐oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR‐FA). The Cyclone Phase Space (CPS) is used to diagnose ET. A simulation of the recent historical climate is analyzed and compared with data from the Japanese 55‐year Reanalysis (JRA‐55), and then a simulation of late 21st century climate under Representative Concentration Pathway 4.5 is compared to the historical simulation.
When CPS is applied to the FLOR‐FA output in the historical simulation, the results diverge from those obtained from JRA‐55 by having an unrealistic number of ET cases at low latitudes, due to the presence of strong local maxima in the upper‐level geopotential. These features mislead CPS into detecting a cold core where one is not present. The misdiagnosis is largely corrected by either replacing the maxima required by CPS with the 95th percentile values, smoothing the CPS trajectories in time, or both. Other climate models may contain grid‐scale structures akin to those in FLOR‐FA, and, when used for CPS analysis, require solutions such as those discussed here.
Comparisons of ET in the projected future climate with the historical climate show a number of changes that are robust to the details of the ET diagnosis, though few are statistically significant according to standard tests. Among them are an increase in the ET fraction and a reduction in the mean latitude at which ET occurs in the western North Pacific.
The Southern Ocean south of 30° S represents only one-third of the total ocean area, yet absorbs half of the total ocean anthropogenic carbon and over two-thirds of ocean anthropogenic heat. In the past, the Southern Ocean has also been one of the most sparsely measured regions of the global ocean. Here we use pre-2005 ocean shipboard measurements alongside novel observations from autonomous floats with biogeochemical sensors to calculate changes in Southern Ocean temperature, salinity, pH and concentrations of nitrate, dissolved inorganic carbon and oxygen over two decades. We find local warming of over 3 °C, salinification of over 0.2 psu near the Antarctic coast, and isopycnals are found to deepen between 65° and 40° S. We find deoxygenation along the Antarctic coast, but reduced deoxygenation and nitrate concentrations where isopycnals deepen farther north. The forced response of the Earth system model ESM2M does not reproduce the observed patterns. Accounting for meltwater and poleward-intensifying winds in ESM2M improves reproduction of the observed large-scale changes, demonstrating the importance of recent changes in wind and meltwater. Future Southern Ocean biogeochemical changes are likely to be influenced by the relative strength of meltwater input and poleward-intensifying winds. The combined effect could lead to increased Southern Ocean deoxygenation and nutrient accumulation, starving the global ocean of nutrients sooner than otherwise expected.
The decline of Arctic sea‐ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea‐ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically‐driven SIM export and negative feedbacks from sea‐ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice‐thickness observations collected after melt onset are particularly critical for summer Arctic sea‐ice predictions.
Camargo, Suzana J., C F Giulivi, Adam H Sobel, Allison A Wing, D Kim, Yumin Moon, Jeffrey D Strong, A Del Genio, M Kelley, Hiroyuki Murakami, Kevin A Reed, E Scoccimarro, Gabriel A Vecchi, Michael F Wehner, Colin M Zarzycki, and Ming Zhao, June 2020: Characteristics of model tropical cyclone climatology and the large-scale environment. Journal of Climate, 33(11), doi:10.1175/JCLI-D-19-0500.1. [ Abstract ]
Here we explore the relationship between the global climatological characteristics of tropical cyclones (TCs) in climate models and the modeled large-scale environment across a large number of models. We consider the climatology of TCs in 30 climate models with a wide range of horizontal resolutions. We examine if there is a systematic relationship between the climatological diagnostics for the TC activity (number of tropical cyclones (NTC) and accumulated cyclone energy (ACE)) by hemisphere in the models and the environmental fields usually associated with TC activity, when examined across a large number of models. For low-resolution models, there is no association between a conducive environment and TC activity, when integrated over space (tropical hemisphere) and time (all years of the simulation). As the model resolution increases, for a couple of variables, in particular vertical wind shear there is a statistically significant relationship in between the models’ TC characteristics and the environmental characteristics, but in most cases the relationship is either non-existent or the opposite of what is expected based on observations. It is important to stress that these results do not imply that there is no relationship between individual models’ environmental fields and their TC activity by basin with respect to intraseasonal or interannual variability or due to climate change. However, it is clear that when examined across many models, the models’ mean state does not have a consistent relationship with the models’ mean TC activity. Therefore, other processes associated with model physics, dynamical core, and resolution determine the climatological TC activity in climate models.
Capotondi, Antonietta, and Andrew T Wittenberg, et al., October 2020: Chapter 4: ENSO diversity In El Niño Southern Oscillation in a Changing Climate [McPhaden, M., A. Santoso, and W. Cai (eds.)], American Geophysical Union, Geophysical Monograph Series, doi:10.1002/9781119548164.ch465-86. [ Abstract ]
ENSO events display large interevent differences in amplitude, spatial pattern, and temporal evolution. The differences in spatial pattern, which have important consequences for ENSO teleconnections and societal impacts, have become known as “ENSO diversity.” In this chapter we review key aspects of ENSO diversity, including ENSO's surface and subsurface characteristics, underlying dynamics, predictability, low‐frequency variations, and long‐term evolution, as well as the representation of ENSO diversity in climate models. To better understand the origin of ENSO diversity and identify specific characteristics of different event types, many different classification schemes have been proposed. Here we describe these different approaches and the insights they may provide on the nature of event‐to‐event differences. The last two decades have seen a greater number of El Niño events with the largest sea surface temperature anomalies in the central Pacific. Current research seeks to determine whether such changes in ENSO characteristics were the result of anthropogenic greenhouse gas forcing or just a manifestation of natural variability, and whether and how climate change may affect ENSO diversity in the future.
Cha, E J., and Thomas R Knutson, et al., June 2020: Third Assessment on Impacts of Climate Change on Tropical Cyclones in the Typhoon Committee Region – Part II: Future Projections. Tropical Cyclone Research and Review, 9(2), doi:10.1016/j.tcrr.2020.04.005. [ Abstract ]
This paper assesses published findings on projections of future tropical cyclone (TC) activity in the ESCAP/WMO Typhoon Committee Region under climate change scenarios. This assessment also estimates the projected changes of key TC metrics for a 2oC anthropogenic global warming scenario for the western North Pacific (WNP) following the approach of a WMO Task Team, together with other reported findings for this region. For projections of TC genesis/frequency, most models suggest a reduction of TC frequency, but an increase in the proportion of very intense TCs over the WNP in the future. However, some individual studies project an increase in WNP TC frequency. Most studies agree on a projected increase of WNP TC intensity over the 21st century. All available projections for TC related precipitation in the WNP indicate an increase in TC related precipitation rate in a warmer climate. Anthropogenic warming may also lead to changes in TC prevailing tracks. A further increase in storm surge risk may result from increases in TC intensity. The most confident aspect of forced anthropogenic change in TC inundation risk derives from the highly confident expectation of further sea level rise, which we expect will exacerbate storm inundation risk in coastal regions, assuming all other factors equal.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
Deser, Clara, Flavio Lehner, Keith B Rodgers, T R Ault, Thomas L Delworth, P DiNezio, and Arlene M Fiore, et al., April 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nature Climate Change, 10(4), doi:10.1038/s41558-020-0731-2. [ Abstract ]
Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.
Ding, H, Matthew Newman, Michael A Alexander, and Andrew T Wittenberg, March 2020: Relating CMIP5 model biases to seasonal forecast skill in the tropical Pacific. Geophysical Research Letters, 47(5), doi:10.1029/2019GL086765. [ Abstract ]
We examine links between tropical Pacific mean state biases and ENSO forecast skill, using model‐analog hindcasts of sea surface temperature (SST; 1961‐2015) and precipitation (1979‐2015) at leads of 0‐12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model‐analog forecast skill has been demonstrated to match or even exceed traditional assimilation‐initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10 m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific, and the best SST forecasts at 6‐month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model‐analog hindcast skill – that is, how well a model can capture the observed evolution of tropical Pacific anomalies – is an informative ENSO metric for climate simulations.
Duan, Suqin Q., Kirsten L Findell, and Jonathon S Wright, December 2020: Three Regimes of Temperature Distribution Change over Dry Land, Moist Land and Oceanic Surfaces. Geophysical Research Letters, 47(24), doi:10.1029/2020GL090997. [ Abstract ]
Climate model simulations project different regimes of summertime temperature distribution changes under a quadrupling of CO2 for dry land, moist land, and oceanic surfaces. The entire temperature distribution shifts over dry land surfaces, while moist land surfaces feature an elongated upper tail of the distribution, with extremes increasing more than the corresponding means by ∼20% of the global mean warming. Oceanic surfaces show weaker warming relative to land surfaces, with no significant elongation of the upper tail. Dry land surfaces show little change in turbulent sensible (SH) or latent (LH) fluxes, with new balance reached with compensating adjustments among downwelling and upwelling radiative fluxes. By contrast, moist land surfaces show enhanced partitioning of turbulent flux toward SH, while oceanic surfaces show enhanced partitioning toward LH. Amplified warming of extreme temperatures over moist land surfaces is attributed to suppressed evapotranspiration and larger Bowen ratios.
We compare equilibrium climate sensitivity (ECS) estimates from pairs of long (≥800‐year) control and abruptly quadrupled CO2 simulations with shorter (150‐ and 300‐year) coupled atmosphere‐ocean simulations and slab ocean models (SOMs). Consistent with previous work, ECS estimates from shorter coupled simulations based on annual averages for years 1–150 underestimate those from SOM (−8% ± 13%) and long (−14% ± 8%) simulations. Analysis of only years 21–150 improved agreement with SOM (−2% ± 14%) and long (−8% ± 10%) estimates. Use of pentadal averages for years 51–150 results in improved agreement with long simulations (−4% ± 11%). While ECS estimates from current generation U.S. models based on SOM and coupled annual averages of years 1–150 range from 2.6°C to 5.3°C, estimates based longer simulations of the same models range from 3.2°C to 7.0°C. Such variations between methods argues for caution in comparison and interpretation of ECS estimates across models.
We describe the baseline coupled model configuration and simulation characteristics of GFDL's Earth System Model Version 4.1 (ESM4.1), which builds on component and coupled model developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation contributing to the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's CM4.0 development effort that focuses on ocean resolution for physical climate, ESM4.1 focuses on comprehensiveness of Earth system interactions. ESM4.1 features doubled horizontal resolution of both atmosphere (2° to 1°) and ocean (1° to 0.5°) relative to GFDL's previous‐generation coupled ESM2‐carbon and CM3‐chemistry models. ESM4.1 brings together key representational advances in CM4.0 dynamics and physics along with those in aerosols and their precursor emissions, land ecosystem vegetation and canopy competition, and multiday fire; ocean ecological and biogeochemical interactions, comprehensive land‐atmosphere‐ocean cycling of CO2, dust and iron, and interactive ocean‐atmosphere nitrogen cycling are described in detail across this volume of JAMES and presented here in terms of the overall coupling and resulting fidelity. ESM4.1 provides much improved fidelity in CO2 and chemistry over ESM2 and CM3, captures most of CM4.0's baseline simulations characteristics, and notably improves on CM4.0 in (1) Southern Ocean mode and intermediate water ventilation, (2) Southern Ocean aerosols, and (3) reduced spurious ocean heat uptake. ESM4.1 has reduced transient and equilibrium climate sensitivity compared to CM4.0. Fidelity concerns include (1) moderate degradation in sea surface temperature biases, (2) degradation in aerosols in some regions, and (3) strong centennial scale climate modulation by Southern Ocean convection.
Simulation of coupled carbon‐climate requires representation of ocean carbon cycling, but the computational burden of simulating the dozens of prognostic tracers in state‐of‐the‐art biogeochemistry ecosystem models can be prohibitive. We describe a six‐tracer biogeochemistry module of steady‐state phytoplankton and zooplankton dynamics in Biogeochemistry with Light, Iron, Nutrients and Gas (BLING version 2) with particular emphasis on enhancements relative to the previous version and evaluate its implementation in Geophysical Fluid Dynamics Laboratory's (GFDL) fourth‐generation climate model (CM4.0) with ¼° ocean. Major geographical and vertical patterns in chlorophyll, phosphorus, alkalinity, inorganic and organic carbon, and oxygen are well represented. Major biases in BLINGv2 include overly intensified production in high‐productivity regions at the expense of productivity in the oligotrophic oceans, overly zonal structure in tropical phosphorus, and intensified hypoxia in the eastern ocean basins as is typical in climate models. Overall, while BLINGv2 structural limitations prevent sophisticated application to plankton physiology, ecology, or biodiversity, its ability to represent major organic, inorganic, and solubility pumps makes it suitable for many coupled carbon‐climate and biogeochemistry studies including eddy interactions in the ocean interior. We further overview the biogeochemistry and circulation mechanisms that shape carbon uptake over the historical period. As an initial analysis of model historical and idealized response, we show that CM4.0 takes up slightly more anthropogenic carbon than previous models in part due to enhanced ventilation in the absence of an eddy parameterization. The CM4.0 biogeochemistry response to CO2 doubling highlights a mix of large declines and moderate increases consistent with previous models.
Fedorov, Alexey, Shineng Hu, and Andrew T Wittenberg, et al., October 2020: Chapter 8: ENSO low-frequency modulation and mean state interactions In El Niño Southern Oscillation in a Changing Climate [McPhaden, M., A. Santoso, and W. Cai (eds.)], American Geophysical Union, Geophysical Monograph Series, doi:10.1002/9781119548164.ch8173-198. [ Abstract ]
Is El Niño changing with global warming? Can we anticipate decades with extreme El Niño events? To answer these questions confidently, we need to understand the modulation of the El Niño Southern Oscillation phenomenon (ENSO) that occur on decadal and multidecadal timescales and involve changes in El Niño amplitude, periodicity, dominant “flavors”, shifts in the Intertropical Convergence Zone, and other properties. As major progress has been made in understanding various factors that can affect these characteristics of El Niño, two main paradigms have emerged to explain the observed modulation of ENSO: (i) internally generated variations due to the chaotic nature of the ocean‐atmosphere coupled system and (ii) externally driven variations due to cyclic or secular changes in the properties of the tropical background state such as mean winds or ocean thermocline depth. This article reviews these two paradigms in the context of available observations, idealized models, and comprehensive general circulation models describing El Niño. Which paradigm will dominate in the coming decades and whether global warming is already affecting El Niño remains unclear.
Guilyardi, Eric, Antonietta Capotondi, Matthieu Lengaigne, Sulian Thual, and Andrew T Wittenberg, October 2020: Chapter 9: ENSO modeling: History, progress and challenges In El Niño Southern Oscillation in a Changing Climate [McPhaden, M., A. Santoso, and W. Cai (eds.)], American Geophysical Union, Geophysical Monograph Series, doi:10.1002/9781119548164.ch9199-226. [ Abstract ]
Climate models are essential tools for understanding ENSO mechanisms and exploring the future, either via seasonal‐to‐decadal forecasting or climate projections. Because so few events are well observed, models are also needed to help reconstruct past variability, explore ENSO diversity, and understand the roles of the background mean state and external forcings in mediating ENSO behavior. In this chapter we review the history of ENSO modeling, showing the gradual improvement of models since the pioneering studies of the 1980s and 1990s and describing the existing hierarchy of model complexity. The rest of the chapter is devoted to coupled general circulation models (GCMs) and how these models perform, related model development and improvements, associated systematic biases and the strategies developed to address them, and methods of model evaluation in a multimodel context with reference to observations. We also review how successive generations of multimodel intercomparisons help bridge the gap between our theoretical understanding of ENSO and the representation of ENSO in coupled GCMs. Much of the improved understanding of ENSO in recent decades, addressed in other chapters of this monograph, was obtained from simulation strategies in which part of the coupled ocean‐atmosphere system was either simplified or omitted, such as atmosphere‐only, ocean‐only, partially coupled, or nudged simulations. We here review these strategies and the associated best practices, including their advantages and limitations. The ability of coupled GCMs to simulate ENSO continues to improve, offering exciting opportunities for research, forecasting, understanding past variations, and projecting the future behavior of ENSO and its global impacts. We list the challenges the community is facing, as well as opportunities for further improving ENSO simulations.
We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.
A diagnostic framework is developed to explain the response of tropical cyclones (TCs) to climate in high-resolution global atmospheric models having different complexity of boundary conditions. The framework uses vortex dynamics to identify the large-scale control on the evolution of TC precursors—first non-rotating convective clusters and then weakly rotating seeds. In experiments with perturbed sea surface temperature (SST) and CO2 concentration from the historical values, the response of TCs follows the response of seeds. The distribution of seeds is explained by the distribution of the non-rotating convective clusters multiplied by a probability that they transition to seeds. The distribution of convective clusters is constrained by the large-scale vertical velocity and is verified in aquaplanet experiments with shifting Inter tropical Convergence Zones. The probability of transition to seeds is constrained by the large-scale vorticity via an analytical function, representing the relative importance between vortex stretching and vorticity advection, and is verified in aquaplanet experiments with uniform SST. The consistency between seed and TC responses breaks down substantially when the realistic SST is perturbed such that the spatial gradient is significantly enhanced or reduced. In such cases, the difference between the responses is explained by a change in the ventilation index, which influences the fraction of seeds that develop into TCs. The proposed TC-climate relationship serves as a framework to explain the diversity of TC projection across models and forcing scenarios.
The semi-arid African Sahel region is highly sensitive to changes in monsoon precipitation, as much of the region’s workforce is employed in the agricultural industry (Hamro-Drotz and Programme 2011). Thus, studying the response of rainfall and aridity in this region to radiative perturbations is a matter of pressing humanitarian relevance. In addition, there is evidence to suggest that spatially asymmetric volcanic aerosols produce different hydroclimatic responses based on their hemispheric symmetry, both globally and in the Sahel. We use two different climate models, GFDL’s FLOR model (Vecchi et al. in J Clim 27(21):7994–8016, 2014) and NCAR’s CESM 1.1 model (Otto-Bliesner et al. in Bull Am Meteorol Soc 97(5):735–754, 2016), to characterize the response of rainfall in the Sahel to large volcanic eruptions based on the meridional symmetry of the volcanic eruptions. We find that in both the FLOR experiments simulating three large twentieth century eruptions and in the CESM Last Millennium Ensemble simulations of 46 historic volcanic eruptions, asymmetric Northern Hemisphere cooling causes a subsequent drying response in the Sahel, and Southern Hemisphere cooling causes a wetting, or “greening” response. Symmetric volcanic eruptions have a relatively small effect on rainfall in the Sahel. We also find that the FLOR results show a consistent response in the annual rainfall cycle in the Sahel for all three of the eruptions analyzed, with a reduction in rainfall in early summer followed by an increased rainfall in late summer. The annual cycle response of rainfall in the Sahel from the CESM experiments is different, in that the SH eruptions cause a rainfall maximum in August, NH eruptions cause a rainfall minimum in September, and symmetric eruptions show a slight increase in August and a decrease in October. Our results highlight the need for accurate meridional structures in historic volcanic forcing data used for climate models as well as the need for further study on regional effects of hemispherically asymmetric radiative forcing, especially as they might pertain to aerosol geoengineering.
Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic Subtropical High and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.
Knutson, Thomas R., et al., March 2020: Tropical Cyclones and Climate Change Assessment: Part II. Projected Response to Anthropogenic Warming. Bulletin of the American Meteorological Society, 101(3), doi:10.1175/BAMS-D-18-0194.1. [ Abstract ]
We assess model-projected changes in tropical cyclone activity for a 2°C anthropogenic warming. Medium-to-high confidence projections include increased tropical cyclone rainfall rates, intensity, and proportion of storms that reach Category 4-5 intensity globally.
Model projections of tropical cyclone (TC) activity response to anthropogenic warming in climate models are assessed. Observations, theory, and models, with increasing robustness, indicate rising global TC risk for some metrics -- that are projected to impact multiple regions.
A 2°C anthropogenic global warming is projected to impact TC activity as follows: i) The most confident TC-related projection is that sea level rise accompanying the warming will lead to higher storm inundation levels, assuming all other factors are unchanged. ii) For TC precipitation rates, there is at least medium-to-high confidence in an increase globally, with a median projected increase of 14%, or close to the rate of tropical water vapor increase with warming, at constant relative humidity. iii) For TC intensity, ten of 11 authors had at least medium-to-high confidence that the global average will increase. The median projected increase in lifetime maximum surface wind speeds is about 5% (range 1–10%) in available higher resolution studies. iv) For the global proportion (as opposed to frequency) of TCs that reach very intense (Category 4–5) levels, there is at least medium-to-high confidence in an increase, with a median projected change of +13%. Author opinion was more mixed and confidence levels lower for the following projections: v) a further poleward expansion of the latitude of maximum TC intensity in the western North Pacific; vi) a decrease of global TC frequency, as projected in most studies; vii) an increase in global very intense TC frequency (Category 4–5), seen most prominently in higher resolution models; and viii) a slowdown in TC translation speed.
Statistical downscaling methods are extensively used to refine future climate change projections produced by physical models. Distributional methods, which are among the simplest to implement, are also among the most widely used, either by themselves or in conjunction with more complex approaches. Here, building off of earlier work we evaluate the performance of seven methods in this class that range widely in their degree of complexity. We employ daily maximum temperature over the Continental U. S. in a "Perfect Model" approach in which the output from a large‐scale dynamical model is used as a proxy for both observations and model output. Importantly, this experimental design allows one to estimate expected performance under a future high‐emissions climate‐change scenario.
We examine skill over the full distribution as well in the tails, seasonal variations in skill, and the ability to reproduce the climate change signal. Viewed broadly, there generally are modest overall differences in performance across the majority of the methods. However, the choice of philosophical paradigms used to define the downscaling algorithms divides the seven methods into two classes, of better vs. poorer overall performance. In particular, the bias‐correction plus change‐factor approach performs better overall than the bias‐correction only approach. Finally, we examine the performance of some special tail treatments that we introduced in earlier work which were based on extensions of a widely used existing scheme. We find that our tail treatments provide a further enhancement in downscaling extremes.
Lee, Tsz-Cheung, and Thomas R Knutson, et al., March 2020: Third Assessment on Impacts of Climate Change on Tropical Cyclones in the Typhoon Committee Region – Part I: Observed Changes, Detection and Attribution. Tropical Cyclone Research and Review, 9(1), doi:10.1016/j.tcrr.2020.03.001. [ Abstract ]
Published findings on climate change impacts on tropical cyclones (TCs) in the ESCAP/WMO Typhoon Committee Region are assessed. We focus on observed TC changes in the western North Pacific (WNP) basin, including frequency, intensity, precipitation, track pattern, and storm surge. Results from an updated survey of impacts of past TC activity on various Members of the Typhoon Committee are also reported. Existing TC datasets continue to show substantial interdecadal variations in basin-wide TC frequency and intensity in the WNP. There has been encouraging progress in improving the consensus between different datasets concerning intensity trends. A statistically significant northwestward shift in WNP TC tracks since the 1980s has been documented. There is low-to-medium confidence in a detectable poleward shift since the 1940s in the average latitude where TCs reach their peak intensity in the WNP. A worsening of storm inundation levels is believed to be occurring due to sea level rise--due in part to anthropogenic influence--assuming all other factors equal. However, we are not aware that any TC climate change signal has been convincingly detected in WNP sea level extremes data. We also consider detection and attribution of observed changes based on an alternative Type II error avoidance perspective.
Lu, Lv, Shaoqing Zhang, Stephen G Yeager, Gokhan Danabasoglu, P Chang, Lixin Wu, Xiaopei Lin, Anthony Rosati, and Feiyu Lu, September 2020: Impact of Coherent Ocean Stratification on AMOC Reconstruction by Coupled Data Assimilation with a Biased Model. Journal of Climate, 33(17), doi:10.1175/JCLI-D-19-0735.1. [ Abstract ]
The Atlantic meridional overturning circulation (AMOC) is of great importance in Earth’s climate system, and reconstructing its structure and variability by combining observations with a coupled model is a key step in understanding historical and future states of AMOC. However, models always have systematic errors called bias owing to imperfect numerical representation of the real world. Model bias and the sparse nature of ocean observations, particularly in deep oceans, make it difficult to generate a complete historical picture of AMOC structure and variability. Here, two coupled models that are biased with respect to each other are used to design “twin” experiments to systematically study the influence of model bias on AMOC reconstruction. One model is used to produce the “observations” that sample the “true” solution of the AMOC to be reconstructed, while the other model is used to incorporate the “observations” to reconstruct the “truth” through coupled data assimilation (CDA). The degree to which the “truth” is recovered by a CDA scheme assesses the critical role of coherent (both upper- and deep-ocean incorporate enough observations to mitigate stratification instability) ocean stratification on AMOC reconstruction. Results show that balancing restoration of climatology and assimilation of observations is vital to better reconstruct AMOC structure and variability, given that most ocean observations are only available in the upper 2000 m. The gained results serve as a guideline in ocean-state estimation with a balance of deep restoring and upper data constraint for climate prediction initialization, especially for decadal predictions.
The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).
Menary, M, J Robson, Richard P Allan, Ben B B Booth, Christophe Cassou, G Gastineau, Jonathan M Gregory, D Hodson, C Jones, J Mignot, M A Ringer, Rowan Sutton, Laura J Wilcox, and Rong Zhang, July 2020: Aerosol‐forced AMOC changes in CMIP6 historical simulations. Geophysical Research Letters, 47(14), doi:10.1029/2020GL088166. [ Abstract ]
The Atlantic Meridional Overturning Circulation (AMOC) has been, and will continue to be, a key factor in the modulation of climate change both locally and globally. However, there remains considerable uncertainty in recent AMOC evolution. Here, we show that the multimodel mean AMOC strengthened by approximately 10% from 1850–1985 in new simulations from the 6th Coupled Model Intercomparison Project (CMIP6), a larger change than was seen in CMIP5. Across the models, the strength of the AMOC trend up to 1985 is related to a proxy for the strength of the aerosol forcing. Therefore, the multimodel difference is a result of stronger anthropogenic aerosol forcing on average in CMIP6 than CMIP5, which is primarily due to more models including aerosol‐cloud interactions. However, observational constraints—including a historical sea surface temperature fingerprint and shortwave radiative forcing in recent decades—suggest that anthropogenic forcing and/or the AMOC response may be overestimated.
The sensitivity of river discharge to climate-system warming is highly uncertain, and the processes that govern river discharge are poorly understood, which impedes climate-change adaptation. A prominent exemplar is the Colorado River, where meteorological drought and warming are shrinking a water resource that supports more than 1 trillion dollars of economic activity per year. A Monte Carlo simulation with a radiation-aware hydrologic model resolves the longstanding, wide disparity in sensitivity estimates and reveals the controlling physical processes. We estimate that annual mean discharge has been decreasing by 9.3% per degree Celsius of warming because of increased evapotranspiration, mainly driven by snow loss and a consequent decrease in reflection of solar radiation. Projected precipitation increases likely will not suffice to fully counter the robust, thermodynamically induced drying. Thus, an increasing risk of severe water shortages is expected.
Moon, Yumin, D Kim, Suzana J Camargo, Allison A Wing, Adam H Sobel, Hiroyuki Murakami, Kevin A Reed, E Scoccimarro, Gabriel A Vecchi, Michael F Wehner, Colin M Zarzycki, and Ming Zhao, February 2020: Azimuthally averaged wind and thermodynamic structures of tropical cyclones in global climate models and their sensitivity to horizontal resolution. Journal of Climate, 33(4), doi:10.1175/JCLI-D-19-0172.1. [ Abstract ]
Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs.
Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. When TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud-radiative feedbacks during the early developmental phases of TCs.
Moreno-Chamarro, Eduardo, J Marshall, and Thomas L Delworth, February 2020: Linking ITCZ migrations to AMOC and North Atlantic/Pacific SST decadal variability. Journal of Climate, 33(3), doi:10.1175/JCLI-D-19-0258.1. [ Abstract ]
We examine the link between migrations in the Intertropical Convergence Zone (ITCZ) and changes in the Atlantic Meridional Overturning Circulation (AMOC), Atlantic Multidecadal Variability (AMV), and Pacific Decadal Oscillation (PDO). We use a coupled climate model which allows us to integrate over climate noise and assess underlying mechanisms. We use an ensemble of 10 300-year-long simulations forced by a 50-year oscillatory NAO-derived surface heat flux anomaly in the North Atlantic, and a 4000-year-long preindustrial control simulation performed with the GFDL’s CM2.1 climate model. In both setups, an AMV phase change induced by a change in the AMOC’s cross-equatorial heat transport forces an atmospheric interhemispheric energy imbalance which is compensated by a change in the cross-equatorial atmospheric heat transport due to a meridional ITCZ shift. Such linkages occur on decadal timescales in the ensemble driven by the imposed forcing, and internally on multicentennial timescales in the control. Regional precipitation anomalies differ between the ensemble and the control for a zonally averaged ITCZ shift of similar magnitude, which suggests a dependence on timescale. Our study supports observational evidence of an AMV–ITCZ link in the 20th century and further links it to the AMOC, whose long-timescale variability can influence the phasing of ITCZ migrations. In contrast to the AMV, our calculations suggest that the PDO does not drive ITCZ migrations, because the PDO does not modulate the interhemispheric energy balance.
Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.
Ng, Chin Ho J., and Gabriel A Vecchi, May 2020: Large-scale environmental controls on the seasonal statistics of rapidly intensifying North Atlantic tropical cyclones. Climate Dynamics, 54(9-10), doi:10.1007/s00382-020-05207-4. [ Abstract ]
This study is concerned with the connections between the large-scale environment and the seasonal occurrence of rapid intensification (RI) of North Atlantic tropical cyclones. Physically-motivated statistical analysis using observations and reanalysis products suggests that for tropical cyclones over the open tropical North Atlantic, the interannual variability of the probability of storms undergoing RI is influenced by seasonal large-scale atmospheric and oceanic variables, but not so for storms over the Gulf of Mexico and western Caribbean Sea. We suggest that this differentiated response is due to the former region exhibiting a strong negative correlation between the seasonal anomalies of vertical wind shear and potential intensity. Differences in the mean climatology and subseasonal variations of the large-scale environment in these regions appear to play an insignificant role in the distinctive seasonal environmental controls on RI. We suggest that the interannual correlation of vertical wind shear and potential intensity is an indicator of seasonal predictability of tropical cyclone activity (including RI) across the tropics .
Three consecutive dry winters (2015–2017) in southwestern South Africa (SSA) resulted in the Cape Town “Day Zero” drought in early 2018. The contribution of anthropogenic global warming to this prolonged rainfall deficit has previously been evaluated through observations and climate models. However, model adequacy and insufficient horizontal resolution make it difficult to precisely quantify the changing likelihood of extreme droughts, given the small regional scale. Here, we use a high-resolution large ensemble to estimate the contribution of anthropogenic climate change to the probability of occurrence of multiyear SSA rainfall deficits in past and future decades. We find that anthropogenic climate change increased the likelihood of the 2015–2017 rainfall deficit by a factor of five to six. The probability of such an event will increase from 0.7 to 25% by the year 2100 under an intermediate-emission scenario (Shared Socioeconomic Pathway 2-4.5 [SSP2-4.5]) and to 80% under a high-emission scenario (SSP5-8.5). These results highlight the strong sensitivity of the drought risk in SSA to future anthropogenic emissions.
Recent laboratory and field studies point to an increase of sea salt aerosol (SSA) emissions with temperature, suggesting that SSA may lower climate sensitivity. We assess the impact of a strong (4.2 % K‐1) and weak (0.7% K‐1) temperature response of SSA emissions on the climate sensitivity of the coupled climate model CM4. We find that the stronger temperature dependence improves the simulation of marine aerosol optical depth sensitivity to temperature and lowers CM4 Transient Climate Response (‐0.12K) and Equilibrium Climate Sensitivity (‐0.5K). At CO2 doubling, the higher SSA emission sensitivity causes a negative radiative feedback (‐0.125 W m‐2 K‐1), which can only be partly explained by changes in the radiative effect of SSA (‐0.08 W m‐2 K‐1). Stronger radiative feedbacks are dominated by more negative low‐level clouds feedbacks in the Northern Hemisphere, which are partly offset by more positive feedbacks in the Southern Hemisphere associated with a weaker Atlantic Meridional Overturning Circulation.
Predybaylo, Evgeniya, Georgiy Stenchikov, Andrew T Wittenberg, and Sergey Osipov, September 2020: El Niño/Southern Oscillation response to low-latitude volcanic eruptions depends on ocean pre-conditions and eruption timing. Communications Earth and Environment, 1, 12, doi:10.1038/s43247-020-0013-y. [ Abstract ]
Proxy-based reconstructions of the past suggest that the Pacific ocean has often shown El Niño-like warming after low-latitude volcanic eruptions, while climate model simulations have suggested diverse responses. Here we present simulations from a coupled ocean–atmosphere model that illuminate the roles of ocean preconditioning, eruption magnitude and timing, and air–sea feedbacks in the El Niño/Southern Oscillation (ENSO) response to these eruptions. A deterministic component of the response, which dominates for boreal summer eruptions, leads to cooler tropical Pacific sea surface temperatures in the eruption year and El Niño-like warming the following year. A stochastic component is also important, especially for boreal winter eruptions. The simulated ENSO response depends nonlinearly on the eruption magnitude and the tropical Pacific conditions before the eruption. We conclude that adequate sampling is critical to accurately assess the ENSO responses in both models and observations.
Most present forecast systems for estuaries predict conditions for only a few days into the future. However, there are many reasons to expect that skillful estuarine forecasts are possible for longer time periods, including increasingly skillful extended atmospheric forecasts, the potential for lasting impacts of atmospheric forcing on estuarine conditions, and the predictability of tidal cycles. In this study, we test whether skillful estuarine forecasts are possible for up to 35 days into the future by combining an estuarine model of Chesapeake Bay with 35‐day atmospheric forecasts from an operational weather model. When compared with both a hindcast simulation from the same estuarine model and with observations, the estuarine forecasts for surface water temperature are skillful up to about two weeks into the future, and the forecasts for bottom temperature, surface and bottom salinity, and density stratification are skillful for all or the majority of the forecast period. Bottom oxygen forecasts are skillful when compared to the model hindcast, but not when compared with observations. We also find that skill for all variables in the estuary can be improved by taking the mean of multiple estuarine forecasts driven by an ensemble of atmospheric forecasts. Finally, we examine the forecasts in detail using two case studies of extreme events, and we discuss opportunities for improving the forecast skill.
Smith, D M., Adam A Scaife, Rosie Eade, Panos Athanasiadis, A Bellucci, Ingo Bethke, Roberto Bilbao, L F Borchert, Louis-Philippe Caron, François Counillon, Gokhan Danabasoglu, Thomas L Delworth, Francisco J Doblas-Reyes, Nick Dunstone, V Estella-Perez, S Flavoni, Leon Hermanson, N Keenlyside, Viatcheslav Kharin, M Kimoto, William J Merryfield, J Mignot, T Mochizuki, K Modali, P-A Moneri, Wolfgang A Müller, Dario Nicolí, Pablo Ortega, Klaus Pankatz, Holger Pohlmann, J Robson, P Ruggieri, Reinel Sospedra-Alfonso, Didier Swingedouw, Yan Wang, S Wild, Stephen G Yeager, Xiaosong Yang, and Liping Zhang, July 2020: North Atlantic climate far more predictable than models imply. Nature, 583, doi:10.1038/s41586-020-2525-0796-800. [ Abstract ]
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain. This leads to low confidence in regional projections, especially for precipitation, over the coming decades. The chaotic nature of the climate system may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.
Sun, Jingzhe, Zhengyu Liu, Feiyu Lu, Weimin Zhang, and Shaoqing Zhang, June 2020: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part III: Assimilation of Real World Reanalysis. Monthly Weather Review, 148(6), doi:10.1175/MWR-D-19-0304.1. [ Abstract ]
Recent studies proposed LACC (leading averaged coupled covariance) as an effective strongly coupled data assimilation (SCDA) method to improve the coupled state estimation over weakly coupled data assimilation (WCDA) in a coupled general circulation model (CGCM). This SCDA method, however, has been previously evaluated only in the perfect model scenario. Here, as a further step towards evaluating LACC for real world data assimilation, LACC is evaluated for the assimilation of reanalysis data in a CGCM. Several criterions are used to evaluate LACC against the benchmark WCDA. It is shown that despite significant model bias, LACC can improve the coupled state estimation over WCDA. Compared to WCDA, LACC increases the globally averaged anomaly correlation coefficients (ACCs) of sea surface temperature (SST) by 0.036 and atmosphere temperature at the bottom level (Ts) by 0.058. However, there also exist regions where WCDA outperforms LACC. Although the reduction in the anomaly root-mean-square error (RMSE) is not as consistently clear as the increase in ACC, LACC can largely correct the biased model climatology.
GFDL's new CM4.0 climate model has high transient and equilibrium climate sensitivities near the middle of the upper half of CMIP5 models. The CMIP5 models have been criticized for excessive sensitivity based on observations of present‐day warming and heat uptake and estimates of radiative forcing. An ensemble of historical simulations with CM4.0 produces warming and heat uptake that are consistent with these observations under forcing that is at the middle of the assessed distribution. Energy budget‐based methods for estimating sensitivities based on these quantities underestimate CM4.0's sensitivities when applied to its historical simulations. However, we argue using a simple attribution procedure that CM4.0's warming evolution indicates excessive transient sensitivity to greenhouse gases. This excessive sensitivity is offset prior to recent decades by excessive response to aerosol and land use changes.
Storm surge and coastal flooding caused by tropical cyclones (hurricanes) and extratropical cyclones (nor'easters) pose a threat to communities along the Atlantic coast of the United States. Climate change and sea level rise are altering the statistics of these extreme events in a rather complex fashion. Here we use a fully-coupled global weather/climate modeling system (GFDL CM4) to study characteristics of extreme daily sea level (ESL) along the US Atlantic coast and their response to global warming. We find that under natural weather processes, the Gulf of Mexico coast is most vulnerable to storm surge and related ESL. New Orleans is a striking hotspot with the highest surge efficiency in response to storm winds. Under a 1% per year atmospheric CO2 increase on centennial time scales, the anthropogenic signal in ESL is robust along the US East Coast. It can emerge from the background variability as soon as in twenty years, or even before global sea level rise is taken into account. The regional dynamic sea level rise induced by the weakening of the Atlantic meridional overturning circulation facilitates this early emergence, especially during wintertime coastal flooding associated with nor’easters. Along the Gulf Coast, ESL is sensitive to the modification of hurricane characteristics under the CO2 forcing.
The locally accumulated damage by tropical cyclones (TCs) can intensify substantially when these cyclones move more slowly. While some observational evidence suggests that TC motion might have slowed significantly since the mid-20th century (1), the robustness of the observed trend and its relation to anthropogenic warming have not been firmly established (2–4). Using large-ensemble simulations that directly simulate TC activity, we show that future anthropogenic warming can lead to a robust slowing of TC motion, particularly in the midlatitudes. The slowdown there is related to a poleward shift of the midlatitude westerlies, which has been projected by various climate models. Although the model’s simulation of historical TC motion trends suggests that the attribution of the observed trends of TC motion to anthropogenic forcings remains uncertain, our findings suggest that 21st-century anthropogenic warming could decelerate TC motion near populated midlatitude regions in Asia and North America, potentially compounding future TC-related damages.
Zhang, Shaoqing, Zhengyu Liu, X-F Zhang, Xinrong Wu, G Han, Y Zhao, X Yu, C Liu, Y Liu, S Wu, and Feiyu Lu, et al., June 2020: Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review. Climate Dynamics, 54(11-12), doi:10.1007/s00382-020-05275-6. [ Abstract ]
Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out.
Zhang, Wei, Gabriele Villarini, and Gabriel A Vecchi, August 2020: The East Asian Subtropical Jet Stream and Atlantic Tropical Cyclones. Geophysical Research Letters, 47(15), doi:10.1029/2020GL088851. [ Abstract ]
Atlantic tropical cyclones (TCs) can cause significant societal and economic impacts, as 2019's Dorian serves to remind us of these storms' destructiveness. Decades of effort to understand and predict Atlantic TC activity have improved seasonal forecast skill, but large uncertainties still remain, in part due to an incomplete understanding of the drivers of TC variability. Here we identify an association between the East Asian Subtropical Jet Stream (EASJ) during July–October and the frequency of Atlantic TCs (wind speed ≥34 knot) and hurricanes (wind speed ≥64 knot) during August–November based on observations for 1980–2018. This strong association is tied to the impacts of EASJ on a stationary Rossby wave train emanating from East Asia and the tropical Pacific to the North Atlantic, leading to changes in vertical wind shear in the Atlantic Main Development Region (80–20°W, 10–20°N).
We document the configuration and emergent simulation features from the Geophysical Fluid Dynamics Laboratory (GFDL) OM4.0 ocean/sea‐ice model. OM4 serves as the ocean/sea‐ice component for the GFDL climate and Earth system models. It is also used for climate science research and is contributing to the Coupled Model Intercomparison Project version 6 Ocean Model Intercomparison Project (CMIP6/OMIP). The ocean component of OM4 uses version 6 of the Modular Ocean Model (MOM6) and the sea‐ice component uses version 2 of the Sea Ice Simulator (SIS2), which have identical horizontal grid layouts (Arakawa C‐grid). We follow the Coordinated Ocean‐sea ice Reference Experiments (CORE) protocol to assess simulation quality across a broad suite of climate relevant features. We present results from two versions differing by horizontal grid spacing and physical parameterizations: OM4p5 has nominal 0.5° spacing and includes mesoscale eddy parameterizations and OM4p25 has nominal 0.25° spacing with no mesoscale eddy parameterization.
MOM6 makes use of a vertical Lagrangian‐remap algorithm that enables general vertical coordinates. We show that use of a hybrid depth‐isopycnal coordinate reduces the mid‐depth ocean warming drift commonly found in pure z* vertical coordinate ocean models. To test the need for the mesoscale eddy parameterization used in OM4p5, we examine the results from a simulation that removes the eddy parameterization. The water mass structure and model drift are physically degraded relative to OM4p5, thus supporting the key role for a mesoscale closure at this resolution.
Tropical cyclones that rapidly intensify are typically associated with the highest forecast errors and cause a disproportionate amount of human and financial losses. Therefore, it is crucial to understand if, and why, there are observed upward trends in tropical cyclone intensification rates. Here, we utilize two observational datasets to calculate 24-hour wind speed changes over the period 1982–2009. We compare the observed trends to natural variability in bias-corrected, high-resolution, global coupled model experiments that accurately simulate the climatological distribution of tropical cyclone intensification. Both observed datasets show significant increases in tropical cyclone intensification rates in the Atlantic basin that are highly unusual compared to model-based estimates of internal climate variations. Our results suggest a detectable increase of Atlantic intensification rates with a positive contribution from anthropogenic forcing and reveal a need for more reliable data before detecting a robust trend at the global scale.
Seasonal forecast systems can skillfully predict summer Arctic sea‐ice up to four months in advance. For some regions, however, there is a springtime predictability barrier that causes forecasts initialized prior to May to be less skillful. Since this barrier has only been documented in a few general circulation models (GCMs), we evaluate GCMs participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). We first show sea‐ice volume skillfully predicts summer sea ice‐area (SIA) and has similar skill to a perfect model experiment. Given this result, we assess regional SIA predictability across CMIP5 and find a universal predictability barrier in late spring. For SIA at each summer target month in the marginal seas of the Arctic basin, a notable drop in prediction skill occurs from June to May in each GCM. This suggests summer sea‐ice forecasts initialized after June 1 will have better prediction skill than forecasts initialized before.
Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea–ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system’s OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981–2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea–ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.
Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of Observing System Experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea-ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea-surface temperature (SST) satellite observations and subsurface conductivity-temperature-depth (CTD) measurements. The SST data is found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal timescales.
Castruccio, Frederic, Yohan Ruprich-Robert, Stephen G Yeager, Gokhan Danabasoglu, Rym Msadek, and Thomas L Delworth, March 2019: Modulation of Arctic Sea Ice Loss by Atmospheric Teleconnections from Atlantic Multi-Decadal Variability. Journal of Climate, 32(5), doi:10.1175/JCLI-D-18-0307.1. [ Abstract ]
Observed September Arctic sea ice has declined sharply over the satellite era. While most climate models forced by observed external forcing simulate a decline, few show trends matching the observations, suggesting either model deficiencies or significant contributions from internal variability. Using a set of perturbed climate model experiments, we provide evidence that atmospheric teleconnections associated with the Atlantic Multi-Decadal Variability (AMV) can drive low-frequency Arctic sea ice fluctuations. Even without AMV–related changes in ocean heat transport, AMV–like surface temperature anomalies lead to adjustments in atmospheric circulation patterns that produce similar Arctic sea ice changes in three different climate models. Positive AMV anomalies induce a decrease in the frequency of winter polar anticyclones, which is reflected both in the sea level pressure as a weakening of the Beaufort Sea High and in the surface temperature as warm anomalies in response to increased low-cloud cover. Positive AMV anomalies are also shown to favor an increased prevalence of an Arctic Dipole–like sea level pressure pattern in late winter / early spring. The resulting anomalous winds drive anomalous ice motions (dynamic effect). Combined with the reduced winter sea ice formation (thermodynamic effect), the Arctic sea ice becomes thinner, younger, and more prone to melt in summer. Following a phase shift to positive AMV, the resulting atmospheric teleconnections can lead to a decadal ice thinning trend in the Arctic Ocean of the order of 8-16% of the reconstructed long-term trend, and decadal trend (decline) in September Arctic sea ice area of up to 21% of the observed long-term trend.
Ding, H, Matthew Newman, Michael A Alexander, and Andrew T Wittenberg, February 2019: Diagnosing secular variations in retrospective ENSO seasonal forecast skill using CMIP5 model‐analogs. Geophysical Research Letters, 46(3), doi:10.1029/2018GL080598. [ Abstract ]
Retrospective tropical Indo‐Pacific forecasts for 1961‐2015 are made using 28 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) plus four models from the North American Multi‐Model Ensemble (NMME), using a model‐analog technique. Forecast ensembles are extracted from pre‐existing model simulations, by finding those states that initially best match an observed anomaly and tracking their subsequent evolution, requiring no additional model integrations. Model‐analog forecasts from the ten “best” CMIP5 models have skill for sea surface temperature (SST) and precipitation comparable to that of both the NMME model‐analog forecast ensemble and (since 1982) traditional assimilation‐initialized NMME hindcasts. ENSO forecast skill has no trend over the 55‐yr period and its decadal variations appear largely random, although the skill does improve during epochs of increased ENSO activity. Including the CMIP5‐projected effects of external radiative forcings improves the tropical SST skill of the model‐analog forecasts, but not within the ENSO region.
Fan, Ying, M Clark, David Lawrence, S C Swenson, L E Band, S L Brantley, P D Brooks, W E Dietrich, A Flores, G Grant, J W Kirchner, D S Mackay, J J McDonnell, and P C D Milly, et al., February 2019: Hillslope Hydrology in Global Change Research and Earth System Modeling. Water Resources Research, 55(2), doi:10.1029/2018WR023903. [ Abstract ]
Earth System Models (ESMs) are essential tools for understanding and predicting global change, but they cannot explicitly resolve hillslope‐scale terrain structures that fundamentally organize water, energy, and biogeochemical stores and fluxes at subgrid scales. Here we bring together hydrologists, Critical Zone scientists, and ESM developers, to explore how hillslope structures may modulate ESM grid‐level water, energy, and biogeochemical fluxes. In contrast to the one‐dimensional (1‐D), 2‐ to 3‐m deep, and free‐draining soil hydrology in most ESM land models, we hypothesize that 3‐D, lateral ridge‐to‐valley flow through shallow and deep paths and insolation contrasts between sunny and shady slopes are the top two globally quantifiable organizers of water and energy (and vegetation) within an ESM grid cell. We hypothesize that these two processes are likely to impact ESM predictions where (and when) water and/or energy are limiting. We further hypothesize that, if implemented in ESM land models, these processes will increase simulated continental water storage and residence time, buffering terrestrial ecosystems against seasonal and interannual droughts. We explore efficient ways to capture these mechanisms in ESMs and identify critical knowledge gaps preventing us from scaling up hillslope to global processes. One such gap is our extremely limited knowledge of the subsurface, where water is stored (supporting vegetation) and released to stream baseflow (supporting aquatic ecosystems). We conclude with a set of organizing hypotheses and a call for global syntheses activities and model experiments to assess the impact of hillslope hydrology on global change predictions.
Findell, Kirsten L., P W Keys, R J van der Ent, Benjamin R Lintner, Alexis Berg, and John P Krasting, November 2019: Rising Temperatures Increase Importance of Oceanic Evaporation as a Source for Continental Precipitation. Journal of Climate, 32(22), doi:10.1175/JCLI-D-19-0145.1. [ Abstract ]
Understanding vulnerabilities of continental precipitation to changing climatic conditions is of critical importance to society at large. Terrestrial precipitation is fed by moisture originating as evaporation from oceans and from recycling of water evaporated from continental sources. In this study, continental precipitation and evaporation recycling processes in the earth system model GFDL-ESM2G are shown to be consistent with estimates from two different reanalysis products. The GFDL-ESM2G simulations of historical and future climate also show that values of continental moisture recycling ratios were systematically higher in the past and will be lower in the future. Global mean recycling ratios decrease 2-3% with each degree of temperature increase, indicating increased importance of oceanic evaporation for continental precipitation. Theoretical arguments for recycling changes stem from increasing atmospheric temperatures and evaporative demand that drive more rapid increases in evaporation over oceans than over land as a result of terrestrial soil moisture limitations. Simulated recycling changes are demonstrated to be consistent with these theoretical arguments. A simple prototype describing this theory effectively captures the zonal mean behavior of GFDL-ESM2G. Implications of such behavior are particularly serious in rain-fed agricultural regions where crop yields will become increasingly soil moisture limited.
Mediterranean hurricanes (Medicanes) are intense cyclones that acquire tropical characteristics, associated with extreme winds and rainfall, thus posing a serious natural hazard to populated areas along Mediterranean coasts. Understanding how Medicanes will change with global warming remains, however, a challenge, because coarse resolution and/or the lack of atmosphere‐ocean coupling limit the reliability of numerical simulations. Here we investigate the Medicanes' response to global warming using a recently developed 25‐km global coupled climate model, which features a realistic representation of Medicanes in present climate conditions. It is found that despite a decrease in frequency, Medicanes potentially become more hazardous in the late century, lasting longer and producing stronger winds and rainfall. These changes are associated with a more robust hurricane‐like structure and are mainly confined to autumn. Thus, continued anthropogenic warming will increase the risks associated with Medicanes even in an intermediate scenario (Representative Concentration Pathway, RCP4.5), with potential natural and socioeconomic consequences.
Green, J K., Sonia I Seneviratne, Alexis Berg, and Kirsten L Findell, et al., January 2019: Large influence of soil moisture on long-term terrestrial carbon uptake. Nature, 565(7740), doi:10.1038/s41586-018-0848-x. [ Abstract ]
Although the terrestrial biosphere absorbs about 25 per cent of anthropogenic carbon dioxide (CO2) emissions, the rate of land carbon uptake remains highly uncertain, leading to uncertainties in climate projections1,2. Understanding the factors that limit or drive land carbon storage is therefore important for improving climate predictions. One potential limiting factor for land carbon uptake is soil moisture, which can reduce gross primary production through ecosystem water stress3,4, cause vegetation mortality5 and further exacerbate climate extremes due to land–atmosphere feedbacks6. Previous work has explored the impact of soil-moisture availability on past carbon-flux variability3,7,8. However, the influence of soil-moisture variability and trends on the long-term carbon sink and the mechanisms responsible for associated carbon losses remain uncertain. Here we use the data output from four Earth system models9 from a series of experiments to analyse the responses of terrestrial net biome productivity to soil-moisture changes, and find that soil-moisture variability and trends induce large CO2 fluxes (about two to three gigatons of carbon per year; comparable with the land carbon sink itself1) throughout the twenty-first century. Subseasonal and interannual soil-moisture variability generate CO2 as a result of the nonlinear response of photosynthesis and net ecosystem exchange to soil-water availability and of the increased temperature and vapour pressure deficit caused by land–atmosphere interactions. Soil-moisture variability reduces the present land carbon sink, and its increase and drying trends in several regions are expected to reduce it further. Our results emphasize that the capacity of continents to act as a future carbon sink critically depends on the nonlinear response of carbon fluxes to soil moisture and on land–atmosphere interactions. This suggests that the increasing trend in carbon uptake rate may not be sustained past the middle of the century and could result in accelerated atmospheric CO2 growth.
We present a new global‐to‐regional model, cfvGFS, able to explicitly (without parameterization) represent convection over part of the earth. This model couples the Geophysical Fluid Dynamics Laboratory Finite‐Volume Cubed‐Sphere Dynamical Core (FV3) to the Global Forecast System (GFS) physics and initial conditions, augmented with a six‐category microphysics and a modified planetary boundary layer scheme. We examine the characteristics of cfvGFS on a 3‐km continental United States domain nested within a 13‐km global model. The nested cfvGFS still has good hemispheric skill comparable to or better than the operational GFS, while supercell thunderstorms, squall lines, and derechos are explicitly‐represented over the refined region. In particular, cfvGFS has excellent representations of fine‐scale updraft helicity fields, an important proxy for severe weather forecasting. Precipitation biases are found to be smaller than in uniform‐resolution global models and competitive with operational regional models; the 3‐km domain also improves upon the global models in 2‐m temperature and humidity skill. We discuss further development of cfvGFS and the prospects for a unified global‐to‐regional prediction system.
We describe GFDL's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the pre‐industrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasi‐periodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
Herrara-Estrada, J E., J A Martinez, Francina Dominguez, and Kirsten L Findell, et al., May 2019: Reduced moisture transport linked to drought propagation across North America. Geophysical Research Letters, 46(10), doi:10.1029/2019GL082475. [ Abstract ]
Droughts can have devastating societal impacts. Yet, we do not fully understand the mechanisms that control their development, possibly affecting our ability to predict them. Here we run a moisture‐tracking analytical model using reanalysis data between 1980‐2016 to explore the role of reduced moisture transport in drought propagation. We find that agricultural droughts in multiple sub‐regions across North America may be amplified by decreased moisture transport from upwind land areas, which we link to reduced evapotranspiration and dry soil moisture upwind. We also find that decreases in precipitation recycling are correlated with decreases in moisture arriving from upwind areas. We estimate that decreases in moisture contributions from land areas accounted for 62% of the precipitation deficit during the 2012 Midwest drought. Our results suggest that the land‐surface may contain useful information for drought prediction, and highlight the importance of sustainable land‐use and of regional cooperation for drought risk management.
Solar geoengineering (SG) has the potential to restore average surface temperatures by increasing planetary albedo1–4, but this could reduce precipitation5–7. Thus, although SG might reduce globally aggregated risks, it may increase climate risks for some regions8–10. Here, using the high-resolution forecast-oriented low ocean resolution (HiFLOR) model—which resolves tropical cyclones and has an improved representation of present-day precipitation extremes11,12—alongside 12 models from the Geoengineering Model Intercomparison Project (GeoMIP), we analyse the fraction of locations that see their local climate change exacerbated or moderated by SG. Rather than restoring temperatures, we assume that SG is applied to halve the warming produced by doubling CO2 (half-SG). In HiFLOR, half-SG offsets most of the CO2-induced increase of simulated tropical cyclone intensity. Moreover, neither temperature, water availability, extreme temperature nor extreme precipitation are exacerbated under half-SG when averaged over any Intergovernmental Panel on Climate Change (IPCC) Special Report on Extremes (SREX) region. Indeed, for both extreme precipitation and water availability, less than 0.4% of the ice-free land surface sees exacerbation. Thus, while concerns about the inequality of solar geoengineering impacts are appropriate, the quantitative extent of inequality may be overstated13.
Kessler, William S., Susan E Wijffels, Sophie Cravatte, Neville Smith, Arun Kumar, Yosuke Fujii, William G Large, Yuhei Takaya, Harry Hendon, Stephen G Penny, Adrienne J Sutton, Peter G Strutton, Richard A Feely, Shinya Kouketsu, Sayaka Yasunaka, Yolande L Serra, Boris Dewitte, Ken Takahashi, Y Xue, Ivonne Montes, Carol Anne Clayson, Megan F Cronin, J Thomas Farrar, Tong Lee, Shayne McGregor, X Song, Janet Sprintall, and Andrew T Wittenberg, et al., May 2019: Second Report of TPOS 2020 , GOOS-234, 268pp. [ Abstract ]
Available online at https://tropicalpacific.org/tpos2020-project-archive/reports
Knutson, Thomas R., et al., October 2019: Tropical Cyclones and Climate Change Assessment: Part I. Detection and Attribution. Bulletin of the American Meteorological Society, 100(10), doi:10.1175/BAMS-D-18-0189.1. [ Abstract ]
We assess whether detectable changes in tropical cyclone activity have been identified in observations and whether any changes can be attributed to anthropogenic climate change.
An assessment was made of whether detectable changes in tropical cyclone (TC) activity are identifiable in observations and whether any changes can be attributed to anthropogenic climate change. Overall, historical data suggest detectable TC activity changes in some regions associated with TC track changes, while data quality and quantity issues create greater challenges for analyses based on TC intensity and frequency.
A number of specific published conclusions (case studies) about possible detectable anthropogenic influence on TCs were assessed using the conventional approach of preferentially avoiding Type I errors (i.e., overstating anthropogenic influence or detection). We conclude there is at least low-to-medium confidence that the observed poleward migration of the latitude of maximum intensity in the western North Pacific is detectable, or highly unusual compared to expected natural variability. Opinion on the author team was divided on whether any observed TC changes demonstrate discernible anthropogenic influence, or whether any other observed changes represent detectable changes.
The issue was then reframed by assessing evidence for detectable anthropogenic influence while seeking to reduce the chance of Type II errors (i.e., missing or understating anthropogenic influence or detection). For this purpose, we used a much weaker “balance of evidence” criterion for assessment. This leads to a number of more speculative TC detection and/or attribution statements, which we recognize have substantial potential for being false alarms (i.e., overstating anthropogenic influence or detection) but which may be useful for risk assessment. Several examples of these alternative statements, derived using this approach, are presented in the report.
The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July–August–September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.
The cumulative distribution function transform (CDFt) downscaling method has been used widely to provide local‐scale information and bias correction to output from physical climate models. The CDFt approach is one from the category of statistical downscaling methods that operates via transformations between statistical distributions. Although numerous studies have demonstrated that such methods provide value overall, much less effort has focused on their performance with regard to values in the tails of distributions. We evaluate the performance of CDFt‐generated tail values based on four distinct approaches, two native to CDFt and two of our own creation, in the context of a "Perfect Model" setting in which global climate model output is used as a proxy for both observational and model data. We find that the native CDFt approaches can have sub‐optimal performance in the tails, particularly with regard to the maximum value. However, our alternative approaches provide substantial improvement.
Nitrogen (N) pollution is shaped by multiple processes, the combined effects of which remain uncertain, particularly in the tropics. We use a global land biosphere model to analyze historical terrestrial-freshwater N budgets, considering the effects of anthropogenic N inputs, atmospheric CO2, land use, and climate. We estimate that globally, land currently sequesters 11 (10–13)% of annual N inputs. Some river basins, however, sequester >50% of their N inputs, buffering coastal waters against eutrophication and society against greenhouse gas-induced warming. Other basins, releasing >25% more than they receive, are mostly located in the tropics, where recent deforestation, agricultural intensification, and/or exports of land N storage can create large N pollution sources. The tropics produce 56 ± 6% of global land N pollution despite covering only 34% of global land area and receiving far lower amounts of fertilizers than the extratropics. Tropical land use should thus be thoroughly considered in managing global N pollution.
Li, F, S Lozier, Gokhan Danabasoglu, N P Holliday, Young-Oh Kwon, Anastasia Romanou, Stephen G Yeager, and Rong Zhang, July 2019: Local and downstream relationships between Labrador Sea Water volume and North Atlantic meridional overturning circulation variability. Journal of Climate, 32(13), doi:10.1175/JCLI-D-18-0735.1. [ Abstract ]
While it has generally been understood that the production of Labrador Sea Water (LSW) impacts the Atlantic meridional overturning circulation (MOC), this relationship has not been explored extensively nor validated against observations. To explore this relationship, a suite of global ocean and ocean–sea-ice models forced by the same interannually-varying atmospheric dataset, varying in resolution from non-eddy-permitting to eddy-permitting (1°–1/4°), is analyzed to investigate the local and downstream relationships between LSW formation and the MOC on interannual to decadal time scales. While all models display a strong relationship between changes in the LSW volume and the MOC in the Labrador Sea, this relationship degrades considerably downstream of the Labrador Sea. In particular, there is no consistent pattern among the models in the North Atlantic subtropical basin over interannual to decadal time scales. Furthermore, the strong response of the MOC in the Labrador Sea to LSW volume changes in that basin may be biased by the overproduction of LSW in many models compared to observations. This analysis shows that changes in LSW volume in the Labrador Sea cannot be clearly and consistently linked to a coherent MOC response across latitudes over interannual to decadal time scales in ocean hindcast simulations of the last half-century. Similarly, no coherent relationships are identified between the MOC and the Labrador Sea mixed layer depth or the density of newly formed LSW across latitudes or across models over interannual to decadal time scales.
Liu, Maofeng, Gabriel A Vecchi, James A Smith, and Thomas R Knutson, October 2019: Causes of large projected increases in hurricane precipitation rates with global warming. npj Climate and Atmospheric Science, 2, 38, doi:10.1038/s41612-019-0095-3. [ Abstract ]
Recent climate modeling studies point to an increase in tropical cyclone rainfall rates in response to climate warming. These studies indicate that the percentage increase in tropical cyclone rainfall rates often outpaces the increase in saturation specific humidity expected from the Clausius-Clapeyron relation (~7% °C−1). We explore the change in tropical cyclone rainfall rates over all oceans under global warming using a high-resolution climate model with the ability to simulate the entire intensity spectrum of tropical cyclones. Consistent with previous results, we find a robust increase of tropical cyclone rainfall rates. The percentage increase for inner-core tropical cyclone rainfall rates in our model is markedly larger than the Clausius-Clapeyron rate. However, when the impact of storm intensity is excluded, the rainfall rate increase shows a much better match with the Clausius-Clapeyron rate, suggesting that the “super Clausius-Clapeyron” scaling of rainfall rates with temperature increase is due to the warming-induced increase of tropical cyclone intensity. The increase of tropical cyclone intensity and environmental water vapor, in combination, explain the tropical cyclone rainfall rate increase under global warming.
This study explores the impact of El Niño and La Niña events on precipitation and circulation in East Asia. The results are based on statistical analysis of various observational datasets and Geophysical Fluid Dynamics Laboratory’s (GFDL’s) global climate model experiments. Multiple observational datasets and certain models show that in the southeastern coast of China, precipitation exhibits a nonlinear response to Central Pacific sea surface temperature anomalies during boreal deep fall/early winter. Higher mean rainfall is observed during both El Niño and La Niña events compared to the ENSO-Neutral phase, by an amount of approximately 0.4–0.5 mm/day on average per oC change. We argue that, in October to December, while the precipitation increases during El Niño are the result of anomalous onshore moisture fluxes, those during La Niña are driven by the persistence of terrestrial moisture anomalies resulting from earlier excess rainfall in this region. This is consistent with the nonlinear extreme rainfall behavior in coastal southeastern China, which increases during both ENSO phases and becomes more severe during El Niño than La Niña events.
Climate variations have a profound impact on marine ecosystems and the communities that depend upon them. Anticipating ecosystem shifts using global Earth system models (ESMs) could enable communities to adapt to climate fluctuations and contribute to long-term ecosystem resilience. We show that newly developed ESM-based marine biogeochemical predictions can skillfully predict satellite-derived seasonal to multiannual chlorophyll fluctuations in many regions. Prediction skill arises primarily from successfully simulating the chlorophyll response to the El Niño–Southern Oscillation and capturing the winter reemergence of subsurface nutrient anomalies in the extratropics, which subsequently affect spring and summer chlorophyll concentrations. Further investigations suggest that interannual fish-catch variations in selected large marine ecosystems can be anticipated from predicted chlorophyll and sea surface temperature anomalies. This result, together with high predictability for other marine-resource–relevant biogeochemical properties (e.g., oxygen, primary production), suggests a role for ESM-based marine biogeochemical predictions in dynamic marine resource management efforts.
The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including 8 typhoons/hurricanes. This activity was successfully predicted up to 5 months in advance by the Geophysical Fluid Dynamics Laboratory Forecast‐oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, NICAM and MRI‐AGCM) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific, and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and MRI‐AGCM). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity.
Scaife, Adam A., L Ferranti, Oscar Alves, Panos Athanasiadis, J Baehr, M Dequé, T Dippe, Nick Dunstone, D Fereday, Richard G Gudgel, R J Greatbatch, Leon Hermanson, Yukiko Imada, S Jain, Arun Kumar, C MacLachlan, William J Merryfield, Wolfgang A Müller, Hong-Li Ren, Doug Smith, Yuhei Takaya, Gabriel A Vecchi, and Xiaosong Yang, February 2019: Tropical rainfall predictions from multiple seasonal forecast systems. International Journal of Climatology, 39(2), doi:10.1002/joc.5855. [ Abstract ]
We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year‐to‐year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores.
We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter‐basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi‐model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter‐annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins.
These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter‐basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real‐time forecasts.
Slater, L J., Gabriele Villarini, A Allen Bradley, and Gabriel A Vecchi, December 2019: A dynamical statistical framework for seasonal streamflow forecasting in an agricultural watershed. Climate Dynamics, 53(12), doi:10.1007/s00382-017-3794-7. [ Abstract ]
The state of Iowa in the US Midwest is regularly affected by major floods and has seen a notable increase in agricultural land cover over the twentieth century. We present a novel statistical-dynamical approach for probabilistic seasonal streamflow forecasting using land cover and General Circulation Model (GCM) precipitation forecasts. Low to high flows are modelled and forecast for the Raccoon River at Van Meter, a 8900 km2 catchment located in central-western Iowa. Statistical model fits for each streamflow quantile (from seasonal minimum to maximum; predictands) are based on observed basin-averaged total seasonal precipitation, annual row crop (corn and soybean) production acreage, and observed precipitation from the month preceding each season (to characterize antecedent wetness conditions) (predictors). Model fits improve when including agricultural land cover and antecedent precipitation as predictors, as opposed to just precipitation. Using the dynamically-updated relationship between predictand and predictors every year, forecasts are computed from 1 to 10 months ahead of every season based on annual row crop acreage from the previous year (persistence forecast) and the monthly precipitation forecasts from eight GCMs of the North American Multi-Model Ensemble (NMME). The skill of our forecast streamflow is assessed in deterministic and probabilistic terms for all initialization months, flow quantiles, and seasons. Overall, the system produces relatively skillful streamflow forecasts from low to high flows, but the skill does not decrease uniformly with initialization time, suggesting that improvements can be gained by using different predictors for specific seasons and flow quantiles.
Smith, D M., Rosie Eade, Adam A Scaife, Louis-Philippe Caron, Gokhan Danabasoglu, T DelSole, Thomas L Delworth, Francisco J Doblas-Reyes, Nick Dunstone, Leon Hermanson, Viatcheslav Kharin, M Kimoto, William J Merryfield, T Mochizuki, Wolfgang A Müller, Holger Pohlmann, Stephen G Yeager, and Xiaosong Yang, May 2019: Robust skill of decadal climate predictions. npj Climate and Atmospheric Science, 2, 13, doi:10.1038/s41612-019-0071-y. [ Abstract ]
There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.
van Oldenborgh, G J., E Mitchell-Larson, and Gabriel A Vecchi, et al., November 2019: Cold waves are getting milder in the northern midlatitudes. Environmental Research Letters, 14(11), doi:10.1088/1748-9326/ab4867. [ Abstract ]
The strong two-day cold wave in the midwestern United States in January 2019 again ignited the discussion as to whether cold waves are getting more severe or not as a result of Arctic amplification due to climate change. Assessing the evolution of cold waves in the northern hemisphere midlatitudes in the observations has been difficult because the variability of cold waves is large compared to anthropogenic warming. In order to detect changes in cold spells, two complementary ways to optimise the signal-to-noise ratio are employed: multi-decadal series at individual stations, and for shorter time periods by using spatially aggregated measures. Global warming is now strong enough to make trends clear at individual stations when considering long enough (>50 yr) records of daily minimum and maximum temperature. Calculating the land area that has temperatures below the 1-in-10 year return value (defined over 1951–1980) enables us to investigate trends over a shorter time horizon. The long-term station data have strong decreases everywhere in the lowest minimum temperature. Warming trends in the lowest maximum temperature are smaller over most of the Northern Hemisphere, with dataset-dependent indications of possible negative trends in parts of the United States and Mexico. Considering the area experiencing cold waves over the last decades, the most notable feature is a sharp decline of this area since the 1980s. The natural variability is still so large that it is possible to arbitrarily select starting dates after the decline for which the trend is slightly positive in smaller regions like North America or Europe. However, these values are within uncertainties compatible with a steady decline and have differing starting dates in North America and Europe. An analysis of the entire northern midlatitudes confirms the steady decrease in severity and frequency of cold waves over the last decades in the observations.
Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.
Villarini, Gabriele, B Luitel, and Gabriel A Vecchi, et al., December 2019: Multi-model ensemble forecasting of North Atlantic tropical cyclone activity. Climate Dynamics, 53(12), doi:10.1007/s00382-016-3369-z. [ Abstract ]
North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical–dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982–2015. The skill of these statistical–dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.
Walsh, Kevin J., Suzana J Camargo, Thomas R Knutson, James Kossin, Tsz-Cheung Lee, Hiroyuki Murakami, and Christina M Patricola, December 2019: Tropical cyclones and climate change. Tropical Cyclone Research and Review, 8(4), doi:10.1016/j.tcrr.2020.01.004. [ Abstract ]
Since the Eighth International Workshop on Tropical Cyclones (IWTC-8), held in December 2014, progress has been made in our understanding of the relationship between tropical cyclone (TC) characteristics, climate and climate change. New analysis of observations has revealed trends in the latitude of maximum TC intensity and in TC translation speed. Climate models are demonstrating an increasing ability to simulate the observed TC climatology and its regional variations. The limited representation of air-sea interaction processes in most climate simulations of TCs remains an issue. Consensus projections of future TC behavior continue to indicate decreases in TC numbers, increases in their maximum intensities and increases in TC-related rainfall. Future sea level rise will exacerbate the impact of storm surge on coastal regions, assuming all other factors equal. Studies have also begun to estimate the effect on TCs of the climate change that has occurred to date. Recommendations are made regarding future research directions.
Wing, Allison A., Suzana J Camargo, Adam H Sobel, D Kim, Yumin Moon, Hiroyuki Murakami, Kevin A Reed, Gabriel A Vecchi, Michael F Wehner, Colin M Zarzycki, and Ming Zhao, September 2019: Moist static energy budget analysis of tropical cyclone intensification in high-resolution climate models. Journal of Climate, 32(18), doi:10.1175/JCLI-D-18-0599.1. [ Abstract ]
Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spin-up, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the inter-model spread in TC simulation.
Atlantic Multidecadal Variability (AMV) is a multivariate phenomenon. Here for the first time we obtain a multivariate AMV index (MAI) and associated patterns using Multivariate Empirical Orthogonal Function (MEOF) analysis to explore the multivariate nature of AMV. Coherent multidecadal variability that is unique to the Atlantic is found in the observed MEOF‐extracted AMV, various AMV‐related indices, and an Atlantic Meridional Overturning Circulation (AMOC) fingerprint. For comparison, the signal associated with global mean sea surface temperature (SST) is removed from both observations and Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. The residual CMIP5 forced basin‐wide SST‐based AMV index disagrees strongly with the observed residual, which retains a strong AMV signal. The MEOF approach still extracts a residual CMIP5 forced AMV signal that is unique to the Atlantic, although very different from observations. Our findings suggest that the observed AMV is not dominated by external forcing.
Explosive volcanic eruptions have large climate impacts, and can serve as observable tests of the climatic response to radiative forcing. Using a high resolution climate model, we contrast the climate responses to Pinatubo, with symmetric forcing, and those to Santa Maria and Agung, which had meridionally asymmetric forcing. Although Pinatubo had larger global‐mean forcing, asymmetric forcing strongly shifts the latitude of tropical rainfall features, leading to larger local precipitation/TC changes. For example, North Atlantic TC activity over is enhanced/reduced by SH‐forcing (Agung)/NH‐forcing (Santa Maria), but changes little in response to the Pinatubo forcing. Moreover, the transient climate sensitivity estimated from the response to Santa Maria is 20% larger than that from Pinatubo or Agung. This spread in climatic impacts of volcanoes needs to be considered when evaluating the role of volcanoes in global and regional climate, and serves to contextualize the well‐observed response to Pinatubo.
This study examines the performance of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution version of CM2.5 (FLOR; ~ 50-km mesh) and high-resolution FLOR (HiFLOR; ~ 25-km mesh) in reproducing the climatology and interannual variability in rainfall associated with tropical cyclones (TCs) in both sea surface temperature (SST)-nudging and seasonal-forecast experiments. Overall, HiFLOR outperforms FLOR in capturing the observed climatology of TC rainfall, particularly in East Asia, North America and Australia. In general, both FLOR and HiFLOR underestimate the observed TC rainfall in the coastal regions along the Bay of Bengal, connected to their failure to accurately simulate the bimodal structure of the TC genesis seasonality. A crucial factor in capturing the climatology of TC rainfall by the models is the simulation of the climatology of spatial TC density. Overall, while HiFLOR leads to a better characterization of the areas affected by TC rainfall, the SST-nudging and seasonal-forecast experiments with both models show limited skill in reproducing the year-to-year variation in TC rainfall. Ensemble-based estimates from these models indicate low potential skill for year-to-year variations in TC rainfall, yet the models show lower skill than this. Therefore, the low skill for interannual TC rainfall in these models reflects both a fundamental limit on predictability/reproducibility of seasonal TC rainfall as well as shortcomings in the models.
Observed Southern Ocean surface cooling and sea-ice expansion over the past several decades are inconsistent with many historical simulations from climate models. Here we show that natural multidecadal variability involving Southern Ocean convection may have contributed strongly to the observed temperature and sea-ice trends. These observed trends are consistent with a particular phase of natural variability of the Southern Ocean as derived from climate model simulations. Ensembles of simulations are conducted starting from differing phases of this variability. The observed spatial pattern of trends is reproduced in simulations that start from an active phase of Southern Ocean convection. Simulations starting from a neutral phase do not reproduce the observed changes, similarly to the multimodel mean results of CMIP5 models. The long timescales associated with this natural variability show potential for skilful decadal prediction.
Zhang, Rong, et al., June 2019: A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts. Reviews of Geophysics, 57(2), doi:10.1029/2019RG000644. [ Abstract ]
By synthesizing recent studies employing a wide range of approaches (modern observations, paleo reconstructions, and climate model simulations), this paper provides a comprehensive review of the linkage between multidecadal Atlantic Meridional Overturning Circulation (AMOC) variability and Atlantic Multidecadal Variability (AMV) and associated climate impacts. There is strong observational and modeling evidence that multidecadal AMOC variability is a crucial driver of the observed AMV and associated climate impacts, and an important source of enhanced decadal predictability and prediction skill. The AMOC‐AMV linkage is consistent with observed key elements of AMV. Furthermore, this synthesis also points to a leading role of the AMOC in a range of AMV‐related climate phenomena having enormous societal and economic implications, e.g., Intertropical Convergence Zone (ITCZ) shifts; Sahel and Indian monsoons; Atlantic hurricanes; El Niño Southern Oscillation; Pacific Decadal Variability; North Atlantic Oscillation; climate over Europe, North America, and Asia; Arctic sea ice and surface air temperature; and hemispheric‐scale surface temperature. Paleoclimate evidence indicates that a similar linkage between multidecadal AMOC variability and AMV and many associated climate impacts may also have existed in the preindustrial era; that AMV has enhanced multidecadal power significantly above a red noise background; and that AMV is not primarily driven by external forcing. The role of the AMOC in AMV and associated climate impacts has been underestimated in most state‐of‐the‐art climate models, posing significant challenges but also great opportunities for substantial future improvements in understanding and predicting AMV and associated climate impacts.
Improving the seasonal prediction of tropical cyclone (TC) activity demands a robust analysis of the prediction skill and the inherent predictability of TC activity. Using the resampling technique, this study analyzes a state‐of‐the‐art prediction system and offers a robust assessment of when and where the seasonal prediction of TC activity is skillful. We found that uncertainties of initial conditions affect the predictions and the skill evaluation significantly. The sensitivity of predictions to initial conditions also suggests that landfall and high‐latitude activity are inherently harder to predict. The lower predictability is consistent with the relatively low prediction skill in these regions. Additionally, the lower predictability is largely related to the atmospheric environment rather than the sea surface temperature, at least for the predictions initialized shortly before the hurricane season. These findings suggest the potential for improving the seasonal TC prediction and will help the development of the next‐generation prediction systems.
Zhang, Wei, Gabriele Villarini, and Gabriel A Vecchi, December 2019: Impacts of the Pacific meridional mode on rainfall over the maritime continent and australia: potential for seasonal predictions. Climate Dynamics, 53(12), doi:10.1007/s00382-017-3652-7. [ Abstract ]
The skill of monthly sea surface temperature (SST) anomaly predictions for large marine ecosystems (LMEs) in coastal regions of the United States and Canada is assessed using simulations from the climate models in the North American Multimodel Ensemble (NMME). The forecasts based on the full ensemble are generally more skillful than predictions from even the best single model. The improvement in skill is particularly noteworthy for probability forecasts that categorize SST anomalies into upper (warm) and lower (cold) terciles. The ensemble provides a better estimate of the full range of forecast values than any individual model, thereby correcting for the systematic over-confidence (under-dispersion) of predictions from an individual model. Probability forecasts, including tercile predictions from the NMME, are used frequently in seasonal forecasts for atmospheric variables and may have many uses in marine resource management.
Zhang, Gan, Thomas R Knutson, and Stephen T Garner, December 2019: Impacts of Extratropical Weather Perturbations on Tropical Cyclone Activity: Idealized Sensitivity Experiments with a Regional Atmospheric Model. Geophysical Research Letters, 46(23), doi:10.1029/2019GL085398. [ Abstract ]
Extratropical weather perturbations have been linked to Atlantic tropical cyclones (TC) activity in observations. However, modeling studies of the extratropical impact are scarce and disagree about its importance and climate implications. Using a non‐hydrostatic regional atmospheric model, we explore the extratropical impact by artificially suppressing extratropical weather perturbations at the tropical–extratropical interface. Our 22‐year simulations of August–October suggest that the extratropical suppression adds ~3.7 Atlantic TCs per season on average, although the response varies among individual years. The TC response mainly appears within 30°N–40°N, where tropical cyclogenesis frequency quadruples compared to control simulations. This increased cyclogenesis, accompanied by a strong increase of mid‐tropospheric relative humidity, arises as the perturbation suppression reduces the extratropical interference of TC development. The suppression of extratropical perturbations is highly idealized but may suggest mechanisms by which extratropical atmospheric variability potentially influences TC activity in past or future altered climate states.
As one of the first global coupled climate models to simulate and predict category 4 and 5 (Saffir–Simpson scale) tropical cyclones (TCs) and their interannual variations, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model at the Geophysical Fluid Dynamics Laboratory (GFDL) represents a novel source of insight on how the entire TC intensification distribution could be transformed due to climate change. In this study, three 70-year HiFLOR experiments are performed to identify the effects of climate change on TC intensity and intensification. For each of the experiments, sea surface temperature (SST) is nudged to different climatological targets and atmospheric radiative forcing is specified, allowing us to explore the sensitivity of TCs to these conditions.
First, a control experiment, which uses prescribed climatological ocean and radiative forcing based on observations during the years 1986-2005, is compared to two observational records and evaluated for its ability to capture the mean TC behavior during these years. The simulated intensification distributions as well as the percentage of TCs that become major hurricanes show similarities with observations. The control experiment is then compared to two 21st century experiments, in which the climatological SSTs from the control experiment are perturbed by multimodel projected SST anomalies and atmospheric radiative forcing from either 2016-2035 or 2081-2100 (RCP4.5 scenario). The frequency, intensity, and intensification distribution of TCs all shift to higher values as the 21st century progresses. HiFLOR’s unique response to climate change and fidelity in simulating the present climate lays the groundwork for future studies involving models of this type.
Meltwater from the Antarctic Ice Sheet is projected to cause up to one metre of sea-level rise by 2100 under the highest greenhouse gas concentration trajectory (RCP8.5) considered by the Intergovernmental Panel on Climate Change (IPCC). However, the effects of meltwater from the ice sheets and ice shelves of Antarctica are not included in the widely used CMIP5 climate models, which introduces bias into IPCC climate projections. Here we assess a large ensemble simulation of the CMIP5 model ‘GFDL ESM2M’ that accounts for RCP8.5-projected Antarctic Ice Sheet meltwater. We find that, relative to the standard RCP8.5 scenario, accounting for meltwater delays the exceedance of the maximum global-mean atmospheric warming targets of 1.5 and 2 degrees Celsius by more than a decade, enhances drying of the Southern Hemisphere and reduces drying of the Northern Hemisphere, increases the formation of Antarctic sea ice (consistent with recent observations of increasing Antarctic sea-ice area) and warms the subsurface ocean around the Antarctic coast. Moreover, the meltwater-induced subsurface ocean warming could lead to further ice-sheet and ice-shelf melting through a positive feedback mechanism, highlighting the importance of including meltwater effects in simulations of future climate.
The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth System models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and the computational constraints of macroscale models, until now these data have been underutilized for global applications. As a proof of concept, this study explores over a 1/4 degree (~ 25 km) grid cell in southeastern California how to effectively and efficiently harness these data in Earth System models. First, a novel hierarchical multivariate clustering approach (HMC) is used to summarize the high dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this data-driven approach to assemble the model tiles impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled by HMC. The results over the 1/4 degree macroscale grid cell and the underlying 30-meter fine-scale grid in southeastern California show that: 1) the observed similarity over the landscape makes it possible to robustly account for the role of multi-scale heterogeneity in the macroscale states and fluxes with around 300 sub-grid land model tiles; 2) assembling the sub-grid tiles from observed data, at times, leads to noticeable differences in the macroscale water, energy, and carbon cycles; for example, explicit subsurface interactions between the tiles leads to a dampening of macroscale extremes; 3) connecting the fine-scale grid to the model tiles via HMC enables circumventing the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth System models.
An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, “observations” drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the “identical” twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.
Ding, H, Matthew Newman, Michael A Alexander, and Andrew T Wittenberg, July 2018: Skillful climate forecasts of the tropical Indo-Pacific Ocean using model-analogs. Journal of Climate, 31(14), doi:10.1175/JCLI-D-17-0661.1. [ Abstract ]
Seasonal forecasts made by coupled general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME), by selecting from prior long control runs those model states whose monthly tropical IndoPacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1-12 months during 1982-2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multi-model ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical IndoPacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems.
The past few years have seen a growing investment in the development of global eddy‐resolving ocean models, but the impact of incorporating such high ocean resolution on precipitation responses to CO2 forcing has yet to be investigated. This study analyzes precipitation changes from a suite of GFDL models incorporating eddy‐resolving (0.1o), eddy‐permitting (0.25o) and eddy‐parameterizing (1o) ocean models. The incorporation of eddy resolution does not challenge the large‐scale structure of precipitation changes but results in substantial regional differences, particularly over ocean. These oceanic differences are primarily driven by the pattern of SST changes with greater sensitivity in lower latitudes. The largest impact of ocean resolution on SST changes occurs in eddy rich regions (e.g., boundary currents and the Southern Ocean), where impact on precipitation changes is also found to various degrees. In the Gulf Stream region where previous studies found considerable impact of eddy resolution on the simulation of climatological precipitation, we do not find such impact from the GFDL models but we do find substantial impact on precipitation changes. The eddy‐parameterizing model projects a banded structure common to the CMIP5 models, whereas the higher‐resolution models project a poleward shift of precipitation maxima associated with an enhanced Gulf Stream warming. Over land, precipitation changes are generally not very sensitive to ocean resolution. In eastern North America adjacent to the Gulf Stream region, moderate differences are found between resolutions. We discuss the mechanisms of land differences, which arise through the simulation of both climatological SST and SST changes.
The driving of tropical precipitation by variability of the underlying sea surface temperature (SST) plays a critical role in the atmospheric general circulation. To assess the precipitation sensitivity to SST variability, it is necessary to observe and understand the relationship between precipitation and SST. However, the precipitation – SST relationships from any coupled atmosphere-ocean system can be difficult to interpret due to the challenge of disentangling the SST-forced atmospheric response and the atmospheric intrinsic variability. This study demonstrates that the two components can be isolated using uncoupled atmosphere-only simulations, which extract the former when driven by time-varying SSTs and the latter when driven by climatological SSTs. With a simple framework that linearly combines the two types of uncoupled simulations, the coupled precipitation – SST relationships are successfully reproduced. Such a framework can be a useful tool for quantitatively diagnosing tropical air-sea interactions.
The precipitation sensitivity to SST variability is investigated with the use of uncoupled simulations with prescribed SST anomalies, where the influence of atmospheric intrinsic variability on SST is deactivated. Through a focus on local precipitation – SST relationships, the precipitation sensitivity to local SST variability is determined to be predominantly controlled by the local background SST. In addition, the strength of the precipitation response increases monotonically with the local background SST, with a very sharp growth at high SSTs. These findings are supported by basic principles of moist static stability, from which a simple formula for precipitation sensitivity to local SST variability is derived.
Kam, Jonghun, Thomas R Knutson, Fanrong Zeng, and Andrew T Wittenberg, January 2018: CMIP5 Model-Based Assessment of Anthropogenic Influence on Highly Anomalous Arctic Warmth During November-December 2016, [in “Explaining Extreme Events of 2016 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 99(1), doi:10.1175/BAMS-D-17-0116.1S34-S38.
Over regions where snow-melt runoff substantially contributes to winter-spring streamflows, warming can accelerate snow melt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, a detection/attribution of changes in mid-latitude North American winter-spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. In this study, robustness across models, start/end dates for trends, and assumptions about internal variability is evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central U.S., where winter-spring streamflows have been coming earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western U.S./southwestern Canada and in extreme northeastern U.S./Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow-free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.
Mountain snowpack in the western United States provides a natural reservoir for cold season precipitation; variations in snowpack influence warm season water supply, wildfire risk, ecology, and industries like agriculture dependent on snow and downstream water availability. Efforts to understand snowpack variability have predominantly been focused on either weekly (weather) or decadal to centennial (climate variability and change) timescales. We focus on a timescale between these ranges by demonstrating that a global climate model suite can provide snowpack predictions 8 months in advance. The predictions from climate models outperform statistical methods from observations alone. Our results show that seasonal hydroclimate predictions are possible and highlight areas for future prediction system improvements.
This study proposes a set of process-oriented diagnostics with the aim of understanding how model physics and numerics control the representation of tropical cyclones (TCs), especially their intensity distribution, in GCMs. Three simulations are made using two 50-km GCMs developed at NOAA’s Geophysical Fluid Dynamics Laboratory. The two models are forced with fixed sea surface temperature (AM2.5 and HiRAM), and in the third simulation the AM2.5 model is coupled to an ocean GCM (FLOR).
The frequency distributions of maximum surface wind near TC centers show that HiRAM tends to develop stronger TCs than the other models do. Large-scale environmental parameters, such as potential intensity, do not explain the differences between HiRAM and the other models. It is found that HiRAM produces a greater amount of precipitation near the TC center, suggesting that associated greater diabatic heating enables TCs to become stronger in HiRAM. HiRAM also shows a greater contrast in relative humidity and surface latent heat flux between the inner and outer regions of TCs.
Various fields are composited on precipitation percentiles to reveal the essential character of the interaction among convection, moisture, and surface heat flux. Results show that the moisture sensitivity of convection is higher in HiRAM than in the other model simulations. HiRAM also exhibits a stronger feedback from surface latent heat flux to convection via near-surface wind speed in heavy rain rate regimes. The results emphasize that the moisture-convection coupling and the surface heat flux feedback are critical processes that affect the intensity of TCs in GCMs.
Knutson, Thomas R., Jonghun Kam, Fanrong Zeng, and Andrew T Wittenberg, January 2018: CMIP5 Model-Based Assessment of Anthropogenic Influence on Record Global Warmth During 2016, [in “Explaining Extreme Events of 2016 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 99(1), doi:10.1175/BAMS-D-17-0104.1S11-S15.
Knutson, Thomas R., and Fanrong Zeng, June 2018: Model Assessment of Observed Precipitation Trends Over Land Regions: Detectable Human Influences and Possible Low Bias in Model Trends. Journal of Climate, 31(12), doi:10.1175/JCLI-D-17-0672.1. [ Abstract ]
Precipitation trends for 1901-2010, 1951-2010 and 1981- 2010 over relatively well-observed global land regions are assessed for detectable anthropogenic influences and for consistency with Coupled Model Intercomparison Project 5 (CMIP5) historical simulations. The CMIP5 historical All-Forcing runs are broadly consistent with the observed trend pattern (1901-2010), but with an apparent low trend bias tendency in the simulations. Despite this bias, observed and modeled trends are statistically consistent over 59% of the analyzed area. Over 20% (9%) of the analyzed area, increased (decreased) precipitation is partly attributable to anthropogenic forcing. These inferred human-induced changes include: increases over regions of the north-central U.S., southern Canada, Europe, and southern South America; and decreases over parts of the Mediterranean region and northern tropical Africa. Trends for the shorter periods (1951-2010 and 1981-2010) do not indicate a prominent low trend bias in the models, as found for the 1901-2010 trends. An atmosphere-only model, forced with observed sea surface temperatures and other climate forcing agents, also under-predicts the observed precipitation increase in the northern hemisphere extratropics since 1901. The CMIP5 All-Forcing ensemble’s low bias in simulated trends since 1901 is a tentative finding which, if borne out in further studies, suggests that precipitation projections using these regions/models could overestimate future drought risk, and underestimate future flooding risk.
Unprecedented high intensity flooding induced by extreme precipitation was reported over Chennai in India during November-December of 2015, which led to extensive damage to human life and property. It is of utmost importance to determine the odds of occurrence of such extreme floods in future and the related climate phenomena, for planning and mitigation purposes. Here, we make use of a suite of simulations from GFDL high-resolution coupled climate models to investigate the odds of occurrence of extreme floods induced by extreme precipitation over Chennai and the role of radiative forcing and/or large-scale SST forcing in enhancing the probability of such events in future. Climate of 20th century experiments with large ensembles suggest that the radiative forcing may not enhance the probability of extreme floods over Chennai. Doubling of CO2 experiments also fail to show evidence for increase of such events in a global warming scenario. Further, this study explores the role of SST forcing from the Indian and Pacific Oceans on the odds of occurrence of Chennai-like floods. Neither an El Niño nor La Niña enhances the probability of extreme floods over Chennai. However, warm Bay of Bengal tends to increase the odds of occurrence of extreme Chennai-like floods. The atmospheric condition such as a tropical depression over Bay of Bengal favoring the transport of moisture from warm Bay of Bengal is conducive for intense precipitation.
Statistical downscaling is used widely to refine projections of future climate. Although generally successful, in some circumstances it can lead to highly erroneous results.
Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, it aims to provide finer grain detail and also to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical time period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “Perfect Model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.
We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based only on available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products as well as the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced “Perfect Model” designs than the one we have employed.
Lee, Sang-Ki, Hosmay Lopez, E-S Chung, P DiNezio, S-W Yeh, and Andrew T Wittenberg, January 2018: On the fragile relationship between El Niño and California rainfall. Geophysical Research Letters, 45(2), doi:10.1002/2017GL076197. [ Abstract ]
The failed influence of the 2015-16 El Niño on California rainfall has renewed interest in the relationship between El Niño and US rainfall variability. Here, we perform statistical data analyses and simple model experiments to show that sufficiently warm and persistent sea surface temperature anomalies (SSTAs) in the far eastern equatorial Pacific are required to excite an anomalous cyclone in the North Pacific that extends to the east across the US west coast, and thus increases rainfall over California. Among the four most frequently recurring El Niño patterns considered in this study, only the persistent El Niño, which is often characterized by the warm SSTAs in the far eastern equatorial Pacific persisting throughout the winter and spring, is linked to such extratropical teleconnection patterns and significantly increased rainfall over the entire state of California. During the last 69 years, only three of the 25 El Niño events (i.e., 1957-58, 1982-83 and 1997-98) are clearly identified as the persistent El Niño. In addition, the monthly rainfall variance explained by El Niño is less than half that caused by internal variability during the 25 El Niño. Therefore, the rarity of persistent El Niño events combined with the large influence of internal variability effectively explains the fragile relationship between El Niño and California rainfall.
Li, Dawei, Rong Zhang, and Thomas R Knutson, February 2018: Comparison of Mechanisms for Low-Frequency Variability of Summer Arctic Sea Ice in Three Coupled Climate Models. Journal of Climate, 31(3), doi:10.1175/JCLI-D-16-0617.1. [ Abstract ]
In this study, the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled climate models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic/Pacific oceanic heat transport into the Arctic and the Arctic Dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decline and surface warming, especially over the Atlantic (Pacific) sector of the Arctic. A positive phase of the Arctic Dipole induces summer Arctic sea ice decline and surface warming on the Pacific side, and opposite changes on the Atlantic side. There is robust Bjerknes Compensation at low frequency, so that the northward atmospheric heat transport provides a negative feedback to summer Arctic sea ice variations. The influence of the Arctic Dipole on summer Arctic sea ice extent is more (less) effective in simulations with less (excessive) climatological summer sea ice in the Atlantic sector. The response of Arctic sea ice thickness (SIT) to the three key predictors is stronger in models that have thicker climatological Arctic sea ice.
Li, Shan, Shaoqing Zhang, Zhengyu Liu, Lv Lu, J Zhu, X-F Zhang, Xinrong Wu, Ming Zhao, and Gabriel A Vecchi, et al., April 2018: Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation. Journal of Advances in Modeling Earth Systems, 10(4), doi:10.1002/2017MS001222. [ Abstract ]
Parametric uncertainty in convection parameterization is one major source of model errors that cause model climate drift. Convection parameter tuning has been widely studied in atmospheric models to help mitigate the problem. However, in a fully coupled general circulation model (CGCM), convection parameters which impact the ocean as well as the climate simulation may have different optimal values. This study explores the possibility of estimating convection parameters with an ensemble coupled data assimilation method in a CGCM. Impacts of the convection parameter estimation on climate analysis and forecast are analyzed. In a twin experiment framework, five convection parameters in the GFDL coupled model CM2.1 are estimated individually and simultaneously under both perfect and imperfect model regimes. Results show that the ensemble data assimilation method can help reduce the bias in convection parameters. With estimated convection parameters, the analyses and forecasts for both the atmosphere and the ocean are generally improved. It is also found that information in low latitudes is relatively more important for estimating convection parameters. This study further suggests that when important parameters in appropriate physical parameterizations are identified, incorporating their estimation into traditional ensemble data assimilation procedure could improve the final analysis and climate prediction.
Liu, Maofeng, Gabriel A Vecchi, James A Smith, and Hiroyuki Murakami, September 2018: Projection of Landfalling-Tropical Cyclone Rainfall in the Eastern United States under Anthropogenic Warming. Journal of Climate, 31(8), doi:10.1175/JCLI-D-17-0747.1. [ Abstract ]
Landfalling tropical cyclone (TC) rainfall is an important element of inland flood hazards in the eastern United States. The projection of landfalling TC rainfall under anthropogenic warming provides insight to future flood risks. This study examines the frequency of landfalling TCs and associated rainfall using the GFDL Forecast-oriented Low Ocean Resolution (FLOR) climate model through comparisons with observed TC track and rainfall over the July–November 1979–2005 seasons. The projection of landfalling TC frequency and rainfall under the representative concentration pathway (RCP) 4.5 scenario for the late twenty-first century is explored, including an assessment of the impacts of extratropical transition (ET). In most regions of the southeastern United States, competition between increased storm rain rate and decreased storm frequency dominates the change of annual TC rainfall, and rainfall from ET and non-ET storms. In the northeastern United States, a prominent feature is the striking increase of ET storm frequency but with tropical characteristics (i.e., prior to the ET phase), a key element of increased rainfall. The storm-centered rainfall composite analyses show the greatest increase at radius a few hundred kilometers from the storm centers. Over both ocean and land, the increase of rainfall within 500 km from the storm center exceeds the Clausius-Clapeyron scaling for TC-phase storms. Similar results are found in the front-left quadrant of ET-phase storms. Future work involving explorations of multiple models (e.g., higher atmospheric resolution version of FLOR) for TC rainfall projection is expected to add more robustness to projection results.
Extratropical transition can extend the threat of tropical cyclones into the mid‐latitudes, and modify it through expansion of rainfall and wind fields. Despite the scientific and socioeconomic interest, the seasonal forecast of extratropical transition has received little attention. The GFDL High‐Resolution Forecast‐Oriented Low Ocean Resolution (FLOR) model (HiFLOR) shows skill in seasonal forecasts of tropical cyclone frequency as well as major hurricanes. A July‐initialized twelve‐member ensemble retrospective seasonal forecast experiment with HiFLOR in the North Atlantic is conducted, representing one of the very first attempts to predict the extratropical transition activity months in advance. HiFLOR exhibits retrospective skill in seasonal forecasts of basin‐wide and regional ET activity relative to best track and reanalysis data. In contrast, the skill of HiFLOR in predictions of non‐ET activity is limited. Future work targeted at improved predictions of non‐ET storms provides a path for enhanced TC activity forecasting.
Luitel, B, Gabriele Villarini, and Gabriel A Vecchi, January 2018: Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones. Journal of Hydrology, 556, doi:10.1016/j.jhydrol.2016.09.019. [ Abstract ]
The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007–2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from “observational data.” The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.
Although interannual streamflow variability is primarily a result of precipitation variability, temperature also plays a role. The relative weakness of the temperature effect at the annual time scale hinders understanding, but may belie substantial importance on climatic time scales. Here we develop and evaluate a simple theory relating variations of streamflow and evapotranspiration (E) to those of precipitation (P) and temperature. The theory is based on extensions of the Budyko water‐balance hypothesis, the Priestley‐Taylor theory for potential evapotranspiration (Ep), and a linear model of interannual basin storage. The theory implies that the temperature affects streamflow by modifying evapotranspiration through a Clausius‐Clapeyron‐like relation and through the sensitivity of net radiation to temperature. We apply and test (1) a previously introduced “strong” extension of the Budyko hypothesis, which requires that the function linking temporal variations of the evapotranspiration ratio (E/P) and the index of dryness (Ep/P) at an annual time scale is identical to that linking inter‐basin variations of the corresponding long‐term means, and (2) a “weak” extension, which requires only that the annual evapotranspiration ratio depends uniquely on the annual index of dryness, and that the form of that dependence need not be known a priori nor be identical across basins. In application of the weak extension, the readily observed sensitivity of streamflow to precipitation contains crucial information about the sensitivity to potential evapotranspiration and, thence, to temperature. Implementation of the strong extension is problematic, whereas the weak extension appears to capture essential controls of the temperature effect efficiently.
Human communities, economies, and natural ecosystems require a reliable supply of water, and this often is provided by rivers. It has at times been noted that rivers deliver less water in warm years than in cool years, even after adjustment for variations in precipitation. This dependence on temperature raises concerns about the effect of heat waves or climatic warming on water supply. Just why and how much the flow of rivers depends on temperature has not been well understood, but the answers to these questions are relevant for ensuring future water security. Here we present and evaluate a process‐based theory that attempts to answer both questions. In those river basins where long‐term observations of river flow, temperature, and precipitation data are available, the theory is consistent overall with observed sensitivities of river flows to temperature. This success implies potential applicability of the theory also where such observations are not available.
Muhling, Barbara A., Carlos F Gaitán, Charles A Stock, Vincent S Saba, Desiree Tommasi, and Keith W Dixon, March 2018: Potential Salinity and Temperature Futures for the Chesapeake Bay Using a Statistical Downscaling Spatial Disaggregation Framework. Estuaries and Coasts, 41(2), doi:10.1007/s12237-017-0280-8. [ Abstract ]
Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO2 emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries.
We explore factors potentially linked to the enhanced major hurricane activity in the Atlantic during 2017. Using a suite of high-resolution model experiments, we show that the increase in 2017 major hurricanes was not primarily caused by La Niña conditions in the Pacific Ocean, but mainly by pronounced warm sea surface conditions in the tropical North Atlantic. It is further shown that, in the future, a similar pattern of North Atlantic surface warming, superimposed upon long-term increasing sea surface temperature from increases in greenhouse gas concentrations and decreases in aerosols, will likely lead to even higher numbers of major hurricanes. The key factor controlling Atlantic major hurricane activity appears to be how much the tropical Atlantic warms relative to the rest of the global ocean.
Newman, Matthew, and Andrew T Wittenberg, et al., January 2018: The Extreme 2015/16 El Nino, In the Context of Historical Climate Variability and Change, [in “Explaining Extreme Events of 2016 from a Climate Perspective]. Bulletin of the American Meteorological Society, 99(1), doi:10.1175/BAMS-D-17-0116.1S16-S20.
Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled-climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model-data weights that removed spurious biogeochemical signals while benefitting from off-equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.
Widespread multiday convective bursts in the southwestern United States during the North American monsoon are often triggered by Gulf of California moisture surges (GoC surges). However, how GoC surges, and the amount and intensity of associated precipitation, will change in response to CO2-induced warming remains little known, not least because the most widely available climate models do not currently resolve the relevant mesoscale dynamics due to their coarse resolution (100 km or more). In this study, a 50-km resolution global coupled model (FLOR) is used to address this question. It is found that the mean number of GoC surge events remains unchanged under CO2 doubling, but intermediate-to-high intensity surge-related precipitation tends to become less frequent, thus reducing the mean summertime rainfall. Lowlevel moisture fluxes associated with GoC surges as well as their convergence over land to the east of the GoC intensify, but the increases in low-level moisture are not matched by the larger increments in the near-surface saturation specific humidity due to amplified land warming. This results in a more unsaturated, low-level atmospheric environment which disfavors moist convection. These thermodynamic changes are accompanied by dynamics changes that are also less conducive to convective activity, with the mid-level monsoonal ridge projected to expand and move to the west of its present-day climatological maximum. Despite the overall reduction in precipitation, the frequency of very intense, localized daily surge-related precipitation in Arizona and surrounding areas is projected to increase, consistently with increased precipitable water.
The Pacific equatorial cold tongue plays a leading role in Earth’s strongest and most predictable climate signals. To illuminate the processes governing cold tongue temperatures, the upper-ocean heat budget is explored using the GFDL FLOR coupled GCM. Starting from the exact temperature budget for layers of time-varying thickness, the layer temperature tendency terms are studied using hourly-, daily-, and monthly-mean output from a 30-year simulation driven by present-day radiative forcings. The budget is then applied to (1) a surface mixed layer whose temperature is highly correlated with SST, in which the air-sea heat flux is balanced mainly by downward diffusion of heat across the layer base; and (2) a thicker advective layer that subsumes most of the vertical mixing, in which the air-sea heat flux is balanced mainly by monthly-scale advection. The surface warming from shortwave fluxes and submonthly meridional advection, and the subsurface cooling from monthly vertical advection, are both shown to be essential to maintain the cold tongue thermal stratification against the destratifying effects of vertical mixing. Although layer undulations strongly mediate the tendency terms on diurnal-to-interannual scales, the 30-year-mean tendencies are found to be well summarized by analogous budgets developed for stationary but spatially-varying layers. The results are used to derive practical simplifications of the exact budget, to support the analyses in Part II of this paper and to facilitate broader application of heat budget analyses when evaluating and comparing climate simulations.
The heat budget of the Pacific equatorial cold tongue (ECT) is explored using the GFDL FLOR coupled GCM and ocean reanalyses, leveraging the two-layer framework developed in Part I. Despite FLOR’s relatively weak meridional stirring by tropical instability waves (TIWs), the model maintains a reasonable SST and thermocline depth in the ECT via two compensating biases: (1) enhanced monthly-scale vertical advective cooling below the surface mixed layer (SML), due to overly cyclonic off-equatorial wind stress which acts to cool the equatorial source waters; and (2) an excessive SST contrast between the ECT and off-equator, which boosts the equatorward heat transport by TIWs. FLOR’s strong advective cooling at the SML base is compensated by strong downward diffusion of heat out of the SML, which then allows FLOR’s ECT to take up a realistic heat flux from the atmosphere. Correcting FLOR’s climatological SST and wind stress biases via flux adjustment (FA) leads to weaker deep advective cooling of the ECT, which then erodes the upper-ocean thermal stratification, enhances vertical mixing, and excessively deepens the thermocline. FA does strengthen FLOR’s meridional shear of the zonal currents in the east Pacific, but this does not amplify the simulated TIWs nor their equatorward heat transport, likely due to FLOR’s coarse zonal ocean resolution. The analysis suggests that to advance coupled simulations of the ECT, improved winds and surface heat fluxes must go hand in hand with improved subseasonal and parameterized ocean processes. Implications for model development and the tropical Pacific observing system are discussed.
Ruprich-Robert, Yohan, Thomas L Delworth, and Rym Msadek, et al., May 2018: Impacts of the Atlantic Multidecadal Variability on North American Summer Climate and Heat Waves. Journal of Climate, 31(9), doi:10.1175/JCLI-D-17-0270.1. [ Abstract ]
The impacts of the Atlantic Multidecadal Variability (AMV) on summertime North American climate are investigated using three Coupled Global Climate Models (CGCMs) in which North Atlantic sea surface temperatures (SSTs) are restored to observed AMV anomalies. Large ensemble simulations are performed to estimate how AMV can modulate the occurrence of extreme weather like heat waves. We show that, in response to an AMV warming, all models simulate a precipitation deficit and a warming over northern Mexico and southern US that lead to an increased number of heat wave days by about 30% compared to an AMV cooling. The physical mechanisms associated with these impacts are discussed. The positive tropical Atlantic SST anomalies associated with the warm AMV drive a Matsuno-Gill-like atmospheric response that favors subsidence over northern Mexico and southern US. This leads to a warming of the whole tropospheric column, and to a decrease in relative humidity, cloud cover, and precipitation. Soil moisture response to AMV also plays a role in the modulation of heat wave occurrence. An AMV warming favors dry soil conditions over northern Mexico and southern US by driving year-round precipitation deficit through atmospheric teleconnections coming both directly from the North Atlantic SST forcing and indirectly from the Pacific. The indirect AMV teleconnections highlight the importance of using CGCMs to fully assess the AMV impacts on North America. Given the potential predictability of the AMV, the teleconnections discussed here suggest a source of predictability for the North American climate variability and in particular for the occurrence of heat waves at multi-year timescales.
Santanello, J A., Paul A Dirmeyer, Craig Ferguson, and Kirsten L Findell, et al., June 2018: Land-Atmosphere Interactions: The LoCo Perspective. Bulletin of the American Meteorological Society, 99(6), doi:10.1175/BAMS-D-17-0001.1. [ Abstract ]
Metrics derived by the LoCo working group have matured and begun to enter the mainstream, signaling the success of the GEWEX approach to foster grassroots participation. In this article, LoCo’s researchers discuss past, present and planned efforts.
Land-atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land-Atmosphere System Study (GLASS) panel has supported ‘L-A coupling’ as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hotspots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local L-A Coupling (‘LoCo’) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales, and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges.
Schenkel, Benjamin A., Ning Lin, Daniel Chavas, and Gabriel A Vecchi, et al., October 2018: Lifetime Evolution of Outer Tropical Cyclone Size and Structure as Diagnosed from Reanalysis and Climate Model Data. Journal of Climate, 31(19), doi:10.1175/JCLI-D-17-0630.1. [ Abstract ]
The present study examines the lifetime evolution of outer tropical cyclone (TC) size and structure in the North Atlantic (NA) and western North Pacific (WNP). The metric for outer TC size is the radius at which the azimuthal-mean 10-m azimuthal wind equals 8 m s−1 (r8) derived from the NCEP Climate Forecast System Reanalysis (CFSR) and GFDL High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR). Radial profiles of the azimuthal-mean 10-m azimuthal wind are also analyzed to demonstrate that the results are robust across a broad range of wind radii. The analysis shows that most TCs in both basins are characterized by: 1) minimum lifetime r8 at genesis, 2) subsequent substantial increases in r8 as the TC wind field expands, 3) peak r8 values occurring near or after the midpoint of TC lifetime, and 4) nontrivial decreases in r8 and outer winds during the latter part of TC lifetime. Compared to the NA, WNP TCs are systematically larger up until the end of their lifetime, exhibit r8 growth and decay rates that are larger in magnitude, and are characterized by an earlier onset of lifetime maximum r8 near their lifetime midpoint. In both basins, the TCs exhibiting the largest r8 increases are the longest-lived, especially those that traverse the longest distances (i.e., recurving TCs). Finally, analysis of TCs undergoing extratropical transition (ET) shows that NA TCs exhibit negligible changes in r8 during ET, while WNP ET cases either show r8 decreases (CFSR) or negligible changes in r8 (HiFLOR).
Smith, D M., Adam A Scaife, E Hawkins, Roberto Bilbao, G J Boer, M Caian, Louis-Philippe Caron, Gokhan Danabasoglu, Thomas L Delworth, Francisco J Doblas-Reyes, R Doescher, Nick Dunstone, Rosie Eade, Leon Hermanson, Masao Ishii, Viatcheslav Kharin, M Kimoto, Torben Koenigk, Y Kushnir, D Matei, Gerald A Meehl, Martin Ménégoz, William J Merryfield, T Mochizuki, Wolfgang A Müller, Holger Pohlmann, Scott B Power, M Rixen, Reinel Sospedra-Alfonso, M Tuma, K Wyser, Xiaosong Yang, and Stephen G Yeager, November 2018: Predicted chance that global warming will temporarily exceed 1.5°C. Geophysical Research Letters, 45(21), doi:10.1029/2018GL079362. [ Abstract ]
The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5oC above pre‐industrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5oC. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5oC above pre‐industrial levels in the coming five‐years. For the period 2017 to 2021 we predict a 38% and 10% chance respectively of monthly or yearly temperatures exceeding 1.5oC, with virtually no chance of the five‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events.
Strong, Jeffrey D., Gabriel A Vecchi, and Paul Ginoux, May 2018: The Climatological Effect of Saharan Dust on Global Tropical Cyclones in a Fully Coupled GCM. Journal of Geophysical Research: Atmospheres, 123(10), doi:10.1029/2017JD027808. [ Abstract ]
Climate in the tropical North Atlantic and West Africa is known to be sensitive to both the atmospheric burden and optical properties of aerosolized mineral dust. We investigate the global climatic response to an idealized perturbation in atmospheric burden of Saharan‐born mineral dust, comparable to the observed changes between the 1960's and 1980's, using simulations with the high resolution, fully coupled GFDL Climate Model 2.5, Forecast‐oriented Low Ocean Resolution version, across a range of realistic optical properties, with a specific focus on tropical cyclones. The direct radiative response at the top of the atmosphere (ToA) and at the surface along with regional hydrologic and thermodynamic responses are in agreement with previous studies, depending largely on the amount of aerosol absorption versus scattering. In all simulations, dust causes a decrease in tropical cyclone activity across the North Atlantic Ocean, as determined by a tropical cyclone tracking scheme, with the largest response occurring in the most absorbing and scattering optical regimes. These changes are partially corroborated by common local genesis potential indices. However, no clear‐cut explanation can be developed upon inspection of their constituent variables. There are also non‐negligible anomalies in the North Pacific and Indian Oceans in these simulations. A relationship between accumulated cyclone energy and ToA radiative flux anomalies is used to explain the North Atlantic anomalies, while analogy to known climate variations can help us understand the far‐field response to the dust forcing.
Timmermann, Axel, S I An, Jong-Seong Kug, Fei-Fei Jin, Wenju Cai, Antonietta Capotondi, K M Cobb, Matthieu Lengaigne, Michael J McPhaden, Malte F Stuecker, K Stein, and Andrew T Wittenberg, et al., July 2018: El Niño–Southern Oscillation complexity. Nature, 559(7715), doi:10.1038/s41586-018-0252-6. [ Abstract ]
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.
Floods in the Mississippi basin can have large negative societal, natural and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-year return Lower-Mississippi River floods using a global coupled climate model with an integrated surface-water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land-surface data, transient climate). In the months preceding the model’s 100-year floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the build up of snowpack, making the river system prone to these major flooding events. The melt water from snowpack in the northern Missouri and Upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient-forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-year floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully-coupled land-atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.
The Geophysical Fluid Dynamics Laboratory (GFDL) has recently developed two global coupled GCMs, FLOR and HiFLOR, which are now being utilized for climate research and seasonal predictions. Compared to their predecessor CM2.1, the new versions have improved ocean/atmosphere physics and numerics, and refinement of the atmospheric horizontal grid from 220 km (CM2.1) to 55 km (FLOR) and 26 km (HiFLOR). Both FLOR and HiFLOR demonstrate greatly improved simulations of the tropical Pacific annual‐mean climatology, with FLOR practically eliminating any equatorial cold bias in sea surface temperature. An additional model experiment (LOAR1) using FLOR's ocean/atmosphere physics, but with the atmospheric grid coarsened toward that of CM2.1, is used to further isolate the impacts of the refined atmospheric grid versus the improved physics and numerics. The improved ocean/atmosphere formulations are found to produce more realistic tropical Pacific patterns of sea surface temperature and rainfall, surface heat fluxes, ocean mixed layer depths, surface currents, and tropical instability wave (TIW) activity; enhance the near‐surface equatorial upwelling; and reduce the inter‐centennial warm drift of the tropical Pacific upper ocean. The atmospheric grid refinement further improves these features, and also improves the tropical Pacific surface wind stress, implied Ekman and Sverdrup transports, subsurface temperature and salinity structure, and heat advection in the equatorial upper ocean. The results highlight the importance of nonlocal air‐sea interactions in the tropical Pacific climate system, including the influence of off‐equatorial surface fluxes on the equatorial annual‐mean state. Implications are discussed for improving future simulations, observations, and predictions of tropical Pacific climate.
The Atlantic Meridional Overturning Circulation (AMOC) has profound impacts on various climate phenomena. Using both observations and simulations from the Coupled Model Intercomparison Project Phase 3 and 5 (CMIP3 and CMIP5), here we show that most models underestimate the amplitude of low‐frequency (decadal) AMOC variability. We further show that stronger low‐frequency AMOC variability leads to stronger linkages between the AMOC and key variables associated with the Atlantic multidecadal variability (AMV), and between the subpolar AMV signal and northern hemisphere surface air temperature (NHSAT). Low‐frequency extra‐tropical NHSAT variability might increase with the amplitude of low‐frequency AMOC variability. Atlantic decadal predictability is much higher in models with stronger low‐frequency AMOC variability, and much lower in models with weaker or without AMOC variability. Our results suggest that simulating realistic low‐frequency AMOC variability is very important, both for simulating realistic linkages between AMOC and AMV‐related variables and for achieving substantially higher Atlantic decadal predictability.
A “typical” El Niño leads to wet (dry) wintertime anomalies over the southern (northern) half of the Western United States (WUS). However, during the strong El Niño of 2015/16, the WUS winter precipitation pattern was roughly opposite to this canonical (average of the record) anomaly pattern. To understand why this happened, and whether it was predictable, we use a suite of high-resolution seasonal prediction experiments with coupled climate models. We find that the unusual 2015/16 precipitation pattern was predictable at zero-lead time horizon when the ocean/atmosphere/land components were initialized with observations. However, when the ocean alone is initialized the coupled model fails to predict the 2015/16 pattern, although ocean initial conditions alone can reproduce the observed WUS precipitation during the 1997/98 strong El Niño. Further observational analysis shows that the amplitudes of the El Niño induced tropical circulation anomalies during 2015/16 were weakened by about 50% relative to those of 1997/98. This was caused by relative cold (warm) anomalies in the eastern (western) tropical Pacific suppressing (enhancing) deep convection anomalies in the eastern (western) tropical Pacific during 2015/16. The reduced El Niño teleconnection led to a weakening of the subtropical westerly jet over the southeast North Pacific and southern WUS, resulting in the unusual 2015/16 winter precipitation pattern over the WUS. This study highlights the importance of initial conditions not only in the ocean, but in the land and atmosphere as well, for predicting the unusual El Niño teleconnection and its influence on the winter WUS precipitation anomalies during 2015/16.
This study examines the impacts of the Pacific Meridional Mode (PMM) on North Atlantic tropical cyclones (TCs) making landfall along the coastal US, Caribbean Islands and Mexico, and provides insights on the underlying physical mechanisms using observations and model simulations. There is a statistically significant time-lagged association between spring PMM and the August–October US and Caribbean landfalling TCs. Specifically, the positive (negative) spring PMM events tend to be followed by fewer (more) TCs affecting the coastal US (especially over the Gulf of Mexico and Florida) and the Caribbean Islands. This lagged association is mainly caused by the lagged impacts of PMM on the El Niño Southern Oscillation (ENSO), and the subsequent impacts of ENSO on TC frequency and landfalls. Positive (negative) PMM events are largely followed by El Niño (La Niña) events, which lead to less (more) TC geneses close to the US coast (i.e., the Gulf of Mexico and the Caribbean Sea); this also leads to easterly (westerly) steering flow in the vicinity of the US and Caribbean coast, which is unfavorable (favorable) to TC landfall across the Gulf of Mexico, Florida and Caribbean Islands. Perturbation simulations with the state-of-the-art Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) support the linkage between PMM and TC landfall activity. The time-lagged impacts of spring PMM on TC landfalling activity results in a new predictor to forecast seasonal TC landfall activity along the US (especially over the Gulf of Mexico and Florida) and Caribbean coastal regions.
Over the 1997-2014 period, the mean frequency of western North Pacific (WNP) tropical cyclones (TCs) was markedly lower (~18%) than the period 1980-1996. Here we show that these changes were driven by an intensification of the vertical wind shear in the southeastern/eastern WNP tied to the changes in the Walker circulation, which arose primarily in response to the enhanced sea surface temperature (SST) warming in the North Atlantic, while the SST anomalies associated with the negative phase of the Pacific Decadal Oscillation (PDO) in the tropical Pacific and the anthropogenic forcing play only secondary roles. These results are based on observations and experiments using the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low-ocean Resolution Coupled Climate Model (FLOR) coupled climate model. The present study suggests a crucial role of the North Atlantic SST in causing decadal changes to WNP TC frequency.
Zhang, Honghai, and Thomas L Delworth, April 2018: Detectability of Decadal Anthropogenic Hydroclimate Changes over North America. Journal of Climate, 31(7), doi:10.1175/JCLI-D-17-0366.1. [ Abstract ]
Regional hydroclimate changes on decadal time scales contain substantial natural variability. This presents a challenge for the detection of anthropogenically forced hydroclimate changes on these spatiotemporal scales, because the “signal” of anthropogenic changes is modest compared to the “noise” of natural variability. However, previous studies have shown that this “signal to noise” ratio can be greatly improved in a large model ensemble where each member contains the same “signal” but different “noise”. Here using multiple state-of-the-art large ensembles from two climate models, we quantitatively assess the detectability of anthropogenically caused decadal shifts in precipitation-minus-evaporation (PmE) mean state against natural variability, focusing on North America during 2000-2050.
Anthropogenic forcing is projected to cause detectable (“signal” larger than “noise”) shifts in PmE mean state relative to the 1950-1999 climatology over 50-70% of North America by 2050. The earliest detectable signals include, during November-April, a moistening over northeastern North America and a drying over southwestern North America and, during May- October, a drying over central North America. Different processes are responsible for these signals. Changes in submonthly transient eddy moisture fluxes account for the northeastern moistening and central drying while monthly atmospheric circulation changes explain the southwestern drying. Our model findings suggest that, despite the dominant role of natural internal variability on decadal time scales, anthropogenic shifts in PmE mean state can be detected over most of North America before the middle of the current century.
Zhang, Honghai, and Thomas L Delworth, March 2018: Robustness of anthropogenically forced decadal precipitation changes projected for the 21st century. Nature Communications, 9, 1150, doi:10.1038/s41467-018-03611-3. [ Abstract ]
Precipitation is characterized by substantial natural variability, including on regional and decadal scales. This relatively large variability poses a grand challenge in assessing the significance of anthropogenically forced precipitation changes. Here we use multiple large ensembles of climate change experiments to evaluate whether, on regional scales, anthropogenic changes in decadal precipitation mean state are distinguishable. Here, distinguishable means the anthropogenic change is outside the range expected from natural variability. Relative to the 1950–1999 period, simulated anthropogenic shifts in precipitation mean state for the 2000–2009 period are already distinguishable over 36–41% of the globe—primarily in high latitudes, eastern subtropical oceans, and the tropics. Anthropogenic forcing in future medium-to-high emission scenarios is projected to cause distinguishable shifts over 68–75% of the globe by 2050 and 86–88% by 2100. Our findings imply anthropogenic shifts in decadal-mean precipitation will exceed the bounds of natural variability over most of the planet within several decades.
In this two-part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part I, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode – with prescribed sea surface temperatures (SSTs) and sea ice distribution – is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top-of-atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part II, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.
In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.
Atwood, A R., David S Battisti, and Andrew T Wittenberg, et al., October 2017: Characterizing unforced multi-decadal variability of ENSO: a case study with the GFDL CM2.1 coupled GCM. Climate Dynamics, 49(7-8), doi:10.1007/s00382-016-3477-9. [ Abstract ]
Large multi-decadal fluctuations of El Niño-Southern Oscillation (ENSO) variability simulated in a 4000-year pre-industrial control run of GFDL CM2.1 have received considerable attention due to implications for constraining the causes of past and future changes in ENSO. We evaluated the mechanisms of this low-frequency ENSO modulation through analysis of the extreme epochs of CM2.1 as well as through the use of a linearized intermediate-complexity model of the tropical Pacific, which produces reasonable emulations of observed ENSO variability. We demonstrate that the low-frequency ENSO modulation can be represented by the simplest model of a linear, stationary process, even in the highly nonlinear CM2.1. These results indicate that CM2.1’s ENSO modulation is driven by transient processes that operate at interannual or shorter time scales. Nonlinearities and/or multiplicative noise in CM2.1 likely exaggerate the ENSO modulation by contributing to the overly active ENSO variability. In contrast, simulations with the linear model suggest that intrinsically-generated tropical Pacific decadal mean state changes do not contribute to the extreme-ENSO epochs in CM2.1. Rather, these decadal mean state changes actually serve to damp the intrinsically-generated ENSO modulation, primarily by stabilizing the ENSO mode during strong-ENSO epochs. Like most coupled General Circulation Models, CM2.1 suffers from large biases in its ENSO simulation, including ENSO variance that is nearly twice that seen in the last 50 years of observations. We find that CM2.1’s overly strong ENSO variance directly contributes to its strong multi-decadal modulation through broadening the distribution of epochal variance, which increases like the square of the long-term variance. These results suggest that the true spectrum of unforced ENSO modulation is likely substantially narrower than that in CM2.1. However, relative changes in ENSO modulation are similar between CM2.1, the linear model tuned to CM2.1, and the linear model tuned to observations, underscoring previous findings that relative changes in ENSO variance can robustly be compared across models and observations.
Barcikowska, Monika, Thomas R Knutson, and Rong Zhang, January 2017: Observed and simulated fingerprints of multidecadal climate variability, and their contributions to periods of global SST stagnation. Journal of Climate, 30(2), doi:10.1175/JCLI-D-16-0443.1. [ Abstract ]
This study investigates spatio-temporal features of multidecadal climate variability, using observations and climate model simulation. Aside from a long-term warming trend, observational SST and atmospheric circulation records are dominated by a ~65yr variability component. Though its center of action is over the North Atlantic, but it manifests also over the Pacific and Indian Oceans, suggesting a tropical inter-basin teleconnection maintained through an atmospheric bridge.
Our analysis shows that simulated internal climate variability in a coupled climate model (CSIRO-Mk3.6.0) reproduces the main spatio-temporal features of the observed component. Model-based multidecadal variability comprises a coupled ocean-atmosphere teleconnection, established through a zonally oriented atmospheric overturning circulation between the tropical North Atlantic and eastern tropical Pacific. During the warm SST phase in the North Atlantic, increasing SSTs over the tropical North Atlantic strengthen locally ascending air motion and intensify subsidence and low-level divergence in the eastern tropical Pacific. This corresponds with a strengthening of trade winds and cooling in the tropical central Pacific.
The model’s derived component substantially shapes its global climate variability and is tightly linked to multidecadal variability of the Atlantic Meridional Overturning Circulation (AMOC). This suggests potential predictive utility and underscores the importance of correctly representing North Atlantic variability in simulations of global and regional climate.
If the observations-based component of variability originates from internal climate processes, as found in the model, the recently observed (1970s-2000s) North Atlantic warming and eastern tropical Pacific cooling might presage an ongoing transition to a cold North Atlantic phase with possible implications for near-term global temperature evolution.
Berg, Alexis, Justin Sheffield, and P C D Milly, January 2017: Divergent surface and total soil moisture projections under global warming. Geophysical Research Letters, 44(1), doi:10.1002/2016GL071921. [ Abstract ]
Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.
Berg, Alexis, Benjamin R Lintner, Kirsten L Findell, and A Giannini, April 2017: Soil Moisture Influence on Seasonality and Large-Scale Circulation in Simulations of the West African Monsoon. Journal of Climate, 30(7), doi:10.1175/JCLI-D-15-0877.1. [ Abstract ]
Prior studies have highlighted West Africa as a regional hotspot of land–atmosphere coupling. This study focuses on the large-scale influence of soil moisture variability on the mean circulation and precipitation in the West African monsoon. A suite of six models from the Global Land–Atmosphere Coupling Experiment (GLACE)-CMIP5 is analyzed. In this experiment, model integrations were performed with soil moisture prescribed to a specified climatological seasonal cycle throughout the simulation, which severs the two-way coupling between soil moisture and the atmosphere. Comparison with the control (interactive soil moisture) simulations indicates that mean June–September monsoon precipitation is enhanced when soil moisture is prescribed. However, contrasting behavior is evident over the seasonal cycle of the monsoon, with core monsoon precipitation enhanced with prescribed soil moisture but early-season precipitation reduced, at least in some models. These impacts stem from the enhancement of evapotranspiration at the dry poleward edge of the monsoon throughout the monsoon season, when soil moisture interactivity is suppressed. The early-season decrease in rainfall with prescribed soil moisture is associated with a delayed poleward advancement of the monsoon, which reflects the relative cooling of the continent from enhanced evapotranspiration, and thus a reduced land–ocean thermal contrast, prior to monsoon onset. On the other hand, during the core/late monsoon season, surface evaporative cooling modifies meridional temperature gradients and, through these gradients, alters the large-scale circulation: the midlevel African easterly jet is displaced poleward while the low-level westerlies are enhanced; this enhances precipitation. These results highlight the remote impacts of soil moisture variability on atmospheric circulation and precipitation in West Africa.
Berg, Alexis, Benjamin R Lintner, Kirsten L Findell, and A Giannini, June 2017: Uncertain soil moisture feedbacks in model projections of Sahel precipitation. Geophysical Research Letters, 44(12), doi:10.1002/2017GL073851. [ Abstract ]
Given the uncertainties in climate model projections of Sahel precipitation, at the northern edge of the West African Monsoon, understanding the factors governing projected precipitation changes in this semi-arid region is crucial. This study investigates how long-term soil moisture changes projected under climate change may feedback on projected changes of Sahel rainfall, using simulations with and without soil moisture change from five climate models participating in the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment. In four out of five models analyzed, soil moisture feedbacks significantly influence the projected West African precipitation response to warming; however, the sign of these feedbacks differ across the models. These results demonstrate that reducing uncertainties across model projections of the WAM requires, among other factors, improved mechanistic understanding and constraint of simulated land-atmosphere feedbacks, even at the large spatial scales considered here.
Previous studies found large biases between individual observational and model estimates of historical ocean anthropogenic carbon uptake. We show that the largest bias between the Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble mean and between two observational estimates of ocean anthropogenic carbon is due to a difference in start date. After adjusting the CMIP5 and observational estimates to the 1791-1995 period, all three carbon uptake estimates agree to within 3 Pg of C, about 4% of the total. The CMIP5 ensemble mean spatial bias compared to the observations is generally smaller than the observational error, apart from a negative bias in the Southern Ocean, and a positive bias in the Southern Indian and Pacific Oceans compensating each other in the global mean. This dipole pattern is likely due to an equatorward and weak bias in the position of Southern Hemisphere westerlies and lack of mode and intermediate water ventilation.
Due to its persistence on seasonal timescales, Arctic sea-ice thickness (SIT) is a potential source of predictability for summer sea-ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea-ice volume anomalies is studied using a 700-year control integration and a suite of initialized ensemble forecasts from a fully-coupled global climate model. Our analysis is focused on the September sea-ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea-ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea-ice concentration field, melt-onset date, snow coverage, and ice-thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice-albedo feedback processes in seasonal forecast systems.
Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea-ice extent (SIE). In this work, we move towards stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981–2015 made with a coupled atmosphere-ocean-sea ice-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent, and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea-ice thickness initial conditions provide a crucial source of skill for regional summer SIE.
Chen, C, Mark Cane, Andrew T Wittenberg, and D Chen, January 2017: ENSO in the CMIP5 simulations: lifecycles, diversity, and responses to climate change. Journal of Climate, 30(2), doi:10.1175/JCLI-D-15-0901.1. [ Abstract ]
Focusing on ENSO seasonal phase locking, diversity in peak location and propagation direction, as well as the El Niño-La Niña asymmetry in amplitude, duration and transition, a set of empirical probabilistic diagnostics (EPD) is introduced to investigate how the ENSO behaviors reflected in SST may change in a warming climate.
EPD is first applied to estimate the natural variation of ENSO behaviors. In the observations El Niños and La Niñas mainly propagate westward and peak in boreal winter. El Niños occur more at the eastern Pacific while La Niñas prefer the central Pacific. In a pre-industrial control simulation of the GFDL CM2.1 model, the El Niño-La Niña asymmetry is substantial. La Niña characteristics generally agree with observations but El Niños do not, typically propagating eastward and showing no obvious seasonal phase locking. So an alternative approach is using a stochastically forced simulation of a nonlinear data-driven model, which exhibits reasonably realistic ENSO behaviors and natural variation ranges.
EPD is then applied to assess the potential changes of ENSO behaviors in the 21st century using CMIP5 models. Other than the increasing SST climatology, projected changes in many aspects of ENSO reflected in SST anomalies are heavily model-dependent and generally within the range of natural variation. Shifts favoring eastward propagating El Niño and La Niña are the most robust. Given various model biases for the 20th century and lack of sufficient model agreements for the 21st century projection, whether the projected changes for ENSO behaviors would actually take place remains largely uncertain.
The relationship between the North Atlantic Oscillation (NAO) and Atlantic sea surface temperature (SST) variability is investigated using models and observations. Coupled climate models are used in which the ocean component is either a fully dynamic ocean, or a slab ocean with no resolved ocean heat transport. On time scales less than ten years NAO variations drive a tripole pattern of SST anomalies in both observations and models. This SST pattern is a direct response of the ocean mixed layer to turbulent surface heat flux anomalies associated with the NAO.
On time scales longer than ten years a similar relationship exists between the NAO and the tripole pattern of SST anomalies in models with a slab ocean. A different relationship exists both for the observations and for models with a dynamic ocean. In these models a positive (negative) NAO anomaly leads, after a decadal-scale lag, to a monopole pattern of warming (cooling) that resembles the Atlantic Multidecadal Oscillation (AMO), although with smaller than observed amplitudes of tropical SST anomalies. Ocean dynamics are critical to this decadal scale response in the models. The simulated Atlantic Meridional Overturning Circulation (AMOC) strengthens (weakens) in response to a prolonged positive (negative) phase of the NAO, thereby enhancing (decreasing) poleward heat transport, leading to broad scale warming (cooling).
We use additional simulations in which heat flux anomalies derived from observed NAO variations from 1901 to 2014 are applied to the ocean component of coupled models. We show that ocean dynamics allow models to reproduce important aspects of the observed AMO, mainly in the subpolar gyre.
Dufour, Carolina O., Adele K Morrison, Stephen M Griffies, I Frenger, Hannah Zanowski, and Michael Winton, October 2017: Preconditioning of the Weddell Sea polynya by the ocean mesoscale and dense water overflows. Journal of Climate, 30(19), doi:10.1175/JCLI-D-16-0586.1. [ Abstract ]
TheWeddell Sea polynya is a large opening in the open-ocean sea ice cover associated with intense deep convection in the ocean. A necessary condition to form and maintain a polynya is the presence of a strong subsurface heat reservoir. This study investigates the processes that control the stratification and hence the build-up of the subsurface heat reservoir in theWeddell Sea. To do so, a climate model run for 200 years under preindustrial forcing with two eddying resolutions in the ocean (0.25° CM2.5 and 0.10° CM2.6) is investigated. Over the course of the simulation, CM2.6 develops two polynyas in the Weddell Sea, while CM2.5 exhibits quasi-continuous deep convection, but no polynyas, exemplifying that deep convection is not a sufficient condition for a polynya to occur. CM2.5 features a weaker subsurface heat reservoir than CM2.6 due to weak stratification associated with episodes of gravitational instability and enhanced vertical mixing of heat, resulting in an erosion of the reservoir. In contrast, in CM2.6, the water column is more stably stratified, allowing the subsurface heat reservoir to build up. The enhanced stratification in CM2.6 arises from its refined horizontal grid spacing and resolution of topography which allows, in particular, a better representation of the restratifying effect by transient mesoscale eddies and of the overflows of dense waters along the continental slope.
Easterling, David R., Kenneth E Kunkel, J R Arnold, and Thomas R Knutson, et al., November 2017: Precipitation change in the United States In Climate Science Special Report: Fourth National Climate Assessment, Volume I, Washington, DC, Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.), U.S. Global Change Research Program, Washington, DC, doi:10.7930/J0H993CC207-230.
Land surface processes modulate the severity of heat waves, droughts, and other extreme events. However, models show contrasting effects of land surface changes on extreme temperatures. Here, we use an earth system model from the Geophysical Fluid Dynamics Laboratory to investigate regional impacts of land use and land cover change on combined extremes of temperature and humidity, namely aridity and moist enthalpy, quantities central to human physiological experience of near-surface climate. The model’s near-surface temperature response to deforestation is consistent with recent observations, and conversion of mid-latitude natural forests to cropland and pastures is accompanied by an increase in the occurrence of hot-dry summers from once-in-a-decade to every 2–3 years. In the tropics,long time-scale oceanic variability precludes determination of how much of a small, but significant, increase in moist enthalpy throughout the year stems from the model’s novel representation of historical patterns of wood harvesting, shifting cultivation, and regrowth of secondary vegetation and how much is forced by internal variability within the tropical oceans.
Goddard, P, Carolina O Dufour, Jianjun Yin, Stephen M Griffies, and Michael Winton, October 2017: CO2-Induced Ocean Warming of the Antarctic Continental Shelf in an Eddying Global Climate Model. Journal of Geophysical Research: Oceans, 122(10), doi:10.1002/2017JC012849. [ Abstract ]
Ocean warming near the Antarctic ice shelves has critical implications for future ice sheet mass loss and global sea level rise. A global climate model with an eddying ocean is used to quantify the mechanisms contributing to ocean warming on the Antarctic continental shelf in an idealized 2xCO2 experiment. The results indicate that relatively large warm anomalies occur both in the upper 100 m and at depths above the shelf floor, which are controlled by different mechanisms. The near-surface ocean warming is primarily a response to enhanced onshore advective heat transport across the shelf break. The deep shelf warming is initiated by onshore intrusions of relatively warm Circumpolar Deep Water (CDW), in density classes that access the shelf, as well as the reduction of the vertical mixing of heat. CO2-induced shelf freshening influences both warming mechanisms. The shelf freshening slows vertical mixing by limiting gravitational instabilities and the upward diffusion of heat associated with CDW, resulting in the build-up of heat at depth. Meanwhile, freshening near the shelf break enhances the lateral density gradient of the Antarctic Slope Front (ASF) and disconnect isopycnals between the shelf and CDW, making cross-ASF heat exchange more difficult. However, at several locations along the ASF, the cross-ASF heat transport is less inhibited and heat can move onshore. Once onshore, lateral and vertical heat advection work to disperse the heat anomalies across the shelf region. Understanding the inhomogeneous Antarctic shelf warming will lead to better projections of future ice sheet mass loss.
Coupled general circulation models (CGCMs) simulate a diverse range of El Niño–Southern Oscillation behaviors. “Double peaked” El Niño events—where two separate centers of positive sea surface temperature (SST) anomalies evolve concurrently in the eastern and western equatorial Pacific—have been evidenced in Coupled Model Intercomparison Project version 5 CGCMs and are without precedent in observations. The characteristic CGCM double peaked El Niño may be mistaken for a central Pacific warming event in El Niño composites, shifted westwards due to the cold tongue bias. In results from the Australian Community Climate and Earth System Simulator coupled model, we find that the western Pacific warm peak of the double peaked El Niño event emerges due to an excessive westward extension of the climatological cold tongue, displacing the region of strong zonal SST gradients towards the west Pacific. A coincident westward shift in the zonal current anomalies reinforces the western peak in SST anomalies, leading to a zonal separation between the warming effect of zonal advection (in the west Pacific) and that of vertical advection (in the east Pacific). Meridional advection and net surface heat fluxes further drive growth of the western Pacific warm peak. Our results demonstrate that understanding historical CGCM El Niño behaviors is a necessary precursor to interpreting projections of future CGCM El Niño behaviors, such as changes in the frequency of eastern Pacific El Niño events, under global warming scenarios.
There is large uncertainty in the simulation of transient climate sensitivity. This study aims to understand how such uncertainty is related to the simulation of the base climate by comparing two simulations with the same model but in which CO2 is increased from either a preindustrial (1860) or a present-day (1990) control simulation. This allows different base climate ocean circulations that are representative of those in current climate models to be imposed upon a single model. As a result, the model projects different transient climate sensitivities that are comparable to the multimodel spread. The greater warming in the 1990-start run occurs primarily at high latitudes and particularly over regions of oceanic convection. In the 1990-start run, ocean overturning circulations are initially weaker and weaken less from CO2 forcing. As a consequence, there are smaller reductions in the poleward ocean heat transport, leading to less tropical ocean heat storage and less moderated high-latitude surface warming. This process is evident in both hemispheres, with changes in the Atlantic meridional overturning circulation and the Antarctic Bottom Water formation dominating the warming differences in each hemisphere. The high-latitude warming in the 1990-start run is enhanced through albedo and cloud feedbacks, resulting in a smaller ocean heat uptake efficacy. The results highlight the importance of improving the base climate ocean circulation in order to provide a reasonable starting point for assessments of past climate change and the projection of future climate change.
Jenni, K E., M B Goldhaber, J Betancourt, J S Baron, R S Bristol, Mary Cantrill, P E Exter, M J Focazio, J W Haines, L E Hay, Leslie Hsu, V F Labson, K D Lafferty, K A Ludwig, and P C D Milly, et al., June 2017: Grand challenges for integrated USGS science—A workshop report, Reston, VA: Open-File Report 2017-1076, U.S. Geological Survey, doi:10.3133/ofr2017107694pp.
This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extra-tropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extra-tropical near surface land temperature. We show that most of the lead-0 month spring predictive skill of land temperature over extra-tropics, particularly over northern Eurasia, stems from stratospheric initialization. We further reveal that this predictive skill of extra-tropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.
Karspeck, Alicia R., Detlef Stammer, Armin Köhl, Gokhan Danabasoglu, Magdalena Alonso Balmaseda, D M Smith, Yosuke Fujii, Shaoqing Zhang, B Giese, Hiroyuki Tsujino, and Anthony Rosati, August 2017: Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Climate Dynamics, 49(3), doi:10.1007/s00382-015-2787-7. [ Abstract ]
The mean and variability of the Atlantic meridional overturning circulation (AMOC), as represented in six ocean reanalysis products, are analyzed over the period 1960–2007. Particular focus is on multi-decadal trends and interannual variability at 26.5°N and 45°N. For four of the six reanalysis products, corresponding reference simulations obtained from the same models and forcing datasets but without the imposition of subsurface data constraints are included for comparison. An emphasis is placed on identifying general characteristics of the reanalysis representation of AMOC relative to their reference simulations without subsurface data constraints. The AMOC as simulated in these two sets are presented in the context of results from the Coordinated Ocean-ice Reference Experiments phase II (CORE-II) effort, wherein a common interannually varying atmospheric forcing data set was used to force a large and diverse set of global ocean-ice models. Relative to the reference simulations and CORE-II forced model simulations it is shown that (1) the reanalysis products tend to have greater AMOC mean strength and enhanced variance and (2) the reanalysis products are less consistent in their year-to-year AMOC changes. We also find that relative to the reference simulations (but not the CORE-II forced model simulations) the reanalysis products tend to have enhanced multi-decadal trends (from 1975–1995 to 1995–2007) in the mid to high latitudes of the northern hemisphere.
Khouakhi, A, Gabriele Villarini, and Gabriel A Vecchi, January 2017: Contribution of tropical cyclones to rainfall at the global scale. Journal of Climate, 30(1), doi:10.1175/JCLI-D-16-0298.1. [ Abstract ]
This study quantifies the relative contribution of tropical cyclones (TCs) to annual, seasonal and extreme rainfall, and examines the connection between El Niño–Southern Oscillation (ENSO) and the occurrence of extreme TC-induced rainfall across the globe. We use historical six-hour best track TC datasets and daily precipitation data from 18607 global rain gauges with at least 25 complete years of data between 1970 and 2014. The highest TC-induced rainfall totals occur in eastern Asia (>400 mm/year) and northeastern Australia (>200mm/year), followed by the southeastern United States and along the coast of the Gulf of Mexico (100 to 150 mm/year). Fractionally, TCs account for 35% to 50% of the mean annual rainfall in northwestern Australia, southeastern China, the northern Philippines and Baja California, Mexico. Seasonally, between 40% and 50% of TC-induced rain is recorded along the western coast of Australia and in islands of the south Indian Ocean in the austral summer, and in eastern Asia and Mexico in boreal summer and fall. In terms of extremes, using annual maximum and peak-over-threshold approaches, we find the highest proportions of TC-induced rainfall in East Asia, followed by Australia and North-Central America, with fractional contributions generally decreasing as one moves inland from the coast. The relationship between TC-induced extreme rainfall and ENSO reveals that TC-induced extreme rainfall tends to occur more frequently in Australia and along the U.S. East Coast during La Niña, and along eastern Asia and the northwestern Pacific islands during El Niño.
Knutson, Thomas R., James Kossin, C Mears, J Perlwitz, and Michael F Wehner, November 2017: Detection and attribution of climate change In Climate Science Special Report: Fourth National Climate Assessment, Volume I, Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.), Washin, U.S. Global Change Research Program, doi:10.7930/J01834ND114-132.
Kossin, James, T Hall, and Thomas R Knutson, et al., November 2017: Extreme storms In Climate Science Special Report: Fourth National Climate Assessment, Volume I, Washington, DC, Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.), U.S. Global Change Research Program, Washington, DC, doi:10.7930/J07S7KXX257-276.
L'Heureux, Michelle L., Ken Takahashi, A B Watkins, A Barnston, E Becker, T E Di Liberto, F Gamble, J Gottschalck, M S Halpert, B Huang, K Mosquera-Vásquez, and Andrew T Wittenberg, July 2017: Observing and Predicting the 2015/16 El Niño. Bulletin of the American Meteorological Society, 98(7), doi:10.1175/BAMS-D-16-0009.1. [ Abstract ]
The El Niño of 2015/16 was among the strongest El Niño events observed since 1950 and took place almost two decades after the previous major event in 1997/98. Here, perspectives of the event are shared by scientists from three national meteorological or climate services that issue regular operational updates on the status and prediction of El Niño–Southern Oscillation (ENSO). Public advisories on the unfolding El Niño were issued in the first half of 2015. This was followed by significant growth in sea surface temperature (SST) anomalies, a peak during November 2015–January 2016, subsequent decay, and its demise during May 2016. The life cycle and magnitude of the 2015/16 El Niño was well predicted by most models used by national meteorological services, in contrast to the generally overexuberant model predictions made the previous year. The evolution of multiple atmospheric and oceanic measures demonstrates the rich complexity of ENSO, as a coupled ocean–atmosphere phenomenon with pronounced global impacts. While some aspects of the 2015/16 El Niño rivaled the events of 1982/83 and 1997/98, we show that it also differed in unique and important ways, with implications for the study and evaluation of past and future ENSO events. Unlike previous major El Niños, remarkably above-average SST anomalies occurred in the western and central equatorial Pacific but were milder near the coast of South America. While operational ENSO systems have progressed markedly over the past several decades, the 2015/16 El Niño highlights several challenges that will continue to test both the research and operational forecast communities.
Li, Dawei, Rong Zhang, and Thomas R Knutson, April 2017: On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline. Nature Communications, 8, 14991, doi:10.1038/ncomms14991. [ Abstract ]
This study aims to understand the relative roles of external forcing versus internal climate variability in causing the observed Barents Sea winter sea ice extent (SIE) decline since 1979. We identify major discrepancies in the spatial patterns of winter Northern Hemisphere sea ice concentration trends over the satellite period between observations and CMIP5 multi-model mean externally forced response. The CMIP5 externally forced decline in Barents Sea winter SIE is much weaker than that observed. Across CMIP5 ensemble members, March Barents Sea SIE trends have little correlation with global mean surface air temperature trends, but are strongly anti-correlated with trends in Atlantic heat transport across the Barents Sea Opening (BSO). Further comparison with control simulations from coupled climate models suggests that enhanced Atlantic heat transport across the BSO associated with regional internal variability may have played a leading role in the observed decline in winter Barents Sea SIE since 1979.
Liu, Maofeng, Gabriel A Vecchi, James A Smith, and Hiroyuki Murakami, April 2017: The Present-Day Simulation and Twenty-First-Century Projection of the Climatology of Extratropical Transition in the North Atlantic. Journal of Climate, 30(8), doi:10.1175/JCLI-D-16-0352.1. [ Abstract ]
This study explores the simulations and 21st century projections of extratropical transition (ET) of tropical cyclones (TCs) in the North Atlantic, with a newly developed global climate model: the Forecast-oriented Low Ocean Resolution (FLOR) version of the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model version 2.5 (CM2.5). FLOR exhibits good skill in simulating present-day ET properties (e.g., Cyclone Phase Space parameters). A version of FLOR in which sea surface temperature (SST) biases are artificially corrected through flux-adjustment (FLOR-FA) shows much improved simulation of ET activity (e.g., annual ET number). This result is largely attributable to better simulation of basinwide TC activity which is strongly dependent on larger-scale climate simulation. FLOR-FA is also used to explore changes of ET activity in the 21st century under the representative concentration pathway (RCP) 4.5 scenario. We find a contrasting pattern in which regional TC density increases in the eastern North Atlantic and decreases in the western North Atlantic, probably due to changes in the TC genesis location. The increasing TC frequency in the eastern Atlantic is dominated by increased ET cases. The increased density of TCs undergoing ET in the eastern subtropics of the Atlantic shows two propagation paths: one moves northwest towards the northeast coast of the United States and the other moves northeast toward Western Europe, implying increased TC-related risks in these regions. A more TC-favorable future climate, evident in the projected changes of SST and vertical wind shear, is hypothesized to favor the increased ET occurrence in the eastern North Atlantic.
Lu, Feiyu, Zhengyu Liu, Y Liu, Shaoqing Zhang, and R Jacob, May 2017: Understanding the control of extratropical atmospheric variability on ENSO using a coupled data assimilation approach. Climate Dynamics, 48(9), doi:10.1007/s00382-016-3256-7. [ Abstract ]
The control of extratropical atmospheric variability on ENSO variability is studied in a coupled general circulation model (CGCM) utilizing an ensemble-based coupled data assimilation (CDA) method in the perfect-model framework. Assimilation is limited to the desired model components (e.g. atmosphere) and spatial areas (e.g. the extratropics) to study the ensemble-mean model response (e.g. tropical response to “observed” extratropical atmospheric variability). The CDA provides continuously “corrected” extratropical atmospheric forcing and boundary conditions for the tropics and the use of ensemble optimizes the observational forcing signal over internal variability in the model component or region without assimilation. The experiments demonstrate significant control of extratropical atmospheric forcing on ENSO variability in the CGCM. When atmospheric “observations” are assimilated only poleward of 20° in both hemispheres, most ENSO events in the “observation” are reproduced and the error of the Nino3.4 index is reduced by over 40 % compared to the ensemble control experiment that does not assimilate any observations. Further experiments with the assimilation in each hemisphere show that the forced ENSO variability is contributed roughly equally and independently by the Southern and Northern Hemisphere extratropical atmosphere. Further analyses of the ENSO events in the southern hemisphere forcing experiment reveal robust precursors in both the extratropical atmosphere over southeastern Pacific and equatorial Pacific thermocline, consistent with previous studies of the South Pacific Meridional Mode and the discharge-recharge paradigm, respectively. However, composite analyses based on each precursor show that neither precursor alone is sufficient to trigger ENSO onset by itself and therefore neither alone could serve as a reliable predictor. Additional experiments with northern hemisphere forcing, ocean assimilation or different latitudes are also performed.
Milly, P C., and Krista A Dunne, August 2017: A Hydrologic Drying Bias in Water-Resource Impact Analyses of Anthropogenic Climate Change. Journal of the American Water Resources Association, 53(4), doi:10.1111/1752-1688.12538. [ Abstract ]
For water-resource planning, sensitivity of freshwater availability to anthropogenic climate change (ACC) often is analyzed with “offline” hydrologic models that use precipitation and potential evapotranspiration (Ep) as inputs. Because Ep is not a climate-model output, an intermediary model of Ep must be introduced to connect the climate model to the hydrologic model. Several Ep methods are used. The suitability of each can be assessed by noting a credible Ep method for offline analyses should be able to reproduce climate models’ ACC-driven changes in actual evapotranspiration in regions and seasons of negligible water stress (Ew). We quantified this ability for seven commonly used Ep methods and for a simple proportionality with available energy (“energy-only” method). With the exception of the energy-only method, all methods tend to overestimate substantially the increase in Ep associated with ACC. In an offline hydrologic model, the Ep-change biases produce excessive increases in actual evapotranspiration (E), whether the system experiences water stress or not, and thence strong negative biases in runoff change, as compared to hydrologic fluxes in the driving climate models. The runoff biases are comparable in magnitude to the ACC-induced runoff changes themselves. These results suggest future hydrologic drying (wetting) trends likely are being systematically and substantially overestimated (underestimated) in many water-resource impact analyses.
This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple timescales. To illustrate the approach, three sets of 32-year-long simulations are analyzed for northeastern North America and for the March-May season using the Geophysical Fluid Dynamics Laboratory’s Low Ocean-Atmosphere Resolution (LOAR) and Forecast-oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach, and are associated with a misrepresentation of troughs centered north of the Great Lakes, and deep coastal troughs. Moreover, the intra-seasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, inter-experiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple timescales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.
Given knowledge at the time, the recent 2015–2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower—but still of potential use to decision-makers—for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.
The 2015 hurricane season in the Eastern and Central Pacific Oceans (EPO and CPO), particularly around Hawaii, was extremely active – including a record number of tropical cyclones (TCs) and the first instance of three simultaneous Category 4 hurricanes in the EPO and CPO. A strong El Niño developed during the 2015 boreal summer season, and was attributed by some to be the cause of the extreme number of TCs. However, according to a suite of targeted high-resolution model experiments, the extreme 2015 EPO and CPO hurricane season was not primarily induced by the 2015 El Niño’s tropical Pacific warming, but by warming in the subtropical Pacific Ocean. This warming is not typical of El Niño, but rather the “Pacific Meridional Mode (PMM)” superimposed on long-term anthropogenic warming. Although the likelihood of such an extreme year depends on the phase of natural variability, the coupled GCM projects an increase in the frequency of such extremely active TC years over the next few decades for the EPO, CPO, and Hawaii due enhanced subtropical Pacific warming from anthropogenic greenhouse forcing.
In 2014 and 2015, post-monsoon extremely severe cyclonic storms (ESCS)—defined by the WMO as tropical storms with lifetime maximum winds greater than 46 m s−1—were first observed over the Arabian Sea (ARB), causing widespread damage. However, it is unknown to what extent this abrupt increase in post-monsoon ESCSs can be linked to anthropogenic warming, natural variability, or stochastic behaviour. Here, using a suite of high-resolution global coupled model experiments that accurately simulate the climatological distribution of ESCSs, we show that anthropogenic forcing has likely increased the probability of late-season ECSCs occurring in the ARB since the preindustrial era. However, the specific timing of observed late-season ESCSs in 2014 and 2015 was likely due to stochastic processes. It is further shown that natural variability played a minimal role in the observed increase of ESCSs. Thus, continued anthropogenic forcing will further amplify the risk of cyclones in the ARB, with corresponding socio-economic implications.
Two state-of-the-art Earth System Models (ESMs) were used in an idealized experiment to explore the role of mountains in shaping Earth’s climate system. Similar to previous studies, removing mountains from both ESMs results in the winds becoming more zonal, and weaker Indian and Asian monsoon circulations. However, there are also broad changes to the Walker circulation and the El Niño Southern Oscillation (ENSO). Without orography, convection moves across the entire equatorial Indo-Pacific basin on interannual timescales. The ENSO has a stronger amplitude, lower frequency and increased regularity. A wider equatorial wind zone and changes to equatorial wind stress curl result in a colder cold tongue and a steeper equatorial thermocline across the Pacific basin during La Niña years. Anomalies associated with ENSO warm events are larger without mountains, and have greater impact on the mean tropical climate than when mountains are present. Without mountains the centennial-mean Pacific Walker circulation weakens in both models by ~45%, but the strength of the mean Hadley circulation changes by <2%. Changes in the Walker circulation in these experiments can be explained by the large spatial excursions of atmospheric deep convection on interannual timescales. These results suggest that mountains are an important control on the large-scale tropical circulation, impacting ENSO dynamics and the Walker circulation, but have little impact on the strength of the Hadley circulation.
Future changes in the North American monsoon, a circulation system that brings abundant summer rains to vast areas of the North American Southwest1, 2, could have significant consequences for regional water resources3. How this monsoon will change with increasing greenhouse gases, however, remains unclear4, 5, 6, not least because coarse horizontal resolution and systematic sea-surface temperature biases limit the reliability of its numerical model simulations5, 7. Here we investigate the monsoon response to increased atmospheric carbon dioxide (CO2) concentrations using a 50-km-resolution global climate model which features a realistic representation of the monsoon climatology and its synoptic-scale variability8. It is found that the monsoon response to CO2 doubling is sensitive to sea-surface temperature biases. When minimizing these biases, the model projects a robust reduction in monsoonal precipitation over the southwestern United States, contrasting with previous multi-model assessments4, 9. Most of this precipitation decline can be attributed to increased atmospheric stability, and hence weakened convection, caused by uniform sea-surface warming. These results suggest improved adaptation measures, particularly water resource planning, will be required to cope with projected reductions in monsoon rainfall in the American Southwest.
Perlwitz, J, and Thomas R Knutson, et al., November 2017: Large-scale circulation and climate variability In Climate Science Special Report: Fourth National Climate Assessment, Volume I, Washington, DC, Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.), U.S. Global Change Research Program, Washington, DC, doi:10.7930/J0RV0KVQ161-184.
Predybaylo, Evgeniya, Georgiy Stenchikov, Andrew T Wittenberg, and Fanrong Zeng, January 2017: Impacts of a Pinatubo-size volcanic eruption on ENSO. Journal of Geophysical Research: Atmospheres, 122(2), doi:10.1002/2016JD025796. [ Abstract ]
Observations and model simulations of the climate responses to strong explosive low-latitude volcanic eruptions suggest a significant increase in the likelihood of El Niño during the eruption and posteruption years, though model results have been inconclusive and have varied in magnitude and even sign. In this study, we test how this spread of responses depends on the initial phase of El Niño-Southern Oscillation (ENSO) in the eruption year and on the eruption's seasonal timing. We employ the Geophysical Fluid Dynamics Laboratory CM2.1 global coupled general circulation model to investigate the impact of the Pinatubo 1991 eruption, assuming that in 1991 ENSO would otherwise be in central or eastern Pacific El Niño, La Niña, or neutral phases. We obtain statistically significant El Niño responses in a year after the eruption for all cases except La Niña, which shows no response in the eastern equatorial Pacific. The eruption has a weaker impact on eastern Pacific El Niños than on central Pacific El Niños. We find that the ocean dynamical thermostat and (to a lesser extent) wind changes due to land-ocean temperature gradients are the main feedbacks affecting El Niño development after the eruption. The El Niño responses to eruptions occurring in summer are more pronounced than for winter and spring eruptions. That the climate response depends on eruption season and initial ENSO phase may help to reconcile apparent inconsistencies among previous studies.
Ice bridges are static structures composed of tightly packed sea ice that can form during the course of its
flow through a narrow strait. Despite their important role in local ecology and climate, the formation and
breakup of ice bridges is not well understood and has proved difficult to predict. Using long-wave
approximations and a continuum description of sea ice dynamics, we develop a one-dimensional theory for
the wind-driven formation of ice bridges in narrow straits, which is verified against direct numerical
simulations. We show that for a given wind stress and minimum and maximum channel widths, a steadystate
ice bridge can only form beyond a critical value of the thickness and the compactness of the ice field.
The theory also makes quantitative predictions for ice fluxes, which are particularly useful to estimate the
ice export associated with the breakup of ice bridges. We note that similar ideas are applicable to dense
granular flows in confined geometries.
Rallabandi, B, Z Zheng, Michael Winton, and H Stone, July 2017: Formation of sea ice bridges in narrow straits in response to wind and water stresses. Journal of Geophysical Research: Oceans, 122(7), doi:10.1002/2017JC012822. [ Abstract ]
Ice bridges are rigid structures composed of sea ice that form seasonally in the many straits and channels of the Canadian Arctic Archipelago. Driven primarily by atmospheric stresses, these ice bridges are formed when sufficiently thick ice “jams” during the course of its flow between land masses, resulting in a region of stationary compacted ice that is separated from a region of flowing open water (a polynya) by a static arch. Using a continuum description of sea ice that is widely used in climate modeling, we present an asymptotic theory of the process of formation of such bridges in slender channels when the motion of the ice is driven by external wind and water stresses. We show that for an arbitrary channel shape, ice bridges can only form within a range of ice properties that is determined by the channel geometry and the external stress. We then compare the results of our theory with direct numerical simulations and observational evidence. Finally, we provide simple analytical expressions for the mean speed of the ice flow as a function of the channel shape, the properties of the ice, and the wind and water stresses along the channel.
Ruprich-Robert, Yohan, Rym Msadek, Frederic Castruccio, Stephen G Yeager, Thomas L Delworth, and Gokhan Danabasoglu, April 2017: Assessing the Climate impacts of the observed Atlantic Mulitdecadal Variability using the GFDL CM2.1 and NCAR CESM1 Global Coupled Models. Journal of Climate, 30(8), doi:10.1175/JCLI-D-16-0127.1. [ Abstract ]
The climate impacts of the observed Atlantic Multidecadal Variability (AMV) are investigated using the GFDL-CM2.1 and the NCAR-CESM1 coupled climate models. The model North Atlantic sea surface temperatures are restored to fixed anomalies corresponding to an estimate of the internally driven component of the observed AMV. Both models show that during boreal summer the AMV alters the Walker Circulation and generates precipitation anomalies over the whole tropical belt. A warm phase of the AMV yields reduced precipitation over western US, drier conditions over the Mediterranean basin, and wetter conditions over Northern Europe. During boreal winter, the AMV modulates by a factor of ~2 the frequency of occurrence of El Niño/La Niña events. This response is associated with anomalies over the Pacific that project onto the Interdecadal Pacific Oscillation pattern, i.e., Pacific Decadal Oscillation-like anomalies in the Northern hemisphere and a symmetrical pattern in the Southern Hemisphere. This winter response is a lagged adjustment of the Pacific Ocean to the AMV forcing in summer. Most of the simulated global-scale impacts are driven by the tropical part of the AMV, except for the winter North Atlantic Oscillation-like response over the North Atlantic/European region, which is driven by both the subpolar and the tropical parts of the AMV. The teleconnections between the Pacific and Atlantic basins alter the direct North Atlantic local response to the AMV, which highlights the importance of using a global coupled framework to investigate the climate impacts of the AMV. The similarity of the two model responses gives confidence that impacts described in this paper are robust.
Salas, E A L., V A Seamster, K G Boykin, N M Harings, and Keith W Dixon, March 2017: Modeling the impacts of climate change on Species of Concern (birds) in South Central U.S. based on bioclimatic variables. AIMS Environmental Science, 4(2), doi:10.3934/environsci.2017.2.358. [ Abstract ]
We used 19 bioclimatic variables, five species distribution modeling (SDM) algorithms, four general circulation models, and two climate scenarios (2050 and 2070) to model nine bird species. Identified as Species of Concern (SOC), we highlighted these birds: Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin’s Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus). The Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, Multivariate Adaptive Regression Splines, and an ensemble model were used to identify present day core bioclimatic-envelopes for the species. We then projected future distributions of suitable climatic conditions for the species using data derived from four climate models run according to two greenhouse gas Representative Concentration Pathways (RCPs 2.6 and 8.5). Our models predicted changes in suitable bioclimatic-envelopes for all species for the years 2050 and 2070. Among the nine species of birds, the quails were found to be highly susceptible to climate change and appeared to be of most future conservation concern. The White-tailed Ptarmigan would lose about 62% of its suitable climatic habitat by 2050 and 67% by 2070. Among the species distribution models (SDMs), the Boosted Regression Tree model consistently performed fairly well based on Area Under the Curve (AUC range: 0.89 to 0.97) values. The ensemble models showed improved True Skill Statistics (all TSS values > 0.85) and Kappa Statistics (all K values > 0.80) for all species relative to the individual SDMs.
Salas, E A L., V A Seamster, N M Harings, K G Boykin, G Alvarez, and Keith W Dixon, August 2017: Projected Future Bioclimate-Envelope Suitability for Reptile and Amphibian Species of Concern in South Central USA. Herpetological Conservation and Biology, 12(2), 522-547. [ Abstract ]
Future climate change has impacts on the distribution of species. Using species distribution models
(SDM), we modeled the bioclimatic envelopes of four herpetofauna species in the South Central USA including
two salamanders, the Sacramento Mountain Salamander (Aneides hardii) and the Jemez Mountains Salamander
(Plethodon neomexicanus), one anuran, the Chiricahua Leopard Frog (Lithobates chiricahuensis), and one turtle,
the Rio Grande Cooter (Pseudemys gorzugi). We used Generalized Linear Model, Random Forest, Boosted
Regression Tree, Maxent, and Multivariate Adaptive Regression Splines, and binary ensembles to develop the
present day distributions of the species based on climate-driven models alone. We projected future distributions of
the species using data from four climate models run according to two greenhouse gas concentration pathways (RCP
2.6 and RCP 8.5). Our model results projected losses and gains in suitable bioclimatic envelopes for the years 2050
and 2070. The Boosted Regression Tree model consistently performed well among SDMs based on Area Under the
Curve (AUC; range = 0.88 to 0.97) values and kappa statistics (K > 0.75).
Salvi, K, Gabriele Villarini, and Gabriel A Vecchi, October 2017: High resolution decadal precipitation predictions over the continental United States for impacts assessment. Journal of Hydrology, 553, doi:10.1016/j.jhydrol.2017.07.043. [ Abstract ]
Unprecedented alterations in precipitation characteristics over the last century and especially in the last two decades have posed serious socio-economic problems to society in terms of hydro-meteorological extremes, in particular flooding and droughts. The origin of these alterations has its roots in changing climatic conditions; however, its threatening implications can only be dealt with through meticulous planning that is based on realistic and skillful decadal precipitation predictions (DPPs). Skillful DPPs represent a very challenging prospect because of the complexities associated with precipitation predictions. Because of the limited skill and coarse spatial resolution, the DPPs provided by General Circulation Models (GCMs) fail to be directly applicable for impact assessment. Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches. For both the approaches, statistical relationships established over the calibration period (1961–1990) are applied to the retrospective and near future decadal predictions by GCMs to obtain DPPs at ∼4 km resolution. The skill is quantified across different metrics that evaluate potential skill, biases, long-term statistical properties, and uncertainty. Both the statistical approaches show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DPPs from nine GCMs with 4-km spatial resolution, which can be used as a key input for impacts assessments.
Salvi, K, Gabriele Villarini, and Gabriel A Vecchi, et al., November 2017: Decadal temperature predictions over the continental United States: Analysis and Enhancement. Climate Dynamics, 49(9-10), doi:10.1007/s00382-017-3532-1. [ Abstract ]
Increases in global temperature over recent decades and the projected acceleration in warming trends over the 21 century have resulted in a strong need to obtain information about future temperature conditions. Hence, skillful decadal temperature predictions (DTPs) can have profound societal and economic benefits through informed planning and response. However, skillful and actionable DTPs are extremely challenging to achieve. Even though general circulation models (GCMs) provide decadal predictions of different climate variables, the direct use of GCM data for regional-scale impacts assessment is not encouraged because of the limited skill they possibly exhibit and their coarse spatial resolution. Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling. For both the approaches, statistical parameters/relationships, established over the calibration period (1961–1990) are applied to retrospective and near future decadal predictions by GCMs to obtain DTPs at ‘4 km’ resolution. Predictions are assessed using different evaluation metrics, long-term statistical properties, and uncertainty in terms of the range of predictions. Both the approaches adopted here show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DTPs from 14 GCMs with a spatial resolution of 4 km, which can be used as a key input for impacts assessments.
Smedsrud, L H., M H Halvorsen, Julienne Stroeve, Rong Zhang, and K Kloster, January 2017: Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years. The Cryosphere, 11(1), doi:10.5194/tc-11-65-2017. [ Abstract ]
A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km2, representing 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with several years having annual ice exports that exceeded 1 million km2. This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual ice export of about +6 % per decade, with spring and summer showing larger changes in ice export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased ice export during winter will generally result in new ice growth and contributes to thinning inside the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice–albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice concentration. We find a general moderate influence between export anomalies and the following September sea ice extent, explaining 18 % of the variance between 1935 and 2014, but with higher values since 2004.
Stuecker, Malte F., Axel Timmermann, Fei-Fei Jin, Y Chikamoto, W J Zhang, Andrew T Wittenberg, E Widiasih, and Sen Zhao, March 2017: Revisiting ENSO/Indian Ocean Dipole phase relationships. Geophysical Research Letters, 44(5), doi:10.1002/2016GL072308. [ Abstract ]
Here we show that the characteristics of the Indian Ocean Dipole (IOD), such as its power spectrum and phase relationship with the El Niño–Southern Oscillation (ENSO), can be succinctly explained by ENSO combination mode (C-mode) wind and heat flux forcing together with a seasonal modulation of the air/sea coupled Indian Ocean (IO) Bjerknes feedback. This model explains the observed high-frequency near-annual IOD variability in terms of deterministic ENSO/annual cycle interactions. ENSO-independent IOD events can be understood as a seasonally modulated ocean response to white noise atmospheric forcing. Under this new physical null hypothesis framework, IOD predictability is determined by both ENSO predictability and the ENSO signal-to-noise ratio. We further emphasize that lead/lag correlations between different climate variables are easily misinterpreted when not accounting properly for the seasonal modulation of the underlying climate phenomena.
Tommasi, Desiree, Charles A Stock, A J Hobday, R Methot, Isaac C Kaplan, J P Eveson, Kirstin Holsman, Timothy J Miller, Sarah K Gaichas, Marion Gehlen, A Pershing, Gabriel A Vecchi, Rym Msadek, Thomas L Delworth, C M Eakin, Melissa A Haltuch, Roland Séférian, C M Spillman, J R Hartog, Samantha A Siedlecki, Jameal F Samhouri, Barbara A Muhling, R G Asch, Malin L Pinsky, Vincent S Saba, Sarah B Kapnick, and Carlos F Gaitán, et al., March 2017: Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Progress in Oceanography, 152, doi:10.1016/j.pocean.2016.12.011. [ Abstract ]
Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.
Tommasi, Desiree, Charles A Stock, Kathleen Pegion, and Gabriel A Vecchi, et al., March 2017: Improved management of small pelagic fisheries through seasonal climate prediction. Ecological Applications, 27(2), doi:10.1002/eap.1458. [ Abstract ]
Populations of small pelagic fish are strongly influenced by climate. The inability of managers to anticipate environment-driven fluctuations in stock productivity or distribution can lead to overfishing and stock collapses, inflexible management regulations inducing shifts in the functional response to human predators, lost opportunities to harvest populations, bankruptcies in the fishing industry, and loss of resilience in the human food supply. Recent advances in dynamical global climate prediction systems allow for sea surface temperature (SST) anomaly predictions at a seasonal scale over many shelf ecosystems. Here we assess the utility of SST predictions at this “fishery relevant” scale to inform management, using Pacific sardine as a case study. The value of SST anomaly predictions to management was quantified under four harvest guidelines (HGs) differing in their level of integration of SST data and predictions. The HG that incorporated stock biomass forecasts informed by skillful SST predictions led to increases in stock biomass and yield, and reductions in the probability of yield and biomass falling below socioeconomic or ecologically acceptable levels. However, to mitigate the risk of collapse in the event of an erroneous forecast, it was important to combine such forecast-informed harvest controls with additional harvest restrictions at low biomass.
Decisions made by fishers and fisheries managers are informed by climate and fisheries observations that now often span more than 50 years. Multi-annual climate forecasts could further inform such decisions if they were skillful in predicting future conditions relative to the 50-year scope of past variability. We demonstrate that an existing multi-annual prediction system skillfully forecasts the probability of next year, the next 1–3 years, and the next 1–10 years being warmer or cooler than the 50-year average at the surface in coastal ecosystems. Probabilistic forecasts of upper and lower seas surface temperature (SST) terciles over the next 3 or 10 years from the GFDL CM 2.1 10-member ensemble global prediction system showed significant improvements in skill over the use of a 50-year climatology for most Large Marine Ecosystems (LMEs) in the North Atlantic, the western Pacific, and Indian oceans. Through a comparison of the forecast skill of initialized and uninitialized hindcasts, we demonstrate that this skill is largely due to the predictable signature of radiative forcing changes over the 50-year timescale rather than prediction of evolving modes of climate variability. North Atlantic LMEs stood out as the only coastal regions where initialization significantly contributed to SST prediction skill at the 1 to 10 year scale.
van der Wiel, Karin, Sarah B Kapnick, G J van Oldenborgh, K Whan, S Philip, Gabriel A Vecchi, R K Singh, J Arrighi, and H Cullen, February 2017: Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change. Hydrology and Earth System Sciences, 21(2), doi:10.5194/hess-21-897-2017. [ Abstract ]
A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (U.S.) starting around 10 August, 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a three-day maximum found at a station in Livingston, LA (east of Baton Rouge) from 12–14 August, 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30,000 people had been rescued, nearly 10,600 people had slept in shelters on the night of 14 August, and at least 60,600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least thirteen people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the U.S. since Super Storm Sandy made landfall in New Jersey on 24 October, 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real-time using the best readily available observational data and high-resolution global climate model simulations.
The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data, and analysis methods are readily available upon the start. It is the authors aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the Central U.S. Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region: 29–31º N, 85–95º W, which we refer to as the Central U.S. Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (C.I.): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A Central U.S. Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses. The magnitude of the shift in probabilities is greater in the 25 km (higher resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.
Climate change has been shown to impact the mean climate state and climate extremes. Though climate extremes have the potential to disrupt society, extreme conditions are rare by definition. In contrast, mild weather occurs frequently and many human activities are built around it. We provide a global analysis of mild weather based on simple criteria and explore changes in response to radiative forcing. We find a slight global mean decrease in the annual number of mild days projected both in the near future (−4 days per year, 2016–2035) and at the end of this century (−10 days per year, 2081–2100). Projected seasonal and regional redistributions of mild days are substantially greater. These changes are larger than the interannual variability of mild weather caused by El Niño–Southern Oscillation. Finally, we show an observed global decrease in the recent past, and that observed regional changes in mild weather resemble projections.
Temperature variability in the North Atlantic Ocean is the result of many competing physical processes, but the relative roles of these processes is a source of contention. Here, scientists present two perspectives on the debate.
Wehner, Michael F., J R Arnold, and Thomas R Knutson, et al., November 2017: Droughts, floods, and wildfires In Climate Science Special Report: Fourth National Climate Assessment, Volume I, Washington, DC, Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.), U.S. Global Change Research Program, Washington, DC, doi:10.7930/J0CJ8BNN231-256.
Wuebbles, D J., David R Easterling, K Hayhoe, and Thomas R Knutson, et al., November 2017: Our globally changing climate In Climate Science Special Report: Fourth National Climate Assessment, Volume I, Washington, DC, Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.), U.S. Global Change Research Program, Washington, DC, doi:10.7930/J08S4N3535-72.
The TAO/TRITON array is the cornerstone of the tropical Pacific and ENSO observing system. Motivated by the recent rapid decline of the TAO/TRITON array, the potential utility of TAO/TRITON was assessed for ENSO monitoring and prediction. The analysis focused on the period when observations from Argo floats were also available. We coordinated observing system experiments (OSEs) using the global ocean data assimilation system (GODAS) from the National Centers for Environmental Prediction and the ensemble coupled data assimilation (ECDA) from the Geophysical Fluid Dynamics Laboratory for the period 2004–2011. Four OSE simulations were conducted with inclusion of different subsets of in situ profiles: all profiles (XBT, moorings, Argo), all except the moorings, all except the Argo and no profiles. For evaluation of the OSE simulations, we examined the mean bias, standard deviation difference, root-mean-square difference (RMSD) and anomaly correlation against observations and objective analyses. Without assimilation of in situ observations, both GODAS and ECDA had large mean biases and RMSD in all variables. Assimilation of all in situ data significantly reduced mean biases and RMSD in all variables except zonal current at the equator. For GODAS, the mooring data is critical in constraining temperature in the eastern and northwestern tropical Pacific, while for ECDA both the mooring and Argo data is needed in constraining temperature in the western tropical Pacific. The Argo data is critical in constraining temperature in off-equatorial regions for both GODAS and ECDA. For constraining salinity, sea surface height and surface current analysis, the influence of Argo data was more pronounced. In addition, the salinity data from the TRITON buoys played an important role in constraining salinity in the western Pacific. GODAS was more sensitive to withholding Argo data in off-equatorial regions than ECDA because it relied on local observations to correct model biases and there were few XBT profiles in those regions. The results suggest that multiple ocean data assimilation systems should be used to assess sensitivity of ocean analyses to changes in the distribution of ocean observations to get more robust results that can guide the design of future tropical Pacific observing systems.
Xue, Y, C. Wen, Arun Kumar, Magdalena Alonso Balmaseda, Yosuke Fujii, Oscar Alves, M Martin, Xiaosong Yang, G Vernieres, C Desportes, Tong Lee, I Ascione, Richard G Gudgel, and I Ishikawa, December 2017: A real-time ocean reanalyses intercomparison project in the context of tropical pacific observing system and ENSO monitoring. Climate Dynamics, 49(11-12), doi:10.1007/s00382-017-3535-y. [ Abstract ]
An ensemble of nine operational ocean reanalyses (ORAs) is now routinely collected, and is used to monitor the consistency across the tropical Pacific temperature analyses in real-time in support of ENSO monitoring, diagnostics, and prediction. The ensemble approach allows a more reliable estimate of the signal as well as an estimation of the noise among analyses. The real-time estimation of signal-to-noise ratio assists the prediction of ENSO. The ensemble approach also enables us to estimate the impact of the Tropical Pacific Observing System (TPOS) on the estimation of ENSO-related oceanic indicators. The ensemble mean is shown to have a better accuracy than individual ORAs, suggesting the ensemble approach is an effective tool to reduce uncertainties in temperature analysis for ENSO. The ensemble spread, as a measure of uncertainties in ORAs, is shown to be partially linked to the data counts of in situ observations. Despite the constraints by TPOS data, uncertainties in ORAs are still large in the northwestern tropical Pacific, in the SPCZ region, as well as in the central and northeastern tropical Pacific. The uncertainties in total temperature reduced significantly in 2015 due to the recovery of the TAO/TRITON array to approach the value before the TAO crisis in 2012. However, the uncertainties in anomalous temperature remained much higher than the pre-2012 value, probably due to uncertainties in the reference climatology. This highlights the importance of the long-term stability of the observing system for anomaly monitoring. The current data assimilation systems tend to constrain the solution very locally near the buoy sites, potentially damaging the larger-scale dynamical consistency. So there is an urgent need to improve data assimilation systems so that they can optimize the observation information from TPOS and contribute to improved ENSO prediction.
Observed Atlantic major hurricane frequency has exhibited pronounced multidecadal variability since the 1940s. However, the cause of this variability is debated. Using observations and a coupled earth system model (GFDL-ESM2G), here we show that the decline of the Atlantic major hurricane frequency during 2005–2015 is associated with a weakening of the Atlantic Meridional Overturning Circulation (AMOC) inferred from ocean observations. Directly observed North Atlantic sulfate aerosol optical depth has not increased (but shows a modest decline) over this period, suggesting the decline of the Atlantic major hurricane frequency during 2005–2015 is not likely due to recent changes in anthropogenic sulfate aerosols. Instead, we find coherent multidecadal variations involving the inferred AMOC and Atlantic major hurricane frequency, along with indices of Atlantic Multidecadal Variability and inverted vertical wind shear. Our results provide evidence for an important role of the AMOC in the recent decline of Atlantic major hurricane frequency.
This study examines the year-to-year modulation of the western North Pacific (WNP) tropical cyclones (TC) activity by the Atlantic Meridional Mode (AMM) using both observations and the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) global coupled model. 1. The positive (negative) AMM phase suppresses (enhances) WNP TC activity in observations. The anomalous occurrence of WNP TCs results mainly from changes in TC genesis in the southeastern part of the WNP. 2. The observed responses of WNP TC activity to the AMM are connected to the anomalous zonal vertical wind shear (ZVWS) caused by AMM-induced changes to the Walker circulation. During the positive AMM phase, the warming in the North Atlantic induces strong descending flow in the tropical eastern and central Pacific, which intensifies the Walker cell in the WNP. The intensified Walker cell is responsible for the suppressed (enhanced) TC genesis in the eastern (western) part of the WNP by strengthening (weakening) ZVWS. 3. The observed WNPTC–AMM linkage is examined by the long-term control and idealized perturbations experiment with FLOR-FA. A suite of sensitivity experiments strongly corroborate the observed WNPTC–AMM linkage and underlying physical mechanisms.
The impact of multidecadal variations of the Atlantic meridional overturning circulation (AMOC) on the Southern Ocean (SO) is investigated in the current paper using a coupled ocean–atmosphere model. We find that the AMOC can influence the SO via fast atmosphere teleconnections and subsequent ocean adjustments. A stronger than normal AMOC induces an anomalous warm SST over the North Atlantic, which leads to a warming of the Northern Hemisphere troposphere extending into the tropics. This induces an increased equator-to-pole temperature gradient in the Southern Hemisphere (SH) upper troposphere and lower stratosphere due to an amplified tropical upper tropospheric warming as a result of increased latent heat release. This altered gradients leads to a poleward displacement of the SH westerly jet. The wind change over the SO then cools the SST at high latitudes by anomalous northward Ekman transports. The wind change also weakens the Antarctic bottom water (AABW) cell through changes in surface heat flux forcing. The poleward shifted westerly wind decreases the long term mean easterly winds over the Weddell Sea, thereby reducing the turbulent heat flux loss, decreasing surface density and therefore leading to a weakening of the AABW cell. The weakened AABW cell produces a temperature dipole in the SO, with a warm anomaly in the subsurface and a cold anomaly in the surface that corresponds to an increase of Antarctic sea ice. Opposite conditions occur for a weaker than normal AMOC. Our study here suggests that efforts to attribute the recent observed SO variability to various factors should take into consideration not only local process but also remote forcing from the North Atlantic.
This study attempts to improve the prediction of western North Pacific (WNP) and East Asia (EA) landfalling tropical cyclones (TCs) using modes of large-scale climate variability [e.g., the Pacific meridional mode (PMM), the Atlantic meridional mode (AMM), and North Atlantic sea surface temperature anomalies (NASST)] as predictors in a hybrid statistical–dynamical scheme, based on dynamical model forecasts with the GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 with flux adjustments (FLOR-FA). Overall, the predictive skill of the hybrid model for the WNP TC frequency increases from lead month 5 (initialized in January) to lead month 0 (initialized in June) in terms of correlation coefficient and root-mean-square error (RMSE). The hybrid model outperforms FLOR-FA in predicting WNP TC frequency for all lead months. The predictive skill of the hybrid model improves as the forecast lead time decreases, with values of the correlation coefficient increasing from 0.56 for forecasts initialized in January to 0.69 in June. The hybrid models for landfalling TCs over the entire East Asian (EEA) coast and its three subregions [i.e., southern EA (SEA), middle EA (MEA), and northern EA (NEA)] dramatically outperform FLOR-FA. The correlation coefficient between predicted and observed TC landfall over SEA increases from 0.52 for forecasts initialized in January to 0.64 in June. The hybrid models substantially reduce the RMSE of landfalling TCs over SEA and EEA compared with FLOR-FA. This study suggests that the PMM and NASST/AMM can be used to improve statistical/hybrid forecast models for the frequencies of WNP or East Asia landfalling TCs.
This study explores the potential predictability of the Southern Ocean (SO) climate on decadal timescales as represented in the GFDL CM2.1 model using prognostic methods. We conduct perfect model predictability experiments starting from ten different initial states, and show potentially predictable variations of Antarctic bottom water formation (AABW) rates on time scales as long as twenty years. The associated Weddell Sea (WS) subsurface temperatures and Antarctic sea ice have comparable potential predictability as the AABW cell. The predictability of sea surface temperature (SST) variations over the WS and the SO is somewhat smaller, with predictable scales out to a decade. This reduced predictability is likely associated with stronger damping from air-sea interaction. As a complement to our perfect predictability study, we also make hindcasts of SO decadal variability using the GFDL CM2.1 decadal prediction system. Significant predictive skill for SO SST on multi-year time scales is found in the hindcast system. The success of the hindcasts, especially in reproducing observed surface cooling trends, is largely due to initializing the state of the AABW cell. A weak state of the AABW cell leads to cooler surface conditions and more extensive sea ice. Although there are considerable uncertainties regarding the observational data used to initialize the hindcasts, the consistency between the perfect model experiments and the decadal hindcasts at least gives us some indication as to where and to what extent skillful decadal SO forecasts might be possible.
Zhang, Wei, Gabriele Villarini, and Gabriel A Vecchi, April 2017: Impacts of the Pacific Meridional Mode on June-August Precipitation in the Amazon River Basin. Quarterly Journal of the Royal Meteorological Society, 143(705), doi:10.1002/qj.3053. [ Abstract ]
This study examines the impacts of the Pacific Meridional Mode (PMM) on Amazon precipitation during the June-August months using observations and several experiments with the National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory (NOAA/GFDL) Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR). We find that the positive (negative) PMM can lead to precipitation surplus (deficit) using both observations and climate simulations with FLOR. The impacts of PMM on Amazon precipitation during June-August are induced by the forcing of sea surface temperature (SST) anomalies associated with PMM. Positive PMM can force the baroclinic Gill responses to the heat source in the Pacific with two low-level cyclones (anticyclones) located west (east) of the heating source. The anomalous low-level anticyclone and high-level cyclone located in the Amazon region are associated with low-level moisture moisture transport from the Atlantic. There is significant positive correlation between the PMM index and moisture flux convergence in most part of the Amazon basin, with negative correlation in its northwestern part. Such physical mechanisms underlying the linkage between PMM and the Amazon precipitation are supported by both the 500-year control experiment and a suite of perturbation experiments with FLOR.
The average predictability time (APT) method is used to identify the most predictable components of decadal sea surface temperature (SST) variations over the Southern Ocean (SO) in a 4000 year unforced control run of the GFDL CM2.1 model. The most predictable component shows significant predictive skill for periods as long as 20 years. The physical pattern of this variability has a uniform sign of SST anomalies over the SO, with maximum values over the Amundsen-Bellingshausen-Weddell Seas. Spectral analysis of the associated APT time series shows a broad peak on time scales of 70-120 years. This most predictable pattern is closely related to the mature phase of a mode of internal variability in the SO that is associated with fluctuations of deep ocean convection. The second most predictable component of SO SST is characterized by a dipole structure, with SST anomalies of one sign over the Weddell Sea and SST anomalies of the opposite sign over the Amundsen-Bellingshausen Seas. This component has significant predictive skill for periods as long as 6 years. This dipole mode is associated with a transition between phases of the dominant pattern of SO internal variability. The long time scales associated with variations in SO deep convection provide the source of the predictive skill of SO SST on decadal scales. These analyses suggest that if we could adequately initialize the SO deep convection in a numerical forecast model, the future evolution of SO SST and its associated climate impacts is potentially predictable.
Zhang, Rong, August 2017: On the Persistence and Coherence of Subpolar Sea Surface Temperature and Salinity Anomalies Associated with the Atlantic Multidecadal Variability. Geophysical Research Letters, 44(15), doi:10.1002/2017GL074342. [ Abstract ]
This study identifies key features associated with the Atlantic Multidecadal Variability (AMV) in both observations and a fully coupled climate model that can be used to distinguish the AMV mechanism: e.g. decadal persistence of monthly mean subpolar North Atlantic (NA) sea surface temperature (SST) and salinity (SSS) anomalies, and high coherence at low frequency among subpolar NA SST/SSS, upper ocean heat/salt content, and the Atlantic Meridional Overturning Circulation (AMOC) fingerprint. These key AMV features cannot be explained by the slab ocean model results or the red noise process, but are consistent with the ocean dynamics mechanism. This study also shows that at low frequency, the correlation and regression between net surface heat flux and SST anomalies are key indicators of the relative roles of oceanic versus atmospheric forcing in SST anomalies. The oceanic forcing plays a dominant role in the subpolar NA SST anomalies associated with the AMV.
Zhao, Y, X Deng, Shaoqing Zhang, Zhengyu Liu, C Liu, and Gabriel A Vecchi, et al., November 2017: Impact of Optimal Observational Time Window on Coupled Data Assimilation: Simulation with a Simple Climate Model. Nonlinear Processes in Geophysics, 24(4), doi:10.5194/npg-24-681-2017. [ Abstract ]
Climate signals are the results of interactions of multiple time scale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples. Given different time scales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system, we address this issue here. Results show that required by retrieval of characteristic variability of each coupled medium, an optimal OTW determined from the de-correlation time scale provides maximal observational information that best fits characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulted CDA improves the analysis of climate signals greatly. The simple model results provide a guideline when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.
Arid Extratropical Asia (AEA) is bisected at the wetter Tianshan Mountains (a northern offshoot of the Tibetan Plateau) into East and West Deserts, each with unique climatological characteristics. The East Deserts (~ 75 – 115°E, ~ 35 – 55°N) have a summer precipitation maximum, and the West Deserts (~ 45 – 75°E, ~ 35 – 55°N) have a winter-spring precipitation maximum. A new high-resolution (50 km atmosphere/land) global coupled climate model is run with the Tianshan Mountains removed to determine whether these mountains are responsible for the climatological East-West differentiation of AEA. Multi-centennial simulations for the Control and NoTianshan runs highlight statistically significant effects of the Tianshan. Overall, the Tianshan are found to enhance the precipitation seasonality gradient across AEA, mostly through altering the East Deserts. The Tianshan dramatically change the precipitation seasonality of the Taklimakan Desert directly to its east (the driest part of AEA), by blocking West winter precipitation, enhancing subsidence over this region, and increasing East summer precipitation. The Tianshan increase East summer precipitation through two mechanisms: 1) orographic precipitation which is greatest on the eastern edge of the Tianshan in summer, and 2) remote enhancement of the East Asian Summer Monsoon through alteration of the larger-scale seasonal mean atmospheric circulation. The decrease in East winter precipitation also generates remote warming of the Altai and Kunlun Mountains, northeast and southeast of the Tianshan respectively, due to reduction of snow cover and corresponding albedo decrease.
Berg, Alexis, Kirsten L Findell, Benjamin R Lintner, A Giannini, Sonia I Seneviratne, Bart van den Hurk, R Lorenz, A J Pitman, S Hagemann, A Meier, F Cheruy, A Ducharne, Sergey Malyshev, and P C D Milly, September 2016: Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nature Climate Change, 6(9), doi:10.1038/nclimate3029. [ Abstract ]
The response of the terrestrial water cycle to global warming is central to issues including water resources, agriculture and ecosystem health. Recent studies1, 2, 3, 4, 5, 6 indicate that aridity, defined in terms of atmospheric supply (precipitation, P) and demand (potential evapotranspiration, Ep) of water at the land surface, will increase globally in a warmer world. Recently proposed mechanisms for this response emphasize the driving role of oceanic warming and associated atmospheric processes4, 5. Here we show that the aridity response is substantially amplified by land–atmosphere feedbacks associated with the land surface’s response to climate and CO2 change. Using simulations from the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment7, 8, 9, we show that global aridity is enhanced by the feedbacks of projected soil moisture decrease on land surface temperature, relative humidity and precipitation. The physiological impact of increasing atmospheric CO2 on vegetation exerts a qualitatively similar control on aridity. We reconcile these findings with previously proposed mechanisms5 by showing that the moist enthalpy change over land is unaffected by the land hydrological response. Thus, although oceanic warming constrains the combined moisture and temperature changes over land, land hydrology modulates the partitioning of this enthalpy increase towards increased aridity.
Brown, Patrick T., S Lozier, Rong Zhang, and Wenhong Li, April 2016: The necessity of cloud feedback for a basin-scale Atlantic Multidecadal Oscillation. Geophysical Research Letters, 43(8), doi:10.1002/2016GL068303. [ Abstract ]
The Atlantic Multidecadal Oscillation (AMO), characterized by basin-scale multidecadal variability in North Atlantic sea surface temperatures (SSTs), has traditionally been interpreted as the surface signature of variability in oceanic heat convergence (OHC) associated with the Atlantic Meridional Overturning Circulation (AMOC). This view has been challenged by recent studies that show that AMOC variability is not simultaneously meridionally coherent over the North Atlantic and that AMOC-induced low-frequency variability of OHC is weak in the tropical North Atlantic. Here we present modeling evidence that the AMO-related SST variability over the extratropical North Atlantic results directly from anomalous OHC associated with the AMOC but that the emergence of the coherent multidecadal SST variability over the tropical North Atlantic requires cloud feedback. Our study identifies atmospheric processes as a necessary component for the existence of a basin-scale AMO, thus amending the canonical view that the AMOC-AMO connection is solely attributable to oceanic processes.
Chang, You-Soon, and Shaoqing Zhang, December 2016: XBT Effects on the Global Ocean State Estimates Using a Coupled Data Assimilation System. Terrestrial Atmospheric and Oceanic Sciences, 27(6), doi:10.3319/TAO.2016.09.23.01. [ Abstract ]
The early 21st century experienced a transition in global ocean observing systems from the expendable bathythermograph (XBT) to the Argo. There has been a decrease in XBT observations, and a significant increase in Argo profiling floats in the global ocean. However, numerical XBT observation evaluations during this transition period have been under presented. This study investigates the XBT use effects on the global ocean observing systems using a coupled data assimilation model developed by the Geophysical Fluid Dynamics Laboratory (GFDL). Results show that the inclusion of XBT data significantly increases the accuracy of heat content and sea level change estimations during the pre-Argo period. During the Argo period, the amount of heat content correction by XBT assimilation is significantly weakened, especially in the upper ocean. However, it remains in the deeper oceans below 700 m depths, which is the residual effects of assimilating XBT data with the pre-Argo period. This study also confirms that although XBT only provides temperature observations mostly in the upper 700 m of the northern hemisphere, it can affect both the temperature and salinity fields of data assimilation systems, especially in the deep and southern oceans, which is also supported by the significant change in steric height.
Cheng, Jun, Zhengyu Liu, and Shaoqing Zhang, et al., March 2016: Reduced interdecadal variability of Atlantic Meridional Overturning Circulation under global warming. Proceedings of the National Academy of Sciences, 113(12), doi:10.1073/pnas.1519827113. [ Abstract ]
The Atlantic Meridional Overturning Circulation (AMOC) is a key component of the climate system, and its interdecadal variability (IV) significantly modulates climate changes around the North Atlantic region and worldwide. We report a robust shortening in period and weakening in amplitude of AMOC-IV in response to future global warming, which may be contributed to by increased oceanic stratification and, in turn, speedup of Rossby wave propagation. This finding sheds light on the mechanism of AMOC-IV responses to varying background climatology and global warming and therefore should contribute significantly to our understanding and projection of future climate changes.
Cheng, Jun, Zhengyu Liu, and Shaoqing Zhang, et al., May 2016: Reply to Parker: Robust response of AMOC interdecadal variability to future intense warming. Proceedings of the National Academy of Sciences, 113(20), doi:10.1073/pnas.1604999113.
Cravatte, Sophie, William S Kessler, Neville Smith, Susan E Wijffels, Lisan Yu, Kentaro Ando, Megan F Cronin, J Thomas Farrar, Eric Guilyardi, Arun Kumar, Tong Lee, Dean Roemmich, Yolande L Serra, Janet Sprintall, Peter G Strutton, Adrienne J Sutton, Ken Takahashi, and Andrew T Wittenberg, December 2016: First Report of TPOS 2020 , GOOS-215, 200pp. [ Abstract ]
Available online at https://tropicalpacific.org/tpos2020-project-archive/reports
Day, J J., Steffen Tietsche, Matthew Collins, William J Hurlin, Masao Ishii, S P E Keeley, D Matei, and Rym Msadek, et al., June 2016: The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set. Geoscientific Model Development, doi:10.5194/gmd-9-2255-2016. [ Abstract ]
Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other
Delworth, Thomas L., and Fanrong Zeng, February 2016: The impact of the North Atlantic Oscillation on climate through its influence on the Atlantic Meridional Overturning Circulation. Journal of Climate, 29(3), doi:10.1175/JCLI-D-15-0396.1. [ Abstract ]
The impact of the North Atlantic Oscillation (NAO) on the Atlantic Meridional Overturning Circulation (AMOC) and large-scale climate is assessed using simulations with three different climate models. Perturbation experiments are conducted in which a pattern of anomalous heat flux corresponding to the NAO is added to the model ocean. Differences between the perturbation experiments and a control illustrate how the model ocean and climate system respond to the NAO. A positive phase of the NAO strengthens the AMOC by extracting heat from the subpolar gyre, thereby increasing deepwater formation, horizontal density gradients, and the AMOC. The flux forcings have the spatial structure of the observed NAO, but the amplitude of the forcing varies in time with distinct periods varying from 2 to 100 years. The response of the AMOC to NAO variations is small at short time scales, but increases up to the dominant time scale of internal AMOC variability (20-30 years for the models used). The amplitude of the AMOC response, and associated oceanic heat transport, is approximately constant as the timescale of the forcing is increased further. In contrast, the response of other properties, such as hemispheric temperature or Arctic sea ice, continues to increase as the time scale of the forcing becomes progressively longer. The larger response is associated with the time integral of the anomalous oceanic heat transport at longer time scales, combined with an increased impact of radiative feedback processes. We show that NAO fluctuations, similar in amplitude to those observed over the last century, can modulate hemispheric temperature by several tenths of a degree.
Pronounced climate changes have occurred since the 1970s, including rapid loss of Arctic sea ice1, large-scale warming2 and increased tropical storm activity3 in the Atlantic. Anthropogenic radiative forcing is likely to have played a major role in these changes4, but the relative influence of anthropogenic forcing and natural variability is not well established. The above changes have also occurred during a period in which the North Atlantic Oscillation has shown marked multidecadal variations5. Here we investigate the role of the North Atlantic Oscillation in these rapid changes through its influence on the Atlantic meridional overturning circulation and ocean heat transport. We use climate models to show that observed multidecadal variations of the North Atlantic Oscillation can induce multidecadal variations in the Atlantic meridional overturning circulation and poleward ocean heat transport in the Atlantic, extending to the Arctic. Our results suggest that these variations have contributed to the rapid loss of Arctic sea ice, Northern Hemisphere warming, and changing Atlantic tropical storm activity, especially in the late 1990s and early 2000s. These multidecadal variations are superimposed on long-term anthropogenic forcing trends that are the dominant factor in long-term Arctic sea ice loss and hemispheric warming.
Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.
Fu, H, X Wu, W Li, Yuanfu Xie, G Han, and Shaoqing Zhang, June 2016: Reconstruction of Typhoon Structure Using 3-Dimensional Doppler Radar Radial Velocity Data with the Multigrid Analysis: A Case Study in an Idealized Simulation Context. Advances in Meteorology, 2170746, doi:10.1155/2016/2170746. [ Abstract ]
Extracting multiple-scale observational information is critical for accurately reconstructing the structure of mesoscale circulation systems such as typhoon. The Space and Time Mesoscale Analysis System (STMAS) with multigrid data assimilation developed in Earth System Research Laboratory (ESRL) in National Oceanic and Atmospheric Administration (NOAA) has addressed this issue. Previous studies have shown the capability of STMAS to retrieve multiscale information in 2-dimensional Doppler radar radial velocity observations. This study explores the application of 3-dimensional (3D) Doppler radar radial velocities with STMAS for reconstructing a 3D typhoon structure. As for the first step, here, we use an idealized simulation framework. A two-scale simulated “typhoon” field is constructed and referred to as “truth,” from which randomly distributed conventional wind data and 3D Doppler radar radial wind data are generated. These data are used to reconstruct the synthetic 3D “typhoon” structure by the STMAS and the traditional 3D variational (3D-Var) analysis. The degree by which the “truth” 3D typhoon structure is recovered is an assessment of the impact of the data type or analysis scheme being evaluated. We also examine the effects of weak constraint and strong constraint on STMAS analyses. Results show that while the STMAS is superior to the traditional 3D-Var for reconstructing the 3D typhoon structure, the strong constraint STMAS can produce better analyses on both horizontal and vertical velocities.
Gregory, Jonathan M., N Bouttes-Mauhourat, Stephen M Griffies, Helmuth Haak, William J Hurlin, J H Jungclaus, M Kelley, W G Lee, J Marshall, Anastasia Romanou, Oleg A Saenko, Detlef Stammer, and Michael Winton, November 2016: The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) contribution to CMIP6: Investigation of sea-level and ocean climate change in response to CO2 forcing. Geoscientific Model Development, 9(11), doi:10.5194/gmd-9-3993-2016. [ Abstract ]
The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate the spread in simulations of sea-level and ocean climate change in response to CO2 forcing by atmosphere-ocean general circulation models (AOGCMs). It is particularly motivated by the uncertainties in projections of ocean heat uptake, global-mean sea-level rise due to thermal expansion and the geographical patterns of sea-level change due to ocean density and circulation change. FAFMIP has three tier-1 experiments, in which prescribed surface flux perturbations of momentum, heat and freshwater respectively are applied to the ocean in separate AOGCM simulations. All other conditions are as in the pre-industrial control. The prescribed fields are typical of pattern and magnitude of changes in these fluxes projected by AOGCMs for doubled CO2 concentration. Five groups have tested the experimental design with existing AOGCMs. Their results show diversity in the pattern and magnitude of changes, with some common qualitative features. Heat and water flux perturbation cause the dipole in sea-level change in the North Atlantic, while momentum and heat flux perturbation cause the gradient across the Antarctic Circumpolar Current. The Atlantic Meridional Overturning Circulation (AMOC) declines in response to the heat flux perturbation, and there is a strong positive feedback on this effect due to the consequent cooling of sea surface temperature in the North Atlantic, which enhances the local heat input to the ocean. The momentum and water flux perturbations do not substantially affect the AMOC. Heat is taken up largely as a passive tracer in the Southern Ocean, which is the region of greatest heat input, but elsewhere heat is actively redistributed towards lower latitude. Future analysis of these and other phenomena with the wider range of CMIP6 FAFMIP AOGCMs will benefit from new diagnostics of temperature and salinity tendencies, which will enable investigation of the model spread in behaviour in terms of physical processes as formulated in the models.
Griffies, Stephen M., Gokhan Danabasoglu, Paul J Durack, Alistair Adcroft, V Balaji, C Böning, Eric P Chassignet, Enrique N Curchitser, Julie Deshayes, H Drange, Baylor Fox-Kemper, Peter J Gleckler, Jonathan M Gregory, Helmuth Haak, Robert Hallberg, Helene T Hewitt, David M Holland, Tatiana Ilyina, J H Jungclaus, Y Komuro, John P Krasting, William G Large, S J Marsland, S Masina, Trevor J McDougall, A J George Nurser, James C Orr, Anna Pirani, Fangli Qiao, Ronald J Stouffer, Karl E Taylor, A M Treguier, Hiroyuki Tsujino, P Uotila, M Valdivieso, Michael Winton, and Stephen G Yeager, September 2016: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project. Geoscientific Model Development, 9(9), doi:10.5194/gmd-9-3231-2016. [ Abstract ]
The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). OMIP addresses these aims in two complementary manners: (A) by providing an experimental protocol for global ocean/sea-ice models run with a prescribed atmospheric forcing, (B) by providing a protocol for ocean diagnostics to be saved as part of CMIP6. We focus here on the physical component of OMIP, with a companion paper (Orr et al., 2016) offering details for the inert chemistry and interactive biogeochemistry. The physical portion of the OMIP experimental protocol follows that of the interannual Coordinated Ocean-ice Reference Experiments (CORE-II). Since 2009, CORE-I (Normal Year Forcing) and CORE-II have become the standard method to evaluate global ocean/sea-ice simulations and to examine mechanisms for forced ocean climate variability. The OMIP diagnostic protocol is relevant for any ocean model component of CMIP6, including the DECK (Diagnostic, Evaluation and Characterization of Klima experiments), historical simulations, FAFMIP (Flux Anomaly Forced MIP), C4MIP (Coupled Carbon Cycle Climate MIP), DAMIP (Detection and Attribution MIP), DCPP (Decadal Climate Prediction Project), ScenarioMIP (Scenario MIP), as well as the ocean-sea ice OMIP simulations. The bulk of this paper offers scientific rationale for saving these diagnostics.
Guilyardi, Eric, and Andrew T Wittenberg, et al., May 2016: ENSO in a Changing Climate - Meeting summary of the 4th CLIVAR Workshop on the Evaluation of ENSO Processes in Climate Models. Bulletin of the American Meteorological Society, 97(5), doi:10.1175/BAMS-D-15-00287.1.
Han, R, H Wang, Zeng-Zhen Hu, Arun Kumar, W Li, L Long, J-K E Schemm, P Peng, Wanqui Wang, D Si, X Jia, Ming Zhao, and Gabriel A Vecchi, et al., September 2016: An assessment of multi-model simulations for the variability of western North Pacific tropical cyclones and its association with ENSO. Journal of Climate, 29(18), doi:10.1175/JCLI-D-15-0720.1. [ Abstract ]
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature, and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs and the multi-model ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.
Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño, namely eastern Pacific (EP) and central Pacific (CP) El Niño, and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.
This study investigates the roles of radiative forcing, sea surface temperatures (SSTs), and atmospheric and land initial conditions in the summer warming episodes of the United States. The summer warming episodes are defined as the significantly above normal (1983-2012) June-August 2-m temperature anomalies, and are referred to as heat waves in this study. Two contrasting cases, the summers of 2006 and 2012, are explored in detail to illustrate the distinct roles of SSTs, direct radiative forcing, and atmospheric and land initial conditions in driving U.S. summer heat waves. For 2012, simulations with the GFDL atmospheric general circulation model reveal that SSTs play a critical role. Further sensitivity experiments reveal the contributions of uniform global SST warming, SSTs in individual ocean basins and direct radiative forcing to the geographic distribution and magnitudes of warm temperature anomalies. In contrast, for 2006, the atmospheric and land initial conditions are key drivers. The atmospheric (land) initial conditions play a major (minor) role in the central and northwestern (eastern) U.S.. Due to changes in radiative forcing, the probability of areal-averaged summer temperature anomalies over U.S. exceeding the observed 2012 anomaly increases with time over the early 21st century. La Niña (El Niño) events tend to increase (reduce) the occurrence rate of heat waves. The temperatures over the central U.S. are mostly influenced by El Niño/La Niña, with the central tropical Pacific playing a more important role than the eastern tropical Pacific. Thus, atmospheric and land initial conditions, SSTs and radiative forcing are all important drivers of, and sources of predictability for U.S. summer heat waves.
Kam, Jonghun, Thomas R Knutson, Fanrong Zeng, and Andrew T Wittenberg, December 2016: Multimodel Assessment of Anthropogenic Influence on Record Global and Regional Warmth During 2015, Section 2 of “[Explaining extreme events of 2015 from a climate perspective]”. Bulletin of the American Meteorological Society, 97(12), doi:10.1175/BAMS-D-16-0138.1S4-S8.
The seasonal variability of the mean kinetic energy (MKE) and eddy kinetic energy (EKE) of the Gulf Stream (GS) is examined using high-resolution regional ocean model simulations. A set of three numerical experiments with different surface wind and buoyancy forcing is analyzed to investigate the mechanisms governing the seasonal cycle of upper ocean energetics. In the GS along-coast region, MKE has a significant seasonal cycle that peaks in summer, while EKE has two comparable peaks in May and September near the surface; The May peak decays rapidly with depth. In the off-coast region, MKE has a weak seasonal cycle that peaks in summer, while EKE has a dominant peak in May and a secondary peak in September near the surface. The May peak also decays with depth leaving the September peak as the only seasonal signal below 100m. An analysis of the three numerical experiments suggests that the seasonal variability in the local wind forcing significantly impacts the September peak of the along-coast EKE through a local-flow barotropic instability process. Alternatively, the seasonal buoyancy forcing primarily impacts the flow baroclinic instability and is consequently related to the May peak of the upper ocean EKE in both regions. The analysis results indicate that the seasonal cycle of the along-coast MKE is influenced by both local energy generation by wind and the advection of energy from upstream regions. Finally, the MKE cycle and the September peak of EKE in the off-coast region are mainly affected by advection of energy from remote regions, giving rise to correlations with the seasonal cycle of remote winds.
One of the most consequential impacts of anthropogenic warming on humans may be increased heat stress, combining temperature and humidity effects. Here we examine whether there are now detectable changes in summertime heat stress over land regions. As a heat stress metric we use a simplified wet bulb globe temperature (WBGT) index. Observed trends in WBGT (1973–2012) are compared to trends from CMIP5 historical simulations (eight-model ensemble) using either anthropogenic and natural forcing agents combined or natural forcings alone. Our analysis suggests that there has been a detectable anthropogenic increase in mean summertime heat stress since 1973, both globally and in most land regions analyzed. A detectable increase is found over a larger fraction of land for WBGT than for temperature, as WBGT summertime means have lower interannual variability than surface temperature at gridbox scales. Notably, summertime WBGT over land has continued increasing in recent years--consistent with climate models--despite the apparent ‘hiatus’ in global warming and despite a decreasing tendency in observed relative humidity over land since the late 1990s.
Global mean temperature over 1998 to 2015 increased at a slower rate (0.1 K decade−1) compared with the ensemble mean (forced) warming rate projected by Coupled Model Intercomparison Project 5 (CMIP5) models (0.2 K decade−1). Here we investigate the prospects for this slower rate to persist for a decade or more. The slower rate could persist if the transient climate response is overestimated by CMIP5 models by a factor of two, as suggested by recent low-end estimates. Alternatively, using CMIP5 models’ warming rate, the slower rate could still persist due to strong multidecadal internal variability cooling. Combining the CMIP5 ensemble warming rate with internal variability episodes from a single climate model—having the strongest multidecadal variability among CMIP5 models—we estimate that the warming slowdown (<0.1 K decade−1 trend beginning in 1998) could persist, due to internal variability cooling, through 2020, 2025 or 2030 with probabilities 16%, 11% and 6%, respectively.
Kossin, James, Kerry A Emanuel, and Gabriel A Vecchi, June 2016: Comment on 'Roles of interbasin frequency changes in the poleward shifts of the maximum intensity location of tropical cyclones'. Environmental Research Letters, 11(6), doi:10.1088/1748-9326/11/6/068001.
Tropical cyclone (TC) activity in the North Pacific and North Atlantic Oceans is known to be affected by the El Niño Southern Oscillation (ENSO). This study uses GFDL FLOR model, which has relatively high-resolution in the atmosphere, as a tool to investigate the sensitivity of TC activity to the strength of ENSO events. We show that TCs exhibit a non-linear response to the strength of ENSO in the tropical eastern North Pacific (ENP) but a quasi-linear response in the tropical western North Pacific (WNP) and tropical North Atlantic. Specifically, stronger El Niño results in disproportionate inhibition of TCs in the ENP and North Atlantic, and leads to an eastward shift in the location of TCs in the southeast of the WNP. However, the character of the response of TCs in the Pacific is insensitive to the amplitude of La Niña events. The eastward shift of TCs in the southeast of the WNP in response to a strong El Niño is due to an eastward shift of the convection and of the associated environmental conditions favorable for TCs. The inhibition of TC activity in the ENP and Atlantic during El Niño is attributed to the increase in the number of days with strong vertical wind shear during stronger El Niño events. These results are further substantiated with coupled model experiments. Understanding of the impact of strong ENSO on TC activity is important for present and future climate as the frequency of occurrence of extreme ENSO events is projected to increase in future.
Kundzewicz, Z W., V Krysanova, R Dankers, Y Hirabayashi, Shinjiro Kanae, F F Hattermann, S Huang, and P C D Milly, et al., October 2016: Differences in flood hazard projections in Europe - their causes and consequences for decision making. Hydrological Sciences, 62(1), doi:10.1080/02626667.2016.1241398. [ Abstract ]
This paper interprets differences in flood hazard projections over Europe and identifies likely sources of discrepancy. Further, it discusses potential implications of these differences for flood risk reduction and adaptation to climate change. The discrepancy in flood hazard projections raises caution, especially among decision makers in charge of water resources management, flood risk reduction, and climate change adaptation at regional to local scales. Because it is naïve to expect availability of trustworthy quantitative projections of future flood hazard, in order to reduce flood risk one should focus attention on mapping of current and future risks and vulnerability hotspots and improve the situation there. Although an intercomparison of flood hazard projections is done in this paper and differences are identified and interpreted, it does not seems possible to recommend which large-scale studies may be considered most credible in particular areas of Europe.
Lee, Sang-Ki, Andrew T Wittenberg, D B Enfield, S J Weaver, Chunzai Wang, and R Atlas, April 2016: US regional tornado outbreaks and their links to spring ENSO phases and North Atlantic SST variability. Environmental Research Letters, 11(4), doi:10.1088/1748-9326/11/4/044008. [ Abstract ]
Recent violent and widespread tornado outbreaks in the US, such as occurred in the spring of 2011,
have caused devastating societal impact with significant loss of life and property. At present, our
capacity to predict US tornado and other severe weather risk does not extend beyond seven days. In an
effort to advance our capability for developing a skillful long-range outlook for US tornado outbreaks,
here we investigate the spring probability patterns of US regional tornado outbreaks during
1950–2014.Weshow that the four dominant springtime El Niño-Southern Oscillation (ENSO) phases
(persistent versus early-terminating El Niño and resurgent versus transitioning La Niña) and the
North Atlantic sea surface temperature tripole variability are linked to distinct and significant US
regional patterns of outbreak probability. These changes in the probability of outbreaks are shown to
be largely consistent with remotely forced regional changes in the large-scale atmospheric processes
conducive to tornado outbreaks. An implication of these findings is that the springtime ENSO phases
and the North Atlantic SST tripole variability may provide seasonal predictability of US regional
tornado outbreaks.
Despite 30 years of basin-wide nutrient-reduction efforts, severe hypoxia continues to be observed in the Chesapeake Bay. Here we demonstrate the critical influence of climate variability, interacting with accumulated nitrogen (N) over multidecades, on Susquehanna River dissolved nitrogen (DN) loads, known precursors of the hypoxia in the Bay. We used the process model LM3-TAN (Terrestrial and Aquatic Nitrogen), which is capable of capturing both seasonal and decadal-to-century changes in vegetation-soil-river N storage, and produced nine scenarios of DN-load distributions under different short-term scenarios of climate variability and extremes. We illustrate that after 1 to 3 yearlong dry spells, the likelihood of exceeding a threshold DN load (56 kt yr−1) increases by 40 to 65% due to flushing of N accumulated throughout the dry spells and altered microbial processes. Our analyses suggest that possible future increases in climate variability/extremes—specifically, high precipitation occurring after multiyear dry spells—could likely lead to high DN-load anomalies and hypoxia.
Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that has empirically-set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model which includes large-scale feedbacks for cumulus convection, and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.
Liu, H, Feiyu Lu, Zhengyu Liu, Y Liu, and Shaoqing Zhang, June 2016: Assimilating atmosphere reanalysis in coupled data assimilation. Journal of Meteorological Research, 30(4), doi:10.1007/s13351-016-6014-1. [ Abstract ]
This paper tests the idea of substituting the atmospheric observations with atmospheric reanalysis when setting up a coupled data assimilation system. The paper focuses on the quantification of the effects on the oceanic analysis resulted from this substitution and designs four different assimilation schemes for such a substitution. A coupled Lorenz96 system is constructed and an ensemble Kalman filter is adopted. The atmospheric reanalysis and oceanic observations are assimilated into the system and the analysis quality is compared to a benchmark experiment where both atmospheric and oceanic observations are assimilated. Four schemes are designed for assimilating the reanalysis and they differ in the generation of the perturbed observation ensemble and the representation of the error covariance matrix. The results show that when the reanalysis is assimilated directly as independent observations, the root-mean-square error increase of oceanic analysis relative to the benchmark is less than 16% in the perfect model framework; in the biased model case, the increase is less than 22%. This result is robust with sufficient ensemble size and reasonable atmospheric observation quality (e.g., frequency, noisiness, and density). If the observation is overly noisy, infrequent, sparse, or the ensemble size is insufficiently small, the analysis deterioration caused by the substitution is less severe since the analysis quality of the benchmark also deteriorates significantly due to worse observations and undersampling. The results from different assimilation schemes highlight the importance of two factors: accurate representation of the error covariance of the reanalysis and the temporal coherence along each ensemble member, which are crucial for the analysis quality of the substitution experiment.
Lorenz, R, D Argueeso, Markus G Donat, A J Pitman, Bart van den Hurk, Alexis Berg, David Lawrence, F Cheruy, A Ducharne, S Hagemann, A Meier, and P C D Milly, et al., January 2016: Influence of land‐atmosphere feedbacks on temperature and precipitation extremes in the GLACE‐CMIP5 ensemble. Journal of Geophysical Research: Atmospheres, 121(2), doi:10.1002/2015JD024053. [ Abstract ]
We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land‐Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE‐CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business‐as‐usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes.
By various measures (drought area1 and intensity2, climatic aridity index3, and climatic water deficits4), some observational analyses have suggested that much of the Earth’s land has been drying during recent decades, but such drying seems inconsistent with observations of dryland greening and decreasing pan evaporation5. ‘Offline’ analyses of climate-model outputs from anthropogenic climate change (ACC) experiments portend continuation of putative drying through the twenty-first century3, 6, 7, 8, 9, 10, despite an expected increase in global land precipitation9. A ubiquitous increase in estimates of potential evapotranspiration (PET), driven by atmospheric warming11, underlies the drying trends4, 8, 9, 12, but may be a methodological artefact5. Here we show that the PET estimator commonly used (the Penman–Monteith PET13 for either an open-water surface1, 2, 6, 7, 12 or a reference crop3, 4, 8, 9, 11) severely overpredicts the changes in non-water-stressed evapotranspiration computed in the climate models themselves in ACC experiments. This overprediction is partially due to neglect of stomatal conductance reductions commonly induced by increasing atmospheric CO2 concentrations in climate models5. Our findings imply that historical and future tendencies towards continental drying, as characterized by offline-computed runoff, as well as other PET-dependent metrics, may be considerably weaker and less extensive than previously thought.
The Southern Ocean plays a dominant role in anthropogenic oceanic heat uptake. Strong northward transport of the heat content anomaly limits warming of the sea surface temperature in the uptake region and allows the heat uptake to be sustained. Using an eddy-rich global climate model, the processes controlling the northward transport and convergence of the heat anomaly in the mid-latitude Southern Ocean are investigated in an idealized 1% yr−1 increasing CO2 simulation. Heat budget analyses reveal that different processes dominate to the north and south of the main convergence region. The heat transport northward from the uptake region in the south is driven primarily by passive advection of the heat content anomaly by the existing time mean circulation, with a smaller 20% contribution from enhanced upwelling. The heat anomaly converges in the mid-latitude deep mixed layers, because there is not a corresponding increase in the mean heat transport out of the deep mixed layers northward into the mode waters. To the north of the deep mixed layers, eddy processes drive the warming and account for nearly 80% of the northward heat transport anomaly. The eddy transport mechanism results from a reduction in both the diffusive and advective southward eddy heat transports, driven by decreasing isopycnal slopes and decreasing along-isopycnal temperature gradients on the northern edge of the peak warming.
Retrospective seasonal forecasts of North Atlantic tropical cyclone (TC) activity over the period 1980–2014 are conducted using a GFDL high-resolution coupled climate model [Forecast-Oriented Low Ocean Resolution (FLOR)]. The focus is on basin-total TC and U.S. landfall frequency. The correlations between observed and model predicted basin-total TC counts range from 0.4 to 0.6 depending on the month of the initial forecast. The correlation values for U.S. landfalling activity based on individual TCs tracked from the model are smaller and between 0.1 and 0.4. Given the limited skill from the model, statistical methods are used to complement the dynamical seasonal TC prediction from the FLOR model. Observed and predicted TC tracks were classified into four groups using fuzzy c-mean clustering to evaluate the model’s predictability in observed classification of TC tracks. Analyses revealed that the FLOR model has the highest skill in predicting TC frequency for the cluster of TCs that tracks through the Caribbean and the Gulf of Mexico.
New hybrid models are developed to improve the prediction of observed basin-total TC and landfall TC frequencies. These models use large-scale climate predictors from the FLOR model as predictors for generalized linear models. The hybrid models show considerable improvements in the skill in predicting the basin-total TC frequencies relative to the dynamical model. The new hybrid model shows correlation coefficients as high as 0.75 for basinwide TC counts from the first two lead months and retains values around 0.50 even at the 6-month lead forecast. The hybrid model also shows comparable or higher skill in forecasting U.S. landfalling TCs relative to the dynamical predictions. The correlation coefficient is about 0.5 for the 2–5-month lead times.
Skillful seasonal forecasting of tropical cyclone (TC; wind speed ≥17.5 m s−1) activity is challenging, even more so when the focus is on major hurricanes (wind speed ≥49.4 m s−1), the most intense hurricanes (Category 4–5; wind speed ≥58.1 m s−1), and landfalling TCs. Here we show that a 25-km resolution global coupled model (HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL) has improved skill in predicting the frequencies of major hurricanes and Category 4–5 hurricanes in the North Atlantic, and landfalling TCs over the United States and Caribbean Islands a few months in advance, relative to its 50-km resolution predecessor climate model (FLOR). HiFLOR also shows significant skill in predicting Category 4–5 hurricanes in the western North Pacific and eastern North Pacific, while both models show comparable skills in predicting basin-total and landfalling TC frequency in the basins. The improved skillful forecasts of basin-total TCs, major hurricanes, and Category 4–5 hurricane activity in the North Atlantic by HiFLOR are obtained mainly by improved representation of the TCs and their response to climate from the increased horizontal resolution, rather than improvements in large-scale parameters.
The impact of atmosphere and ocean horizontal resolution on the climatology of North American Monsoon Gulf of California (GoC) moisture surges is examined in a suite of global circulation models (CM2.1, FLOR, CM2.5, CM2.6, HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These models feature essentially the same physical parameterizations, but differ in horizontal resolution in either the atmosphere (≃200, 50 and 25 km) or the ocean (≃1°, 0.25°, 0.1°). Increasing horizontal atmospheric resolution from 200 km to 50 km results in a drastic improvement in the model’s capability of accurately simulating surge events. The climatological near-surface flow and moisture and precipitation anomalies associated with GoC surges are overall satisfactorily simulated in all higher-resolution models. The number of surge events agrees well with reanalyses but models tend to underestimate July-August surge-related precipitation and overestimate September surge-related rainfall in the southwestern United States. Large-scale controls supporting the development of GoC surges, such as tropical easterly waves (TEWs), tropical cyclones (TCs) and trans-Pacific Rossby wave trains (RWTs), are also well captured, although models tend to underestimate the TEW/TC magnitude and number. Near-surface GoC surge features and their large-scale forcings (TEWs, TCs, RWTs) do not appear to be substantially affected by a finer representation of the GoC at higher ocean resolution. However, the substantial reduction of the eastern Pacific warm sea surface temperature bias through flux adjustment in the FLOR model leads to an overall improvement of tropical-extratropical controls on GoC moisture surges and the seasonal cycle of precipitation in the southwestern United States.
The Intergovernmental Panel on Climate Change (IPCC) fifth assessment of projected global and regional ocean temperature change is based on global climate models that have coarse (∼100-km) ocean and atmosphere resolutions. In the Northwest Atlantic, the ensemble of global climate models has a warm bias in sea surface temperature due to a misrepresentation of the Gulf Stream position; thus, existing climate change projections are based on unrealistic regional ocean circulation. Here we compare simulations and an atmospheric CO2 doubling response from four global climate models of varying ocean and atmosphere resolution. We find that the highest resolution climate model (∼10-km ocean, ∼50-km atmosphere) resolves Northwest Atlantic circulation and water mass distribution most accurately. The CO2 doubling response from this model shows that upper-ocean (0-300 m) temperature in the Northwest Atlantic Shelf warms at a rate nearly twice as fast as the coarser models and nearly three times faster than the global average. This enhanced warming is accompanied by an increase in salinity due to a change in water mass distribution that is related to a retreat of the Labrador Current and a northerly shift of the Gulf Stream. Both observations and the climate model demonstrate a robust relationship between a weakening Atlantic Meridional Overturning Circulation (AMOC) and an increase in the proportion of Warm-Temperate Slope Water entering the Northwest Atlantic Shelf. Therefore, prior climate change projections for the Northwest Atlantic may be far too conservative. These results point to the need to improve simulations of basin and regional-scale ocean circulation.
Tian, D, Ming Pan, Liwei Jia, Gabriel A Vecchi, and Eric F Wood, July 2016: Assessing GFDL High-Resolution Climate Model Water and Energy Budgets from AMIP simulations over Africa. Journal of Geophysical Research: Atmospheres, 121(14), doi:10.1002/2016JD025068. [ Abstract ]
This study assessed surface water and energy budgets in Atmospheric Model Intercomparison Project (AMIP) simulations of a coupled atmosphere-land model developed by Geophysical Fluid Dynamics Laboratory [Atmospheric General Circulation Model (AM2.5)]. The AM2.5 water and energy budget variables were compared with four reanalyses datasets and an observational-based reference, the Variable Infiltration Capacity model simulations forced by Princeton Global Meteorological Forcing (PGF/VIC) over 20-year period during 1991-2010 in nine African river basins. Results showed that AM2.5 have closed water and energy budgets. However, the discrepancies between AM2.5 and other datasets were notable in terms of their long-term averages. For the water budget, the AM2.5 mostly overestimated precipitation, evapotranspiration, and runoff compared to PGF/VIC and reanalyses. The AM2.5, reanalyses, and PGF/VIC showed similar seasonal cycles but discrepant amplitudes. For the energy budget, while the AM2.5 has relatively consistent net radiation with other datasets, it generally showed higher latent heat, lower sensible heat, and lower Bowen ratio than reanalyses and PGF/VIC. In addition, the AM2.5 water and energy budgets terms mostly had the smallest interannual variability compared to both reanalyses and PGF/VIC. The spatial differences of long-term mean precipitation, runoff, evapotranspiration, and latent heat between AM2.5 and other datasets were reasonably small in dry regions. On average, AM2.5 is closer to PGF/VIC than R2 and 20CR are to PGF/VIC, but is not as close as MERRA and CFSR to PGF/VIC. The bias in AM2.5 water and energy budget terms may be associated with the excessive wet surface and parameterization of moisture advection from ocean to land.
The GFDL hurricane modelling system, initiated in the 1970s, has progressed from a research tool to an operational system over four decades. This system is still in use today in research and operations, and its evolution will be briefly described. This study used an idealized version of the 2014 GFDL model to test its sensitivity across a wide range of three environmental factors that are often identified as key factors in tropical cyclone (TC) evolution: SST, atmospheric stability (upper air thermal anomalies), and vertical wind shear (westerly through easterly). A wide range of minimum central pressure intensities resulted (905 to 980hPa). The results confirm that a scenario (e.g., global warming) in which the upper troposphere warms relative to the surface will have less TC intensification than one with a uniform warming with height. TC rainfall is also investigated for the SST-stability parameter space. Rainfall increases for combinations of SST increase and increasing stability similar to global warming scenarios, consistent with climate change TC downscaling studies with the GFDL model. The forecast system’s sensitivity to vertical shear was also investigated. The idealized model simulations showed weak disturbances dissipating under strong easterly and westerly shear of 10 m s-1. A small bias for greater intensity under easterly sheared versus westerly sheared environments was found at lower values of SST. The impact of vertical shear on intensity was different when a strong vortex was used in the simulations. In this case none of the initial disturbances weakened, and most intensified to some extent.
Precipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2°×2° grid cells (typical resolution in the CMIP5 archive) to 0.25°×.25° (tropical cyclone-permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities and seasonal timing. In response to 2×CO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of approximately 3-4% K−1. However, projected regional patterns of changes in extremes are dependent on model resolution. For example, the highest-resolution models show increased precipitation rates during extreme events in the hurricane season in the CONUS southeast, this increase is not found in the low-resolution model. These results emphasize that, for the study of extreme precipitation there is a minimum model resolution that is needed to capture the weather phenomena generating the extremes. Finally, the observed record and historical model experiments were used to investigate changes in the recent past. In part because of large intrinsic variability, no evidence was found for changes in extreme precipitation attributable to climate change in the available observed record.
Walsh, Kevin J., J McBride, Philip J Klotzbach, S Balachandran, Suzana J Camargo, G Holland, Thomas R Knutson, James Kossin, Tsz-Cheung Lee, Adam H Sobel, and M Sugi, January 2016: Tropical cyclones and climate change. Wiley Interdisciplinary Reviews: Climate Change, 7(1), doi:10.1002/wcc.371. [ Abstract ]
Recent research has strengthened the understanding of the links between climate and tropical cyclones (TCs) on various timescales. Geological records of past climates have shown century-long variations in TC numbers. While no significant trends have been identified in the Atlantic since the late 19th century, significant observed trends in TC numbers and intensities have occurred in this basin over the past few decades, and trends in other basins are increasingly being identified. However, understanding of the causes of these trends is incomplete, and confidence in these trends continues to be hampered by a lack of consistent observations in some basins. A theoretical basis for maximum TC intensity appears now to be well established, but a climate theory of TC formation remains elusive. Climate models mostly continue to predict future decreases in global TC numbers, projected increases in the intensities of the strongest storms and increased rainfall rates. Sea level rise will likely contribute toward increased storm surge risk. Against the background of global climate change and sea level rise, it is important to carry out quantitative assessments on the potential risk of TC-induced storm surge and flooding to densely populated cities and river deltas. Several climate models are now able to generate a good distribution of both TC numbers and intensities in the current climate. Inconsistent TC projection results emerge from modeling studies due to different downscaling methodologies and warming scenarios, inconsistencies in projected changes of large-scale conditions, and differences in model physics and tracking algorithms.
Connections between wildfires and modes of variability in climate are sought as a means for predicting fire activity on interannual to multi-decadal timescales. Several fire drivers, such as temperature and local drought index, have been shown to vary on these timescales, and analysis of tree-ring data suggests covariance between fires and climate oscillation indices in some regions. However, the shortness of the satellite record of global fire events limits investigations on larger spatial scales. Here we explore the interplay between climate variability and wildfire emissions with the preindustrial long control numerical experiments and historical ensembles of CESM1 and the NOAA/GFDL ESM2Mb. We find that interannual variability in fires is underpredicted in both Earth System models (ESMs) compared to present day fire emission inventories. Modeled fire emissions respond to the El Niño/southern oscillation (ENSO) and Pacific decadal oscillation (PDO) with increases in southeast Asia and boreal North America emissions, and decreases in southern North America and Sahel emissions, during the ENSO warm phase in both ESMs, and the PDO warm phase in CESM1. Additionally, CESM1 produces decreases in boreal northern hemisphere fire emissions for the warm phase of the Atlantic Meridional Oscillation. Through analysis of the long control simulations, we show that the 20th century trends in both ESMs are statistically significant, meaning that the signal of anthropogenic activity on fire emissions over this time period is detectable above the annual to decadal timescale noise. However, the trends simulated by the two ESMs are of opposite sign (CESM1 decreasing, ESM2Mb increasing), highlighting the need for improved understanding, proxy observations, and modeling to resolve this discrepancy.
Williamson, T N., E A Nystrom, and P C D Milly, November 2016: Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration. Climatic Change, 139(2), doi:10.1007/s10584-016-1782-2. [ Abstract ]
The Delaware River Basin (DRB) encompasses approximately 0.4 % of the area of the United States (U.S.), but supplies water to 5 % of the population. We studied three forested tributaries to quantify the potential climate-driven change in hydrologic budget for two 25-year time periods centered on 2030 and 2060, focusing on sensitivity to the method of estimating potential evapotranspiration (PET) change. Hydrology was simulated using the Water Availability Tool for Environmental Resources (Williamson et al. 2015). Climate-change scenarios for four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and two Representative Concentration Pathways (RCPs) were used to derive monthly change factors for temperature (T), precipitation (PPT), and PET according to the energy-based method of Priestley and Taylor (1972). Hydrologic simulations indicate a general increase in annual (especially winter) streamflow (Q) as early as 2030 across the DRB, with a larger increase by 2060. This increase in Q is the result of (1) higher winter PPT, which outweighs an annual actual evapotranspiration (AET) increase and (2) (for winter) a major shift away from storage of PPT as snow pack. However, when PET change is evaluated instead using the simpler T-based method of Hamon (1963), the increases in Q are small or even negative. In fact, the change of Q depends as much on PET method as on time period or RCP. This large sensitivity and associated uncertainty underscore the importance of exercising caution in the selection of a PET method for use in climate-change analyses.
Wu, Xinrong, G Han, Shaoqing Zhang, and Zhengyu Liu, February 2016: A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model. Climate Dynamics, 46(3-4), doi:10.1007/s00382-015-2608-z. [ Abstract ]
Model error is a major obstacle for enhancing the forecast skill of El Niño-Southern Oscillation (ENSO). Among three kinds of model error sources—dynamical core misfitting, physical scheme approximation and model parameter errors, the model parameter errors are treatable by observations. Based on the Zebiak-Cane model, an ensemble coupled data assimilation system is established to study the impact of parameter optimization (PO) on ENSO predictions within a biased twin experiment framework. “Observations” of sea surface temperature anomalies drawn from the “truth” model are assimilated into a biased prediction model in which model parameters are erroneously set from the “truth” values. The degree by which the assimilation and prediction with or without PO recover the “truth” is a measure of the impact of PO. Results show that PO improves ENSO predictability—enhancing the seasonal-interannual forecast skill by about 18 %, extending the valid lead time up to 33 % and ameliorating the spring predictability barrier. Although derived from idealized twin experiments, results here provide some insights when a coupled general circulation model is initialized from the observing system.
Wu, Xinrong, Shaoqing Zhang, and Zhengyu Liu, February 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies. Advances in Atmospheric Sciences, 33(2), doi:10.1007/s00376-015-5099-2. [ Abstract ]
To further explore enthalpy-based sea-ice assimilation, a one-dimensional (1D) enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model.
http://159.226.119.58/aas/EN/10.1007/s00376-015-5099-2
This study investigates the association between the Pacific Meridional Mode (PMM) and tropical cyclone (TC) activity in the western North Pacific (WNP). It is found that the positive PMM phase favors the occurrence of TCs in the WNP while the negative PMM phase inhibits the occurrence of TCs there. Observed relationships are consistent with those from a long-term pre-industrial control experiment (1000 years) of a high-resolution TC-resolving Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model. The diagnostic relationship between the PMM and TCs in observations and the model is further supported by sensitivity experiments with FLOR. The modulation of TC genesis by the PMM is primarily through the anomalous zonal vertical wind shear (ZVWS) changes in the WNP, especially in the southeastern WNP. The anomalous ZVWS can be attributed to the responses of the atmosphere to the anomalous warming in the northwestern part of the PMM pattern during the positive PMM phase, which resembles a classic Matsuno-Gill pattern. Such influences on TC genesis are strengthened by a cyclonic flow over the WNP. The significant relationship between TCs and the PMM identified here may provide a useful reference for seasonal forecasting of TCs and interpreting changes in TC activity in the WNP.
This study aims to assess whether, and the extent to which, an increase in atmospheric resolution in versions of the Geophysical Fluid Dynamics Laboratory (GFDL) High-Resolution Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) with 50 km and HiFLOR with 25 km improves the simulation of the El Niño Southern Oscillation-tropical cyclone (ENSO-TC) connections in the western North Pacific (WNP). HiFLOR simulates better ENSO-TC connections in the WNP including TC track density, genesis and landfall than FLOR in both long-term control experiments and sea surface temperature (SST)- and sea surface salinity (SSS)-restoring historical runs (1971-2012). Restoring experiments are performed with SSS and SST restored to observational estimates of climatological SSS and interannually-varying monthly SST. In the control experiments of HiFLOR, an improved simulation of the Walker circulation arising from more realistic SST and precipitation is largely responsible for its better performance in simulating ENSO-TC connections in the WNP. In the SST-restoring experiments of HiFLOR, more realistic Walker circulation and steering flow during El Niño/La Niña are responsible for the improved simulation of ENSO-TC connections in the WNP. The improved simulation of ENSO-TC connections with HiFLOR arises from a better representation of SST and better responses of environmental large-scale circulation to SST anomalies associated with El Niño/La Niña. A better representation of ENSO-TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track and landfall, improve our understanding of the interannual variation of TC activity, and provide better projection of TC activity under climate change.
Zhang, X, and Shaoqing Zhang, et al., September 2016: Correction of biased climate simulated by biased physics through parameter estimation in an intermediate coupled model. Climate Dynamics, 47(5-6), doi:10.1007/s00382-015-2939-9. [ Abstract ]
Imperfect physical parameterization schemes are an important source of model bias in a coupled model and adversely impact the performance of model simulation. With a coupled ocean-atmosphere-land model of intermediate complexity, the impact of imperfect parameter estimation on model simulation with biased physics has been studied. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation and “truth” models. To mitigate model bias, the parameters employed in the biased longwave radiation scheme are optimized using three different methods: least-squares parameter fitting (LSPF), single-valued parameter estimation and geography-dependent parameter optimization (GPO), the last two of which belong to the coupled model parameter estimation (CMPE) method. While the traditional LSPF method is able to improve the performance of coupled model simulations, the optimized parameter values from the CMPE, which uses the coupled model dynamics to project observational information onto the parameters, further reduce the bias of the simulated climate arising from biased physics. Further, parameters estimated by the GPO method can properly capture the climate-scale signal to improve the simulation of climate variability. These results suggest that the physical parameter estimation via the CMPE scheme is an effective approach to restrain the model climate drift during decadal climate predictions using coupled general circulation models.
The impact of climate change on the Pacific Decadal Oscillation (PDO) is studied using a fully coupled climate model. The model results show that the PDO has a similar spatial pattern in altered climates, but its amplitude and time scale of variability change in response to global warming or cooling. In response to global warming the PDO amplitude is significantly reduced, with a maximum decrease over the Kuroshio-Oyashio-Extension (KOE) region. This reduction appears to be associated with a weakened meridional temperature gradient in the KOE region. In addition, reduced variability of North Pacific wind stress, partially due to reduced air-sea feedback, also helps to weaken the PDO amplitude by reducing the meridional displacements of the subtropical and subpolar gyre boundaries. In contrast, the PDO amplitude increases in response to global cooling.
In our control simulations the model PDO has an approximately bi-decadal peak. In a warmer climate the PDO timescale becomes shorter, changing from approximately 20 years to approximately 12 years. In a colder climate the timescale of the PDO increases to approximately 34 years. Physically, global warming (cooling) enhances (weakens) ocean stratification. The increased (decreased) ocean stratification acts to increase (reduce) the phase speed of internal Rossby waves, thereby altering the timescale of the simulated PDO.
Zhang, Wei, Gabriele Villarini, Gabriel A Vecchi, Hiroyuki Murakami, and Richard G Gudgel, June 2016: Statistical-dynamical seasonal forecast of western North Pacific and East Asia landfalling tropical cyclones using the high-resolution GFDL FLOR coupled model. Journal of Advances in Modeling Earth Systems, 8(2), doi:10.1002/2015MS000607. [ Abstract ]
This study examines the seasonal prediction of western North Pacific [WNP) and East Asia landfalling tropical cyclones (TCs) using the Geophysical Fluid Dynamics Laboratory(GFDL) Forecast-oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR-FA) and finite-mixture-model (FMM)-based statistical cluster analysis. Using the FMM-based cluster analysis, seven clusters are identified from the historical and FLOR-FA-predicted TC tracks for the period 1980–2013. FLOR-FA has significant skill in predicting year-to-year variations in the frequency of TCs within clusters 1 (recurving TCs) and 5 (straight-moving TCs). By building Poisson regression models for each cluster using key predictors (i.e., sea surface temperature, 500 hPa geopotential height, and zonal vertical wind shear), the predictive skill for almost all the clusters at all initialization months improves with respect to the dynamic prediction. The prediction of total WNP TC frequency made by combining hybrid predictions for each of the seven clusters in the hybrid model shows skill higher than what achieved using the TC frequency directly from FLOR-FA initialized from March to July. However, the hybrid predictions for total WNP TC frequency initialized from January to February exhibit lower skill than FLOR-FA. The prediction of TC landfall over East Asia made by combining the hybrid models of TC frequency in each cluster and its landfall rate over East Asia also outperforms FLOR-FA for all initialization months January through July.
Zhang, Rong, Rowan Sutton, Gokhan Danabasoglu, and Thomas L Delworth, et al., June 2016: Comment on “The Atlantic Multidecadal Oscillation without a role for ocean circulation”. Science, 352(6293), doi:10.1126/science.aaf1660. [ Abstract ]
Clement et al. (Reports, 16 October 2015, p. 320) claim that the Atlantic Multidecadal Oscillation (AMO) is a thermodynamic response of the ocean mixed layer to stochastic atmospheric forcing and that ocean circulation changes have no role in causing the AMO. These claims are not justified. We show that ocean dynamics play a central role in the AMO.
Zhang, Liping, and Thomas L Delworth, August 2016: Impact of the Antarctic bottom water formation on the Weddell Gyre and its northward propagation characteristics in GFDL model. Journal of Geophysical Research: Oceans, 121(8), doi:10.1002/2016JC011790. [ Abstract ]
The impact of Antarctic bottom water (AABW) formation on the Weddell Gyre and its northward propagation characteristics are studied using a 4000-yr long control run of the GFDL CM2.1 model as well as sensitivity experiments. In the control run, the AABW cell and Weddell Gyre are highly correlated when the AABW cell leads the Weddell Gyre by several years, with an enhanced AABW cell corresponding to a strengthened Weddell Gyre and vice versa. An additional sensitivity experiment shows that the response of the Weddell Gyre to AABW cell changes is primarily attributed to interactions between the AABW outflow and ocean topography, instead of the surface wind stress curl and freshwater anomalies. As the AABW flows northward, it encounters topography with steep slopes that induce strong downwelling and negative bottom vortex stretching. The anomalous negative bottom vortex stretching induces a cyclonic barotropic streamfunction over the Weddell Sea, thus leading to an enhanced Weddell Gyre. The AABW cell variations in the control run have significant meridional coherence in density space. Using passive dye tracers, it is found that the slow propagation of AABW cell anomalies south of 35oS corresponds to the slow tracer advection time scale. The dye tracers escape the Weddell Sea through the western limb of the Weddell Gyre and then go northwestward to the Argentine Basin through South Sandwich Trench and Georgia Basin. This slow advection by deep ocean currents determines the AABW cell propagation speed south of 35oS. North of 35oS the propagation speed is determined both by advection in the deep western boundary current and through Kelvin waves.
This study aims to assess the connections between the El Niño Southern Oscillation (ENSO) and tropical cyclones near Guam (GuamTC) using the state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR). In observations, more (less) GuamTCs occur in El Niño (La Niña) years and the ENSO-GuamTC connections arise from TC genesis locations in ENSO phases. The observed ENSO-GuamTC connections are realistically simulated in the two control experiments that use two versions of FLOR, the standard version and another with flux adjustments (FLOR-FA). The ENSO-GuamTC connections in FLOR-FA are closer to observations than those in FLOR because of a better representation of TC genesis during ENSO phases. The physical mechanisms underlying the observed ENSO-GuamTC connections are further supported in the long-term control experiments with FLOR/FLOR-FA. The ENSO-GuamTC connections in sea surface temperature (SST)- and sea surface salinity (SSS)-restoring experiments with FLOR 1990 strongly resemble the observations, suggesting the ENSO-GuamTC connections arise substantially from the forcing of SST. The prediction skill of FLOR-FA for GuamTC frequency is quite promising in terms of correlation and root mean square error and is higher than that of FLOR for the period 1980-2014. This study shows the capability of global climate models (FLOR/FLOR-FA) in simulating the linkage between ENSO and TC activity near a highly localized region (i.e., Guam) and in predicting the frequency of TCs at the sub-basin scale.
Observed austral summertime (November through April) rainfall in southeastern South America (SESA)—including northern Argentina, Uruguay, southern Brazil and Paraguay—has exhibited substantial low-frequency variations with a multi-decadal moistening trend during the 20th century and a subsequent decadal drying trend during the current century. Understanding the mechanisms responsible for these variations is essential for predicting long-term rainfall changes. Here with a suite of attribution experiments using a pair of high-resolution global climate models—GFDL CM2.5 and FLOR_FA, we investigate the causes of these regional rainfall variations. Both models reproduce the 20th-century moistening trend, albeit with a weaker magnitude than observed, in response to the radiative forcing associated with increasing greenhouse gases. The increasing greenhouse gases drive tropical expansion; consequently, the subtropical dry branch of Hadley cell moves away from SESA, leading to the rainfall increase. The amplitude discrepancy between the observed and simulated rainfall changes suggests a possible underestimation by the models of the atmospheric response to the radiative forcing, as well as an important role for low-frequency internal variability in the observed moistening trend. Over the current century, increasing greenhouse gases drive a continuous SESA rainfall increase in the models. However, the observed decadal rainfall decline is largely (~60%) reproduced in response to the observed Pacific trade wind strengthening, which is likely associated with natural Pacific decadal variability. These results suggest that the recent summertime rainfall decline in SESA is temporary and the positive trend will resume in response to both increasing greenhouse gases and a return of Pacific trade winds to normal conditions.
Zhang, Wenjun, Fei-Fei Jin, Malte F Stuecker, and Andrew T Wittenberg, et al., November 2016: Unraveling El Niño's Impact on the East Asian Monsoon and Yangtze River Summer Flooding. Geophysical Research Letters, 43(21), doi:10.1002/2016GL071190. [ Abstract ]
Strong El Niño events are followed by massive summer Monsoon flooding over the Yangtze River basin (YRB), home to about a third of the population in China. Although the El Niño-Southern Oscillation (ENSO) provides the main source of seasonal climate predictability for many parts of the Earth, the mechanisms of its connection to the East Asian Monsoon remain largely elusive. For instance, the traditional Niño3.4 ENSO index only captures precipitation anomalies over East Asia in boreal winter but not during the summer. Here we show that there exists a robust year-round and predictable relationship between ENSO and the Asian Monsoon. This connection is revealed by combining equatorial (Niño3.4) and off-equatorial Pacific SST anomalies (Niño-A index) into a new metric that captures ENSO's various aspects, such as its interaction with the annual cycle and its different flavors. This extended view of ENSO complexity improves predictability of YRB summer flooding events.
Berg, Alexis, Benjamin R Lintner, Kirsten L Findell, Sonia I Seneviratne, Bart van den Hurk, A Ducharne, F Cheruy, S Hagemann, David Lawrence, and Sergey Malyshev, et al., February 2015: Interannual coupling between summertime surface temperature and precipitation over land: processes and implications for climate change. Journal of Climate, 28(3), doi:10.1175/JCLI-D-14-00324.1. [ Abstract ]
Widespread negative correlations between summertime-mean temperatures and precipitation over land regions are a well-known feature of terrestrial climate. This behavior has generally been interpreted in the context of soil moisture-atmosphere coupling, with soil moisture deficits associated with reduced rainfall leading to enhanced surface sensible heating and higher surface temperature. The present study revisits the genesis of these negative temperature-precipitation correlations using simulations from the Global Land-Atmosphere Coupling Experiment - Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5) multi-model experiment. The analyses are based on simulations with 5 climate models, which were integrated with prescribed (non-interactive) and with interactive soil moisture over the period 1950-2100. While the results presented here generally confirm the interpretation that negative correlations between seasonal temperature and precipitation arise through the direct control of soil moisture on surface heat flux partitioning, the presence of widespread negative correlations when soil moisture-atmosphere interactions are artificially removed in at least two out of five models suggests that atmospheric processes, in addition to land surface processes, contribute to the observed negative temperature-precipitation correlation. On longer timescales, the negative correlation between precipitation and temperature is shown to have implications for the projection of climate change impacts on near surface climate: in all models, in the regions of strongest temperature-precipitation anti-correlation on interannual timescales, long-term regional warming is modulated to a large extent by the regional response of precipitation to climate change, with precipitation increases (decreases) being associated with minimum (maximum) warming. This correspondence appears to arise largely as the result of soil-moisture atmosphere interactions.
Cai, Wenju, Guojian Wang, Agus Santoso, Michael J McPhaden, L Wu, Fei-Fei Jin, Axel Timmermann, Matthew Collins, Gabriel A Vecchi, Matthieu Lengaigne, Matthew H England, D Dommenget, Ken Takahashi, and Eric Guilyardi, February 2015: Increased frequency of extreme La Niña events under greenhouse warming. Nature Climate Change, 5(2), doi:10.1038/nclimate2492. [ Abstract ]
The El Niño/Southern Oscillation is Earth’s most prominent source of interannual climate variability, alternating irregularly between El Niño and La Niña, and resulting in global disruption of weather patterns, ecosystems, fisheries and agriculture1, 2, 3, 4, 5. The 1998–1999 extreme La Niña event that followed the 1997–1998 extreme El Niño event6 switched extreme El Niño-induced severe droughts to devastating floods in western Pacific countries, and vice versa in the southwestern United States4, 7. During extreme La Niña events, cold sea surface conditions develop in the central Pacific8, 9, creating an enhanced temperature gradient from the Maritime continent to the central Pacific. Recent studies have revealed robust changes in El Niño characteristics in response to simulated future greenhouse warming10, 11, 12, but how La Niña will change remains unclear. Here we present climate modelling evidence, from simulations conducted for the Coupled Model Intercomparison Project phase 5 (ref. 13), for a near doubling in the frequency of future extreme La Niña events, from one in every 23 years to one in every 13 years. This occurs because projected faster mean warming of the Maritime continent than the central Pacific, enhanced upper ocean vertical temperature gradients, and increased frequency of extreme El Niño events are conducive to development of the extreme La Niña events. Approximately 75% of the increase occurs in years following extreme El Niño events, thus projecting more frequent swings between opposite extremes from one year to the next.
Cai, Wenju, Agus Santoso, Guojian Wang, S-W Yeh, S I An, K M Cobb, Matthew Collins, Eric Guilyardi, Fei-Fei Jin, Jong-Seong Kug, Matthieu Lengaigne, Michael J McPhaden, Ken Takahashi, Axel Timmermann, Gabriel A Vecchi, M Watanabe, and L Wu, September 2015: ENSO and greenhouse warming. Nature Climate Change, 5(9), doi:10.1038/nclimate2743. [ Abstract ]
The El Niño/Southern Oscillation (ENSO) is the dominant climate phenomenon affecting extreme weather conditions worldwide. Its response to greenhouse warming has challenged scientists for decades, despite model agreement on projected changes in mean state. Recent studies have provided new insights into the elusive links between changes in ENSO and in the mean state of the Pacific climate. The projected slow-down in Walker circulation is expected to weaken equatorial Pacific Ocean currents, boosting the occurrences of eastward-propagating warm surface anomalies that characterize observed extreme El Niño events. Accelerated equatorial Pacific warming, particularly in the east, is expected to induce extreme rainfall in the eastern equatorial Pacific and extreme equatorward swings of the Pacific convergence zones, both of which are features of extreme El Niño. The frequency of extreme La Niña is also expected to increase in response to more extreme El Niños, an accelerated maritime continent warming and surface-intensified ocean warming. ENSO-related catastrophic weather events are thus likely to occur more frequently with unabated greenhouse-gas emissions. But model biases and recent observed strengthening of the Walker circulation highlight the need for further testing as new models, observations and insights become available.
The El Niño Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO's impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.
Capotondi, Antonietta, Y-G Ham, Andrew T Wittenberg, and Jong-Seong Kug, December 2015: Climate model biases and El Niño Southern Oscillation (ENSO) simulation. U.S. CLIVAR Variations, 13(1), 21-25.
Choi, Kityan, Gabriel A Vecchi, and Andrew T Wittenberg, November 2015: Nonlinear zonal wind response to ENSO in the CMIP5 models: Roles of the zonal and meridional shift of the ITCZ/SPCZ and the simulated climatological precipitation. Journal of Climate, 28(21), doi:10.1175/JCLI-D-15-0211.1. [ Abstract ]
The observed equatorial Pacific zonal wind response during El Niño tends to be stronger than during La Niña. Most global coupled climate models in CMIP5 exhibit such nonlinearity, yet weaker than observed. The wind response nonlinearity can be reproduced by driving a linear shallow-water atmospheric model with a model’s or the observed precipitation anomalies, which can be decomposed into two main components: the zonal and meridional redistribution of the climatological precipitation. Both redistributions contribute comparably to the total rainfall anomalies while the zonal redistribution plays the dominant role in the zonal wind response.
The meridional redistribution component plays an indirect role in the nonlinear wind response by limiting the zonal redistribution during La Niña and thus enhancing the nonlinearity in the wind response significantly. During La Niña, the poleward movement of the ITCZ/SPCZ reduces the equatorial zonal mean precipitation available for the zonal redistribution and its resulting zonal wind response. Conversely, during El Niño, the equatorward movement of the ITCZ and SPCZ do not limit the zonal redistribution of precipitation.
The linear equatorial zonal wind response to ENSO is found to have a significant linear correlation with the equatorial central Pacific climatological precipitation and SST among the CMIP5 models. However, no linear correlation is found between the nonlinear equatorial zonal wind response and the climatological precipitation.
Portions of western North America have experienced prolonged drought over the last decade. This drought has occurred at the same time as the global warming hiatus – a decadal period with little increase in global mean surface temperature. We use climate models and observational analyses to clarify the dual role of recent tropical Pacific changes in driving both the global warming hiatus and North American drought. When we insert observed tropical Pacific wind stress anomalies into coupled models, the simulations produce persistent negative sea surface temperature anomalies in the eastern tropical Pacific, a hiatus in global warming, and drought over North America driven by SST-induced atmospheric circulation anomalies. In our simulations the tropical wind anomalies account for 92% of the simulated North American drought during the recent decade, with 8% from anthropogenic radiative forcing changes. This suggests that anthropogenic radiative forcing is not the dominant driver of the current drought, unless the wind changes themselves are driven by anthropogenic radiative forcing. The anomalous tropical winds could also originate from coupled interactions in the tropical Pacific or from forcing outside the tropical Pacific. The model experiments suggest that if the tropical winds were to return to climatological conditions, then the recent tendency toward North American drought would diminish. Alternatively, if the tropical winds were to persist, then the impact on North American drought would continue; however, the impact of the enhanced Pacific easterlies on global temperature diminishes after a decade or two due to a surface reemergence of warmer water that was initially subducted into the ocean interior.
This study examines the role of processes transporting tracers across the Polar Front (PF) in the depth interval between the surface and major topographic sills, which we refer to as the “PF core”. A preindustrial control simulation of an eddying climate model coupled to a biogeochemical model (CM2.6-miniBLING, 0.1° ocean model) is used to investigate the transport of heat, carbon, oxygen and phosphate across the PF core, with a particular focus on the role of mesoscale eddies. We find that the total transport across the PF core results from an ubiquitous Ekman transport that drives the upwelled tracers to the north, and a localized opposing eddy transport that induces tracer leakages to the south at major topographic obstacles. In the Ekman layer, the southward eddy transport only partially compensates the northward Ekman transport, while below the Ekman layer, the southward eddy transport dominates the total transport but remains much smaller in magnitude than the near-surface northward transport. Most of the southward branch of the total transport is achieved below the PF core, mainly through geostrophic currents. We find that the eddy diffusive transport reinforces the southward eddy advective transport for carbon and heat, and opposes it for oxygen and phosphate. Eddy advective transport is likely to be the leading-order component of eddy-induced transport for all four tracers. However, eddy diffusive transport may provide a significant contribution to the southward eddy heat transport due to strong along-isopycnal temperature gradients.
Dwyer, John, Suzana J Camargo, Adam H Sobel, M Biasutti, Kerry A Emanuel, Gabriel A Vecchi, Ming Zhao, and Michael K Tippett, August 2015: Projected Twenty-First-Century Changes in the Length of the Tropical Cyclone Season. Journal of Climate, 28(15), doi:10.1175/JCLI-D-14-00686.1. [ Abstract ]
This study investigates projected changes in the length of the tropical cyclone season due to greenhouse gas increases. Two sets of simulations are analyzed, both of which capture the relevant features of the observed annual cycle of tropical cyclones in the recent historical record. Both sets use output from the general circulation models (GCMs) of the CMIP3 or CMIP5 suites. In one set, downscaling is performed by randomly seeding incipient vortices into the large-scale atmospheric conditions simulated by each GCM and simulating the vortices’ evolution in an axisymmetric dynamical tropical cyclone model; in the other, the GCMs’ sea surface temperature (SST) is used as the boundary condition of a high-resolution, global atmospheric model (HIRAM). The downscaling model projects a longer season (in the late 21st century compared to the 20th) in most basins when using CMIP5 data, but a slightly shorter season using CMIP3. HIRAM with either CMIP3 or CMIP5 SST anomalies projects a shorter tropical cyclone season in most basins. Season length is measured by the number of consecutive days that the mean cyclone count is greater than a fixed threshold, but other metrics give consistent results. The projected season length changes are also consistent with the large-scale changes, as measured by a genesis index of tropical cyclones. The season length changes are mostly explained by an idealized year-round multiplicative change in tropical cyclone frequency, but additional changes in the transition months also contribute.
The response of the equatorial Pacific Ocean’s seasonal cycle to orbital forcing is explored using idealized simulations with a coupled atmosphere-ocean GCM, in which eccentricity, obliquity, and longitude of the perihelion are altered while other boundary conditions are maintained at preindustrial levels. The importance of ocean dynamics in the climate response is investigated using additional simulations with a slab ocean version of the model. Precession is found to substantially influence the equatorial Pacific seasonal cycle through both thermodynamic and dynamic mechanisms while changes in obliquity have only a small effect. In the precession experiments, western equatorial Pacific SSTs respond in a direct thermodynamic manner to changes in insolation, while the eastern equatorial Pacific is first affected by the propagation of thermocline temperature anomalies from the west. These thermocline signals result from zonal wind anomalies associated with changes in the strength of subtropical anticyclones and shifts in the regions of convection in the western equatorial Pacific. The redistribution of heat from these thermocline signals, aided by the direct thermodynamic effect of insolation anomalies, results in large changes to the strength and timing of the eastern equatorial Pacific seasonal cycle. A comparison of 10 CMIP5 mid-Holocene experiments, in which the primary forcing is due to precession, shows that this response is relatively robust across models. Because equatorial Pacific SST anomalies have local climate impacts as well as non-local impacts through teleconnections, these results may be important to understanding paleoclimate variations both inside and outside of the tropical Pacific.
Findell, Kirsten L., Pierre Gentine, Benjamin R Lintner, and B P Guillod, August 2015: Data Length Requirements for Observational Estimates of Land-Atmosphere Coupling Strength. Journal of Hydrometeorology, 16(4), doi:10.1175/JHM-D-14-0131.1. [ Abstract ]
Multiple metrics have been developed in recent years to characterize the strength of land-atmosphere coupling in regional and global climate models. Evaluation of these metrics against observations has proven challenging because of limited observations and/or metric definitions based on model experimental designs that are not replicable with observations. Additionally, because observations are limited in time, with only a single realization of the earth’s climate available, metrics of land-atmosphere coupling strength typically assume stationarity and ergodicity, so that an observed time series (or set of time series) can be used in place of an ensemble mean of multiple realizations. The present study evaluates the observational data requirements necessary for robust quantification of a suite of land-atmosphere coupling metrics previously described in the literature. We demonstrate that the amount of data required to obtain robust estimates of metrics assessing relationships between variables is greater than that necessary to constrain means of directly-measured observables. Moreover, while the addition of unbiased noise does not significantly alter the mean of a directly observable quantity, inclusion of such noise degrades metrics based on connections between variables, yielding a unidirectional and negative impact on metric strength estimates. This analysis suggests that longer records of surface observations are required to correctly estimate land-atmosphere coupling strength than are required to estimate mean values of the observed quantities.
We assess the uptake, transport and storage of oceanic anthropogenic carbon and
heat over the period 1861 to 2005 in a new set of coupled carbon-climate Earth
System models conducted for the fifth Coupled Model Intercomparison Project
(CMIP5), with a particular focus on the Southern Ocean. Simulations show the
Southern Ocean south of 30°S, occupying 30% of global surface ocean area, accounts
for 43 ± 3% (42 ± 5 Pg C) of anthropogenic CO2 and 75 ± 22% (23 ± 9 *1022J) of heat
uptake by the ocean over the historical period. Northward transport out of the Southern
Ocean is vigorous, reducing the storage to 33 ± 6 Pg anthropogenic carbon and 12 ± 7
*1022J heat in the region. The CMIP5 models as a class tend to underestimate the
observational-based global anthropogenic carbon storage, but simulate trends in global
ocean heat storage over the last fifty years within uncertainties of observation-based
estimates. CMIP5 models suggest global and Southern Ocean CO2 uptake have been
largely unaffected by recent climate variability and change. Anthropogenic carbon and
heat storage show a common broad-scale pattern of change, but ocean heat storage is
more structured than ocean carbon storage. Our results highlight the significance of
the Southern Ocean for the global climate and as the region where models differ the
most in representation of anthropogenic CO2 and in particular heat uptake.
Goddard, P, Jianjun Yin, Stephen M Griffies, and Shaoqing Zhang, February 2015: An extreme event of sea-level rise along the Northeast coast of North America in 2009–2010. Nature Communications, 6, 6346, doi:10.1038/ncomms7346. [ Abstract ]
The coastal sea levels along the Northeast Coast of North America show significant year-to-year fluctuations in a general upward trend. The analysis of long-term tide gauge records identified an extreme sea-level rise (SLR) event during 2009–10. Within this 2-year period, the coastal sea level north of New York City jumped by 128 mm. This magnitude of interannual SLR is unprecedented (a 1-in-850 year event) during the entire history of the tide gauge records. Here we show that this extreme SLR event is a combined effect of two factors: an observed 30% downturn of the Atlantic meridional overturning circulation during 2009–10, and a significant negative North Atlantic Oscillation index. The extreme nature of the 2009–10 SLR event suggests that such a significant downturn of the Atlantic overturning circulation is very unusual. During the twenty-first century, climate models project an increase in magnitude and frequency of extreme interannual SLR events along this densely populated coast.
The complex nature of El Niño - Southern Oscillation (ENSO) is often simplified through the use of conceptual models, each of which offers a different perspective on the ocean-atmosphere feedbacks underpinning the ENSO cycle. One theory, the unified oscillator, combines a variety of conceptual frameworks in the form of a coupled system of delay differential equations. The system produces a self-sustained oscillation on interannual timescales. While the unified oscillator is assumed to provide a more complete conceptual framework of ENSO behaviors than the models it incorporates, its formulation and performance have not been systematically assessed. This paper investigates the accuracy of the unified oscillator through its ability to replicate the ENSO cycle modeled by flux-forced output from the Australian Community Climate and Earth System Simulator Ocean Model (ACCESS-OM). The anomalous sea surface temperature equation reproduces the main features of the corresponding tendency modeled by ACCESS-OM reasonably well. However, the remaining equations - for the thermocline depth anomaly and zonal wind stress anomalies - are unable to accurately replicate the corresponding tendencies in ACCESS-OM. Modifications to the unified oscillator, including a diagnostic form of the zonal wind stress anomaly equations, improve its ability to emulate simulated ENSO tendencies. Despite these improvements, the unified oscillator model is less adept than the delayed oscillator model it incorporates in capturing ENSO behavior in ACCESS-OM, bringing into question its usefulness as a unifying ENSO framework.
We characterize impacts on heat in the ocean climate system from transient ocean mesoscale eddies. Our tool is a suite of centennial-scale 1990 radiatively forced numerical climate simulations from three GFDL coupled models comprising the CM2-O model suite. CM2-O models differ in their ocean resolution: CM2.6 uses a 0.1° ocean grid, CM2.5 uses an intermediate grid with 0.25° spacing, and CM2-1deg uses a nominally 1.0° grid.
Analysis of the ocean heat budget reveals that mesoscale eddies act to transport heat upward in a manner that partially compensates (or offsets) for the downward heat transport from the time mean currents. Stronger vertical eddy heat transport in CM2.6 relative to CM2.5 accounts for the significantly smaller temperature drift in CM2.6. The mesoscale eddy parameterization used in CM2-1deg also imparts an upward heat transport, yet it differs systematically from that found in CM2.6. This analysis points to the fundamental role that ocean mesoscale features play in transient ocean heat uptake. In general, the more accurate simulation found in CM2.6 provides an argument for either including a rich representation of the ocean mesoscale in model simulations of the mean and transient climate, or for employing parameterizations that faithfully reflect the role of eddies in both lateral and vertical heat transport.
Han, G, Xinrong Wu, Shaoqing Zhang, Zhengyu Liu, I M Navon, and Wei Li, August 2015: A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System. Advances in Meteorology, 2015, Article ID 530764, doi:10.1155/2015/530764. [ Abstract ]
Coupling parameter estimation (CPE) that uses observations to estimate the parameters in a coupled model through error covariance between variables residing in different media may increase the consistency of estimated parameters in an air-sea coupled system. However, it is very challenging to accurately evaluate the error covariance between such variables due to the different characteristic time scales at which flows vary in different media. With a simple Lorenz-atmosphere and slab ocean coupled system that characterizes the interaction of two-timescale media in a coupled “climate” system, this study explores feasibility of the CPE with four-dimensional variational analysis and ensemble Kalman filter within a perfect observing system simulation experiment framework. It is found that both algorithms can improve the representation of air-sea coupling processes through CPE compared to state estimation only. These simple model studies provide some insights when parameter estimation is implemented with a coupled general circulation model for improving climate estimation and prediction initialization.
http://www.hindawi.com/journals/amete/aa/530764/
Huang, B, J Zhu, L Marx, X Wu, Arun Kumar, Zeng-Zhen Hu, Magdalena Alonso Balmaseda, and Shaoqing Zhang, et al., January 2015: Climate drift of AMOC, North Atlantic salinity and arctic sea ice in CFSv2 decadal predictions. Climate Dynamics, 44(1-2), doi:10.1007/s00382-014-2395-y. [ Abstract ]
There are potential advantages to extending operational seasonal forecast models to predict decadal variability but major efforts are required to assess the model fidelity for this task. In this study, we examine the North Atlantic climate simulated by the NCEP Climate Forecast System, version 2 (CFSv2), using a set of ensemble decadal hindcasts and several 30-year simulations initialized from realistic ocean–atmosphere states. It is found that a substantial climate drift occurs in the first few years of the CFSv2 hindcasts, which represents a major systematic bias and may seriously affect the model’s fidelity for decadal prediction. In particular, it is noted that a major reduction of the upper ocean salinity in the northern North Atlantic weakens the Atlantic meridional overturning circulation (AMOC) significantly. This freshening is likely caused by the excessive freshwater transport from the Arctic Ocean and weakened subtropical water transport by the North Atlantic Current. A potential source of the excessive freshwater is the quick melting of sea ice, which also causes unrealistically thin ice cover in the Arctic Ocean. Our sensitivity experiments with adjusted sea ice albedo parameters produce a sustainable ice cover with realistic thickness distribution. It also leads to a moderate increase of the AMOC strength. This study suggests that a realistic freshwater balance, including a proper sea ice feedback, is crucial for simulating the North Atlantic climate and its variability.
This study demonstrates skillful seasonal prediction of 2m air temperature and precipitation over land in a new high-resolution climate model developed by Geophysical Fluid Dynamics Laboratory, and explores the possible sources of the skill. We employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land, and demonstrate the predictive skill of these components. First, we show improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of NINO3.4 index and other aspects of interest. Then we measure the skill of temperature and precipitation in the high-resolution model for boreal winter and summer, and diagnose the sources of the skill. Lastly, we reconstruct predictions using a few most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, we find that the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer, and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2m air temperature and precipitation over land.
Kam, Jonghun, Thomas R Knutson, Fanrong Zeng, and Andrew T Wittenberg, December 2015: Record annual mean warmth over Europe, the northeast Pacific, and the northwest Atlantic during 2014: Assessment of anthropogenic influence. Bulletin of the American Meteorological Society, 96(12), doi:10.1175/BAMS-EEE_2014_ch13.1.
Keenlyside, N, Jin Ba, J Mecking, N-E Omrani, M Latif, Rong Zhang, and Rym Msadek, October 2015: North Atlantic Multi-Decadal Variability — Mechanisms and Predictability In Climate Change: Multidecadal and Beyond, doi:10.1142/9789814579933_0009. [ Abstract ]
The North Atlantic Ocean undergoes pronounced basin-wide, multi-decadal variations. The corresponding fluctuations in sea surface temperature (SST) have become known as the Atlantic Multidecadal Oscillation (AMO) or Atlantic multidecadal variability (AMV). AMV is receiving increasing attention for three key reasons: (1) it has been linked to climate impacts of major socio-economic importance, such as Sahel rainfall; (2) it may temporarily mask anthropogenic global warming not only in the North Atlantic Sector, but over the Northern Hemisphere (NH); and (3) it appears to be predictable on decadal timescales. This chapter provides an overview of current understanding of AMV, summarizing proposed mechanisms, our ability to simulate and predict it, as well as challenges for future research.
Knutson, Thomas R., March 2015: Tropical Cyclones and Climate Change In Encyclopedia of Atmospheric Sciences 2nd edition, Vol 6, Gerald R. North (editor-in-chief), John Pyle and Fuqing Zhang (editors), Oxford, Academic Press, 111-122.
Global projections of intense tropical cyclone activity are derived from the Geophysical Fluid Dynamics Laboratory (GFDL) HiRAM (50 km grid) atmospheric model and the GFDL Hurricane Model using a two-stage downscaling procedure. First, tropical cyclone genesis is simulated globally using the HiRAM atmospheric model. Each storm is then downscaled into the GFDL Hurricane Model, with horizontal grid-spacing near the storm of 6 km, and including ocean coupling (e.g., ‘cold wake’ generation). Simulations are performed using observed sea surface temperatures (SSTs) (1980-2008); for a “control run” with 20 repeating seasonal cycles; and for a late 21st century projection using an altered SST seasonal cycle obtained from a CMIP5/RCP4.5 multi-model ensemble. In general agreement with most previous studies, projections with this framework indicate fewer tropical cyclones globally in a warmer late-21st-century climate, but also an increase in average cyclone intensity, precipitation rates, and in the number and occurrence-days of very intense category 4-5 storms. While these changes are apparent in the globally averaged tropical cyclone statistics, they are not necessarily present in each individual basin. The inter-basin variation of changes in most of the tropical cyclone metrics we examined is directly correlated to the variation in magnitude of SST increases between the basins. Finally, the framework is shown capable of reproducing both the observed global distribution of outer storm size--albeit with a slight high bias--and its inter-basin variability. Projected median size is found to remain nearly constant globally, with increases in most basins offset by decreases in the Northwest Pacific.
Kossin, James, Thomas R Knutson, Kerry A Emanuel, T R Karl, Kenneth E Kunkel, and J O'Brien, July 2015: Reply to ‘Comment on “Monitoring and Understanding Trends in Extreme Storms - State of Knowledge”’. Bulletin of the American Meteorological Society, 96(7), doi:10.1175/BAMS-D-14-00261.1.
This study investigates the seasonality of the relationship between the Great Plains low-level jet (GPLLJ) and the Pacific Ocean from spring to summer, using observational analysis and coupled model experiments. The observed GPLLJ and El Niño-Southern Oscillation (ENSO) relation undergoes seasonal changes with a stronger GPLLJ associated with La Niña in boreal spring and El Niño in boreal summer. The ability of the GFDL FLOR global coupled climate model, which has the high-resolution atmospheric and land components, to simulate the observed seasonality in the GPLLJ-ENSO relationship is assessed. The importance of simulating the magnitude and phase-locking of ENSO accurately in order to better simulate its seasonal teleconnections with the Intra-Americas Seas (IAS) is demonstrated. This study explores the mechanisms for seasonal changes in the GPLLJ-ENSO relation in model and observations. It is hypothesized that ENSO affects the GPLLJ variability through the Caribbean low-level jet (CLLJ) during the summer and spring seasons. These results suggest that climate models with improved ENSO variability would advance our ability to simulate and predict seasonal variations of the GPLLJ and their associated impacts on the United States.
Lintner, Benjamin R., Pierre Gentine, Kirsten L Findell, and G D Salvucci, May 2015: The Budyko and complementary relationships in an idealized model of large-scale land–atmosphere coupling. Hydrology and Earth System Sciences, 19(5), doi:10.5194/hess-19-2119-2015. [ Abstract ]
Expressions corresponding to two well-known relationships in hydrology and hydrometeorology, the Budyko and complementary relationships, are derived within an idealized prototype representing the physics of large-scale land–atmosphere coupling. These relationships are shown to hold on long (climatologic) time scales because of the tight coupling that exists between precipitation, atmospheric radiation, moisture convergence and advection. The slope of the complementary relationship is shown to be dependent the Clausius–Clapeyron relationship between saturation specific humidity and temperature, with important implications for the continental hydrologic cycle in a warming climate, e.g., one consequence of this dependence is that the complementary relationship may be expected to become more asymmetric with warming, as higher values of the slope imply a larger change in potential evaporation for a given change in evapotranspiration. In addition, the transparent physics of the prototype permits diagnosis of the sensitivity of the Budyko and complementary relationships to various atmospheric and land surface processes. Here, the impacts of anthropogenic influences, including large-scale irrigation and global warming, are assessed.
Little, Christopher M., R Horton, Robert E Kopp, M Oppenheimer, Gabriel A Vecchi, and Gabriele Villarini, December 2015: Joint projections of US East Coast sea level and storm surge. Nature Climate Change, 5(12), doi:10.1038/nclimate2801. [ Abstract ]
Future coastal flood risk will be strongly influenced by sea-level rise (SLR) and changes in the frequency and intensity of tropical cyclones. These two factors are generally considered independently. Here, we assess twenty-first century changes in the coastal hazard for the US East Coast using a flood index (FI) that accounts for changes in flood duration and magnitude driven by SLR and changes in power dissipation index (PDI, an integrated measure of tropical cyclone intensity, frequency and duration). Sea-level rise and PDI are derived from representative concentration pathway (RCP) simulations of 15 atmosphere–ocean general circulation models (AOGCMs). By 2080–2099, projected changes in the FI relative to 1986–2005 are substantial and positively skewed: a 10th–90th percentile range 4–75 times higher for RCP 2.6 and 35–350 times higher for RCP 8.5. High-end FI projections are driven by three AOGCMs that project the largest increases in SLR, PDI and upper ocean temperatures. Changes in PDI are particularly influential if their intra-model correlation with SLR is included, increasing the RCP 8.5 90th percentile FI by a further 25%. Sea-level rise from other, possibly correlated, climate processes (for example, ice sheet and glacier mass changes) will further increase coastal flood risk and should be accounted for in comprehensive assessments.
Lu, Feiyu, Zhengyu Liu, Shaoqing Zhang, and Y Liu, September 2015: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study. Monthly Weather Review, 143(9), doi:10.1175/MWR-D-14-00322.1. [ Abstract ]
This paper studies a new Leading Averaged Coupled Covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilates observations into multiple model components like the weakly coupled version (WCDA), but also applies cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice due to different timescales between model components.
In a typical extra-tropical coupled system, the ocean-atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance. Here it is applied to a simple coupled model with the Ensemble Kalman Filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the SCDA using simultaneous coupled covariance. The advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger timescale difference or model biases.
Lu, Feiyu, Zhengyu Liu, Shaoqing Zhang, Y Liu, and R Jacob, November 2015: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM experiments. Monthly Weather Review, 143(11), doi:10.1175/MWR-D-15-0088.1. [ Abstract ]
This paper uses a fully coupled general circulation model (CGCM) to study the Leading Averaged Coupled Covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. Our previous study in a simple coupled climate model (Lu et al. 2015) has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).
Here in Part II, we test the LACC method with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extra-tropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.
Milly, P C., J Betancourt, M Falkenmark, R M Hirsch, Z W Kundzewicz, D Lettenmaier, Ronald J Stouffer, M D Dettinger, and V Krysanova, September 2015: On Critiques of “Stationarity is Dead: Whither Water Management?”. Water Resources Research, 51(9), doi:10.1002/2015WR017408. [ Abstract ]
We review and comment upon some themes in the recent stream of critical commentary on the assertion that “stationarity is dead,” attempting to clear up some misunderstandings; to note points of agreement; to elaborate on matters in dispute; and to share further relevant thoughts. This article is protected by copyright. All rights reserved.
We review past, present and future North Atlantic hurricane activity based on analysis of observational records and models projections. When adjusted for likely missed tropical cyclones, the observational record does not show any significant increase or decrease of North Atlantic hurricane frequency. Downscaling results for most available CMIP5 models show a decrease or little change in overall frequency of tropical storms and hurricanes, although in the Atlantic basin, previous studies by other investigators report a wider range of change (+/−60%). Some model projections of late 21st century hurricane activity indicate an increase in frequency of the strongest storms (category 4–5 hurricanes). The projected increase is substantial (+100% per century) in the CMIP3 ensemble model downscaling, but much smaller (+40%) and only marginally significant in the CMIP5 ensemble model downscaling. Rainfall rates in the inner core of the hurricanes are projected to increase with potentially a substantial damage impact. The largest source of uncertainty in predicting changes in Atlantic tropical storms activity over the first half of the 21st century arises from the internal variability of the climate system. Nonetheless, some of these natural fluctuations appear to be predictable beyond seasonal time scale. We review recent predictability assessment results based on two CMIP5 models. Initializing these models with observational estimates leads to encouraging results in predicting multi-year variations in North Atlantic hurricane frequency. However the short record and the persistent character of the time series limits the ability to confidently predict North Atlantic hurricane activity for now. Remaining model biases, despite the tremendous improvement over the recent decades, and the changing observational system make it an ongoing challenge to simulate past hurricane activity and project or predict its future behavior.
A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model (HiFLOR) has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises of high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean components. HiFLOR was developed from the Forecast Oriented Low Resolution Ocean model (FLOR) by decreasing the horizontal grid spacing of the atmospheric component from 50-km to 25-km, while leaving most of the sub-gridscale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, and a comparable simulation of storm-induced cold wakes and TC-genesis modulation induced by the Madden Julian Oscillation (MJO). Moreover, HiFLOR is able to simulate and predict extremely intense TCs (categories 4 and 5) and their interannual variations, which represents the first time a global coupled model has been able to simulate such extremely intense TCs in a multi-century simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.
Rosati, Anthony, Oscar Alves, Magdalena Alonso Balmaseda, Xiaosong Yang, and Yan Xue, 2015: Ocean data assimilation for ENSO prediction. U.S. CLIVAR Variations, 13(1), .
Sanchez-Franks, A, and Rong Zhang, November 2015: Impact of the Atlantic meridional overturning circulation on the decadal variability of the Gulf Stream path and regional chlorophyll and nutrient concentrations. Geophysical Research Letters, 42(22), doi:10.1002/2015GL066262. [ Abstract ]
In this study, we show that the underlying physical driver for the decadal variability in the Gulf Stream (GS) path and the regional biogeochemical cycling is linked to the low frequency variability in the Atlantic meridional overturning circulation (AMOC). There is a significant anticorrelation between AMOC variations and the meridional shifts of the GS path at decadal time scale in both observations and two Earth system models (ESMs). The chlorophyll and nutrient concentrations in the GS region are found significantly correlated with the AMOC fingerprint and anticorrelated with the GS path at decadal time scale through coherent isopycnal changes in the GS front in the ESMs. Our results illustrate how changes in the large-scale ocean circulation, such as AMOC, are teleconnected with regional decadal physical and biogeochemical variations near the North American east coast. Such linkages are useful for predicting future physical and biogeochemical variations in this region.
Santoso, Agus, Wenju Cai, Matthew Collins, Michael J McPhaden, Fei-Fei Jin, Eric Guilyardi, Gabriel A Vecchi, D Dommenget, and Guojian Wang, November 2015: ENSO Extremes and Diversity: Dynamics, Teleconnections, and Impacts. Bulletin of the American Meteorological Society, 96(11), doi:10.1175/BAMS-D-15-00141.1.
Sea surface temperature (SST) anomalies are often both leading indicators and important drivers of marine resource fluctuations. Assessment of the skill of SST anomaly forecasts within coastal ecosystems accounting for the majority of global fish yields, however, has been minimal. This reflects coarse global forecast system resolution and past emphasis on the predictability of ocean basin-scale SST variations. This paper assesses monthly to inter-annual SST anomaly predictions in coastal “Large Marine Ecosystems” (LMEs). We begin with an analysis of 7 well-observed LMEs adjacent to the United States and then examine how mechanisms responsible for prediction skill in these systems are reflected in predictions for LMEs globally. Historical SST anomaly estimates from the 1/4o daily Optimal Interpolation Sea Surface Temperature reanalysis (OISST.v2) were first found to be highly consistent with in-situ measurements for 6 of the 7 U.S. LMEs. Thirty years of retrospective forecasts from climate forecast systems developed at NOAA’s Geophysical Fluid Dynamics Laboratory (CM2.5-FLOR) and the National Center for Environmental Prediction (CFSv2) were then assessed against OISST.v2. Forecast skill varied widely by LME, initialization month, and lead but there were many cases of high skill that also exceeded that of a persistence forecast, some at leads greater than 6 months. Mechanisms underlying skill above persistence included accurate simulation of a) seasonal transitions between less predictable locally generated and more predictable basin-scale SST variability; b) seasonal transitions between different basin-scale influences; c) propagation of SST anomalies across seasons through sea ice; and d) re-emergence of previous anomalies upon the breakdown of summer stratification. Globally, significant skill above persistence across many tropical systems arises via mechanisms a) and b). Combinations of all four mechanisms contribute to less prevalent but nonetheless significant skill in extratropical systems. While continued refinement of global climate forecast systems and observations are needed to improve coastal SST anomaly prediction and extend predictions to other ecosystem relevant variables (e.g., salinity), present skill warrants close examination of forecasts for marine resource applications.
Strong, Jeffrey D., Gabriel A Vecchi, and Paul Ginoux, September 2015: The Response of the Tropical Atlantic and West African Climate to Saharan Dust in a Fully Coupled GCM. Journal of Climate, 28(18), doi:10.1175/JCLI-D-14-00797.1. [ Abstract ]
This study examines the climate response in West Africa and the tropical Atlantic to an idealized aerosol radiative forcing from Saharan-born mineral dust, comparable to the observed changes between the 1960s and 1990s, using simulations with the fully coupled GFDL Climate Model 2.1 (CM2.1) for two optical property regimes: more absorbing (ABS) and more scattering (SCT) dust. For both regimes dust induces significant regional reductions in radiative flux at the surface (approximately –30 W m−2). At the top of the atmosphere (ToA) dust in the two simulations produces a radiative flux anomaly of opposite sign (+30 W m−2 in the ABS-case and –20 W m−2 in the SCT-case). These differences result in opposing regional hydrologic and thermodynamic effects of dust. The ABS-forced simulations show an increase in the West African monsoon due to dust whereas in the SCT-forced simulations dust causes a decrease in the monsoon. This is due to moist enthalpy changes throughout the atmospheric column over West Africa creating either horizontal divergence or convergence near the surface, respectively. In the tropical North Atlantic, dust acts to cool the ocean surface. However, in the subsurface the ABS-forced simulations show a decrease in upper ocean heat content while the SCT-forced simulations show an increase in upper ocean heat content. The peak differences primarily arise from the wind stress curl response to a shift in the Atlantic ITCZ and associated mixed layer depth anomalies. Changes to upper ocean currents are also found to be important in transporting energy across the equator.
Walsh, Kevin J., Suzana J Camargo, Gabriel A Vecchi, A S Daloz, J B Elsner, Kerry A Emanuel, M Horn, Y-K Lim, Malcolm J Roberts, Christina M Patricola, E Scoccimarro, Adam H Sobel, S E Strazzo, Gabriele Villarini, Michael F Wehner, Ming Zhao, James Kossin, T LaRow, K Oouchi, S D Schubert, H Wang, Julio T Bacmeister, P Chang, F Chauvin, Christiane Jablonowski, Arun Kumar, and Hiroyuki Murakami, et al., July 2015: Hurricanes and climate: the U.S. CLIVAR working group on hurricanes. Bulletin of the American Meteorological Society, 96(6), doi:10.1175/BAMS-D-13-00242.1. [ Abstract ]
While a quantitative climate theory of tropical cyclone formation remains elusive, considerable progress has been made recently in our ability to simulate tropical cyclone climatologies and understand the relationship between climate and tropical cyclone formation. Climate models are now able to simulate a realistic rate of global tropical cyclone formation, although simulation of the Atlantic tropical cyclone climatology remains challenging unless horizontal resolutions finer than 50 km are employed. This article summarizes published research from the idealized experiments of the Hurricane Working Group of U.S. CLIVAR (CLImate VARiability and predictability of the ocean-atmosphere system). This work, combined with results from other model simulations, has strengthened relationships between tropical cyclone formation rates and climate variables such as mid-tropospheric vertical velocity, with decreased climatological vertical velocities leading to decreased tropical cyclone formation. Systematic differences are shown between experiments in which only sea surface temperature is increased versus experiments where only atmospheric carbon dioxide is increased, with the carbon dioxide experiments more likely to demonstrate the decrease in tropical cyclone numbers previously shown to be a common response of climate models in a warmer climate. Experiments where the two effects are combined also show decreases in numbers, but these tend to be less for models that demonstrate a strong tropical cyclone response to increased sea surface temperatures. Further experiments are proposed that may improve our understanding of the relationship between climate and tropical cyclone formation, including experiments with two-way interaction between the ocean and the atmosphere and variations in atmospheric aerosols.
Wittenberg, Andrew T., December 2015: Low-frequency variations of ENSO. U.S. CLIVAR Variations, 13(1), 26-31.
Wright, D, Thomas R Knutson, and James A Smith, December 2015: Regional climate model projections of rainfall from U.S. landfalling tropical cyclones. Climate Dynamics, 45(11-12), doi:10.1007/s00382-015-2544-y. [ Abstract ]
The eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980–2006 tropical cyclone frequency and rainfall intensity for the months of August–October are compared against results from previous studies and observation-based datasets. The 1980–2006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500 km of the storm center increase by 8–17 % in the future climate projections relative to control. This is at least as much as expected from the Clausius–Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300–500 km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones.
While tropical cyclone (TC) prediction, in particular TC genesis, remains very challenging, accurate prediction of TCs is critical for timely preparedness and mitigation. Using a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the authors studied the predictability of two destructive landfall TCs, Hurricane Sandy in 2012 and Super Typhoon Haiyan in 2013. Results demonstrate that the geneses of these two TCs are highly predictable with the maximum prediction lead-time reaching 11 days. The “beyond weather time scale” predictability of tropical cyclogenesis is primarily attributed to the model’s skillful prediction of the intraseasonal Madden-Julian Oscillation (MJO) and the westward propagation of easterly waves. Meanwhile, the landfall location and time can be predicted one week ahead for Sandy’s U.S landfall, and two weeks ahead for Haiyan’s landing in the Philippines. The success in predicting Sandy and Haiyan, together with low false alarms, indicates the potential using the GFDL coupled model for operational extended-range predictions of TCs.
Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden-Julian Oscillation (MJO) prediction skill in boreal wintertime (November-April) is evaluated by analyzing 11 years (2003-2013) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique towards observations. Using the real-time multivariate MJO (RMM) index as a predictand, we demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 vs 7 days) than that between initially strong and weak cases (29 vs 24 days). Meanwhile, the strong dependence on target phases is found, as opposed to the relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during DYNAMO/CINDY (Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.
Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.
The seasonal predictability of extratropical storm tracks in Geophysical Fluid Dynamics Laboratory (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are ENSO-related spatial pattern for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm track changes (e.g., extreme low and high sea level pressure and extreme 2m air temperature) in response to different ENSO phases. These results point towards the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.
Yang, Y, Shang-Ping Xie, L Wu, Yu Kosaka, Ngar-Cheung Lau, and Gabriel A Vecchi, October 2015: Seasonality and Predictability of the Indian Ocean Dipole Mode: ENSO Forcing and Internal Variability. Journal of Climate, 28(20), doi:10.1175/JCLI-D-15-0078.1. [ Abstract ]
This study evaluates the relative contributions to the Indian Ocean Dipole (IOD) mode of interannual variability from the El Niño-Southern Oscillation (ENSO) forcing and ocean-atmosphere feedbacks internal to the Indian Ocean. The ENSO forcing and internal variability is extracted by conducting a 10-member coupled simulation for 1950-2012 where sea surface temperature (SST) is restored to the observed anomalies over the tropical Pacific but interactive with the atmosphere over the rest of the world ocean. In these experiments, the ensemble mean is due to ENSO forcing and the inter-member difference arises from internal variability of the climate system independent of ENSO. These elements contribute one third and two thirds of the total IOD variance, respectively. Both types of IOD variability develop into an east-west dipole pattern due to Bjerknes feedback and peak in September-November. The ENSO forced and internal IOD modes differ in several important ways. The forced IOD mode develops in August with a broad meridional pattern, and eventually evolves into the Indian Ocean Basin mode; while the internal IOD mode grows earlier in June, is more confined to the equator and decays rapidly after October. The internal IOD mode is more skewed than the ENSO forced response. The destructive interference of ENSO forcing and internal variability can explain early-terminating IOD events, referred to IOD-like perturbations that fail to grow during boreal summer.
Our results have implications for predictability. Internal variability, as represented by pre-season sea surface height anomalies off Sumatra, contributes to predictability considerably. Including this indicator of internal variability, together with ENSO, improves the predictability of IOD.
Zhang, X-F, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and G Han, February 2015: Parameter Optimization in an Intermediate Coupled Climate Model with Biased Physics. Journal of Climate, 28(3), doi:10.1175/JCLI-D-14-00348.1. [ Abstract ]
Imperfect physical parameterization schemes in a coupled climate model are an important source of model biases that adversely impact climate prediction. However, how observational information should be used to optimize physical parameterizations through parameter estimation has not been fully studied. Using an intermediate coupled ocean-atmosphere model, we studied parameter optimization when the assimilation model contains biased physics within a biased assimilation experiment framework. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation model and the “truth” model that is used to generate simulated observations. While the stochastic physics, implemented by initially perturbing the physical parameters, can significantly enhance the ensemble spread and improve the representation of the model ensemble, the parameter estimation is able to mitigate the model biases induced by the biased physics. Further, better results for climate estimation and prediction can be obtained when only the most-influential physical parameters are optimized and allowed to vary geographically. In addition, the parameter optimization with the biased model physics improves the performance of the climate estimation and prediction in the deep ocean significantly, even if there is no direct observational constraint on the low frequency component of the state variables. These results provide some insight into decadal predictions in a coupled ocean-atmosphere general circulation model that includes imperfect physical schemes that are initialized from the climate observing system.
Zhang, Rong, April 2015: Mechanisms for low-frequency variability of summer Arctic sea ice extent. Proceedings of the National Academy of Sciences, 112(15), doi:10.1073/pnas.1422296112. [ Abstract ]
Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic.
Zhang, J, and Rong Zhang, July 2015: On the Evolution of Atlantic Meridional Overturning Circulation (AMOC) Fingerprint and Implications for Decadal Predictability in the North Atlantic. Geophysical Research Letters, 42(13), doi:10.1002/2015GL064596. [ Abstract ]
It has been suggested previously that the AMOC anomaly associated with changes in the North Atlantic Deep Water formation propagates southward with an advection speed north of 34°N. In this study, using GFDL CM2.1, we show that this slow southward propagation of the AMOC anomaly is crucial for the evolution and the enhanced decadal predictability of the AMOC fingerprint - the leading mode of upper ocean heat content (UOHC) in the extra-tropical North Atlantic. A positive AMOC anomaly in northern high latitudes leads to a convergence/divergence of the Atlantic meridional heat transport (MHT) anomaly in the subpolar/Gulf Stream region, thus warming in the subpolar gyre (SPG) and cooling in the Gulf Stream region after several years. Recent decadal prediction studies successfully predicted the observed warm shift in the SPG in the mid 1990s. Our results here provide the physical mechanism for the enhanced decadal prediction skills in the SPG UOHC.
This study examines two sets of high-resolution coupled model forecasts starting from no-tropical cyclone (TC) and correct-TC-statistics initial conditions to understand the role of TC events on climate prediction. While the model with no-TC initial conditions can quickly spin up TCs within a week, the initial conditions with a corrected TC distribution can produce more accurate forecast of sea surface temperature up to one and half months and maintain larger ocean heat content up to 6 months due to enhanced mixing from continuous interactions between initialized and forecasted TCs and the evolving ocean states. The TC-enhanced tropical ocean mixing strengthens the meridional heat transport in the Southern Hemisphere driven primarily by Southern Ocean surface Ekman fluxes but weakens the Northern Hemisphere poleward transport in this model. This study suggests a future plausible initialization procedure for seamless weather-climate prediction when individual convection-permitting cyclone initialization is incorporated into this TC-statistics-permitting framework.
Zhang, Liping, and Thomas L Delworth, October 2015: Analysis of the Characteristics and Mechanisms of the Pacific Decadal Oscillation in a Suite of Coupled Models from the Geophysical Fluid Dynamics Laboratory. Journal of Climate, 28(19), doi:10.1175/JCLI-D-14-00647.1. [ Abstract ]
North Pacific decadal oceanic and atmospheric variability is examined in a suite of coupled climate models developed at the Geophysical Fluid Dynamics Laboratory (GFDL). The models have ocean horizontal resolutions ranging from 1° to 0.1°, and atmospheric horizontal resolutions ranging from 200km to 50km. In all simulations the dominant pattern of decadal-scale sea surface temperature (SST) variability over the North Pacific is similar to the observed Pacific Decadal Oscillation (PDO). Simulated SST anomalies in the Kuroshio Oyashio Extension (KOE) region exhibit a significant spectral peak at approximately 20 years.
We use sensitivity experiments to show that: (i) the simulated PDO mechanism involves extratropical air-sea interaction and oceanic Rossby wave propagation, (ii) the oscillation can exist independent of interactions with the Tropics, but that such interactions can enhance the PDO, and (iii) ocean to atmosphere feedback in the extratropics is critical for establishing the approximately 20-year timescale of the PDO. The spatial pattern of the PDO can be generated from atmospheric variability that occurs independently of ocean-atmosphere feedback, but the existence of a spectral peak depends on active air-sea coupling. The specific interdecadal timescale is strongly influenced by the propagation speed of oceanic Rossby waves in the subtropical and subpolar gyres, as they provide a delayed feedback to the atmosphere.
The simulated PDO has a realistic association with precipitation variations over North America, with a warm phase of the PDO generally associated with positive precipitation anomalies over regions of the western United States. The seasonal dependence of this relationship is also reproduced by the model.
Zhang, Shaoqing, G Han, Y Xue, and Juan Jose Ruiz, August 2015: Data Assimilation in Numerical Weather and Climate Models. Advances in Meteorology, 2015, doi:10.1155/2015/626893.
Changes in the Atlantic Meridional Overturning Circulation (AMOC) could have a profound impact on global scale climate, as indicated by both observations and climate model simulations. This review chapter highlights the global and regional scale climate impacts of the AMOC on paleo and modern climate, the tropical and extra-tropical AMOC fingerprints and the origin of the multidecadal North Atlantic sea surface temperature (NASST) variations, the meridional coherence and propagation of AMOC variations, as well as the impacts of the Nordic Sea overflow on the AMOC and large scale North Atlantic ocean circulation. The results reviewed in this chapter are mainly focused on the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate modeling results and their comparisons with available paleo and modern observations, although modelling results from some other climate models are also discussed.
Aires, F, Pierre Gentine, Kirsten L Findell, Benjamin R Lintner, and Christopher Kerr, March 2014: Neural network-based sensitivity analysis of summertime convection over the continental US. Journal of Climate, 27(5), doi:10.1175/JCLI-D-13-00161.1. [ Abstract ]
Although land-atmosphere coupling is thought to play a role in shaping the mean climate and its variability, it remains difficult to quantify precisely. The present study aims to isolate relationships between early morning surface turbulent fluxes partitioning (i.e., evaporative fraction, EF), and subsequent afternoon convective precipitation frequency and intensity. A general approach involving statistical relationships among input and output variables, known as Sensitivity Analysis (SA), is used to develop a reduced complexity meta-model of the linkage between EF and convective precipitation. Two additional quantities characterizing the early morning convective environment, convective triggering potential (CTP) and low-level humidity (HIlow) deficit, are included. The SA approach is applied to the North American Regional Reanalysis (NARR) for June-July-August (JJA) conditions over the entire continental United States, Mexico, and Central America domain. Five land-atmosphere coupling regimes are objectively characterized based on CTP, HIlow and EF. Two western regimes are largely atmospherically controlled, with a positive link to CTP and a negative link to HIlow. The other three regimes occupy Mexico and the eastern half of the domain and show positive links to EF and negative links to HIlow, suggesting that both surface fluxes and atmospheric humidity play a role in the triggering of rainfall in these regions. The regimes associated with high mean EF also tend to have high sensitivity of rainfall frequency to variations in EF. While these results may be sensitive to the choice of dataset, the approach can be applied across observational, reanalysis, and model datasets and thus represents a potentially powerful tool for inter-comparison and validation as well as to characterize land-atmosphere interactions regimes.
Understanding how different physical processes can shape the probability distribution function (pdf) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture-atmosphere interactions to surface temperature pdfs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back on near-surface climate, in particular temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) earth system model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional ’hotspots’ of land-atmosphere coupling. Moreover, higher-order distribution moments such as skewness and kurtosis are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature pdf. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model.
Cai, Wenju, S Borlace, Matthieu Lengaigne, P van Rensch, Matthew Collins, and Gabriel A Vecchi, et al., February 2014: Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4(2), doi:10.1038/nclimate2100. [ Abstract ]
El Niño events are a prominent feature of climate variability with global climatic impacts. The 1997/98 episode, often referred to as ‘the climate event of the twentieth century, and the 1982/83 extreme El Niño3, featured a pronounced eastward extension of the west Pacific warm pool and development of atmospheric convection, and hence a huge rainfall increase, in the usually cold and dry equatorial eastern Pacific. Such a massive reorganization of atmospheric convection, which we define as an extreme El Niño, severely disrupted global weather patterns, affecting ecosystems agriculture, tropical cyclones, drought, bushfires, floods and other extreme weather events worldwide. Potential future changes in such extreme El Niño occurrences could have profound socio-economic consequences. Here we present climate modelling evidence for a doubling in the occurrences in the future in response to greenhouse warming. We estimate the change by aggregating results from climate models in the Coupled Model Intercomparison Project phases 3 (CMIP3; ref. 10) and 5 (CMIP5; ref. 11) multi-model databases, and a perturbed physics ensemble. The increased frequency arises from a projected surface warming over the eastern equatorial Pacific that occurs faster than in the surrounding ocean waters facilitating more occurrences of atmospheric convection in the eastern equatorial region.
Camargo, Suzana J., Michael K Tippett, Adam H Sobel, Gabriel A Vecchi, and Ming Zhao, December 2014: Testing the performance of tropical cyclone genesis indices in future climates using the HIRAM model. Journal of Climate, 27(24), doi:10.1175/JCLI-D-13-00505.1. [ Abstract ]
Tropical cyclone genesis indices (TCGIs) are functions of the large-scale environment which are designed to be proxies for the probability of tropical cyclone (TC) genesis. While the performance of TCGIs in the current climate can be assessed by direct comparison to TC observations, their ability to represent future TC activity based on projections of the large-scale environment cannot. Here we examine the performance of TCGIs in high-resolution atmospheric model simulations forced with sea surface temperatures (SST) of future, warmer, climate scenarios. We investigate whether the TCGIs derived for the present climate can, when computed from large-scale fields taken from future climate simulations, capture the simulated global mean decreases in TC frequency. The TCGIs differ in their choice of environmental predictors, and several choices of predictors perform well in the present climate. However, some TCGIs which perform well in the present climate do not accurately reproduce the simulated future decrease in TC frequency. This decrease is captured when the humidity predictor is the column saturation deficit rather than relative humidity. Using saturation deficit with relative SST as the other thermodynamic predictor over-predicts the TC frequency decrease, while using potential intensity in place of relative SST as the other thermodynamic predictor gives a good prediction of the decrease’s magnitude. These positive results appear to depend on the spatial and seasonal patterns in the imposed SST changes; none of the indices capture correctly the frequency decrease in simulations with spatially uniform climate forcings, whether a globally uniform increase in SST of 2K, or a doubling of CO2 with no change in SST.
There have been few attempts to quantify errors in various objective analyzed (OA) fields, even though they have potential uncertainties associated with data handling and mapping methods. Here, we compare five different OA fields (EN3, GFDL, IPRC, JAMSTEC, and SIO) for 2008–2011. The variability and linear trends of the upper ocean temperature are very similar in every ocean basin, but the mean values are different from each other. This discrepancy is evident, especially around the southern ocean (± 0.07 °C in the Antarctic Ocean) where Argo observations are still sparse, which is related to different first-guess climatologies and decorrelation length scales applied to individual OA products. In the subpolar North Atlantic, detailed spatial anomalous patterns are also different. Along the boundary current areas, substantial warming (salting) anomalies with respect to WOA09 climatology are depicted by GFDL, IPRC, and SIO. By comparing with statistical bin-averaged fields and data assimilation products, we confirm that this anomalous pattern is robust, but it could be exaggerated when we calculate the anomalies with WOA09 climatology or other OA fields showing a relatively weak horizontal gradient across the boundary current regions.
Chiodi, A M., D E Harrison, and Gabriel A Vecchi, May 2014: Subseasonal atmospheric variability and El Niño waveguide warming; observed effects of the Madden-Julian Oscillation and Westerly Wind Events. Journal of Climate, 27(10), doi:10.1175/JCLI-D-13-00547.1. [ Abstract ]
Westerly Wind Events (WWEs) have previously been shown to initiate equatorial Pacific waveguide warming. The relationship between WWE and Madden-Julian Oscillation (MJO) activity, and the role of MJO events in initiating waveguide warming is reconsidered here, over the 1986-2010 period. WWEs are identified in observations of near surface zonal winds using an objective scheme. MJO events are defined using a widely used index, and 64 are identified that occur when the El Niño-Southern Oscillation (ENSO) is in its neutral-state. 43 of these MJO events have one or more embedded WWEs and 21 have not.
We examine the evolution of sea surface temperature anomaly over the equatorial Pacific waveguide following the westerly surface wind phase of the MJO over the western equatorial Pacific. We find waveguide warming for the MJO+WWE events in similar magnitudes as following the WWEs not embedded in an MJO. There is very little statistically significant waveguide warming following MJO events that do not contain an embedded WWE. The observed SSTA changes are well reproduced in an ocean general circulation model forced with the respective composite wind stress anomalies. Further, we find that the occurrence of an MJO event does not significantly affect the likelihood that a WWE will occur. These results extend and confirm the results of Vecchi (2000) with a near doubling of the period of study. We suggest that understanding the sources and predictability of tropical Pacific Westerly Wind Events remains essential to improving predictions of the onset of El Niño events.
Christensen, J H., K K Kanikicharla, Thomas R Knutson, Hiroyuki Murakami, Mary Jo Nath, and Andrew T Wittenberg, et al., March 2014: Climate Phenomena and their Relevance for Future Regional Climate Change In Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, doi:10.1017/CBO9781107415324.0281217-1308.
Precipitation in austral autumn and winter has declined over parts of southern and especially southwestern Australia in the past few decades. According to observations and climate models, at least part of this decline is associated with changes in large-scale atmospheric circulation, including a poleward movement of the westerly winds and increasing atmospheric surface pressure over parts of southern Australia. Here we use a high-resolution global climate model to analyse the causes of this rainfall decline. In our simulations, many aspects of the observed regional rainfall decline over southern and southwest Australia are reproduced in response to anthropogenic changes in levels of greenhouse gases and ozone in the atmosphere, whereas anthropogenic aerosols do not contribute to the simulated precipitation decline. Simulations of future climate with this model suggest amplified winter drying over most parts of southern Australia in the coming decades in response to a high-end scenario of changes in radiative forcing. The drying is most pronounced over southwest Australia, with total reductions in austral autumn and winter precipitation of approximately 40% by the late twenty-first century.
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. This occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake—a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales. Our study also reveals that equilibrium climate sensitivity estimates based on a widely used method of regressing the Earth’s energy imbalance against surface temperature change are biased. Uncertainty in the magnitude of the feedback effects associated with the magnitude and geographic distribution of ocean heat uptake therefore contributes substantially to the uncertainty in allowable carbon emissions for a given multi-century warming target.
Graham, F, J N Brown, C Langlais, S J Marsland, Andrew T Wittenberg, and N J Holbrook, November 2014: Effectiveness of the Bjerknes stability index in representing ocean dynamics. Climate Dynamics, 43(9-10), doi:10.1007/s00382-014-2062-3. [ Abstract ]
The El Niño-Southern Oscillation (ENSO) is a naturally occurring coupled phenomenon originating in the tropical Pacific Ocean that relies on ocean–atmosphere feedbacks. The Bjerknes stability index (BJ index), derived from the mixed-layer heat budget, aims to quantify the ENSO feedback process in order to explore the linear stability properties of ENSO. More recently, the BJ index has been used for model intercomparisons, particularly for the CMIP3 and CMIP5 models. This study investigates the effectiveness of the BJ index in representing the key ENSO ocean feedbacks—namely the thermocline, zonal advective, and Ekman feedbacks—by evaluating the amplitudes and phases of the BJ index terms against the corresponding heat budget terms from which they were derived. The output from Australian Community Climate and Earth System Simulator Ocean Model (a global ocean/sea ice flux-forced model) is used to calculate the heat budget in the equatorial Pacific. Through the model evaluation process, the robustness of the BJ index terms are tested. We find that the BJ index overestimates the relative importance of the thermocline feedback to the zonal advective feedback when compared with the corresponding terms from the heat budget equation. The assumption of linearity between variables in the BJ index formulation is the primary reason for these differences. Our results imply that a model intercomparison relying on the BJ index to explain ENSO behavior is not necessarily an accurate quantification of dynamical differences between models that are inherently nonlinear. For these reasons, the BJ index may not fully explain underpinning changes in ENSO under global warming scenarios.
We provide an assessment of sea level simulated in a suite of global ocean-sea ice models using the interannual CORE atmospheric state to determine surface ocean boundary buoyancy and momentum fluxes. These CORE-II simulations are compared amongst themselves as well as to observation-based estimates. We focus on the final 15 years of the simulations (1993-2007), as this is a period where the CORE-II atmospheric state is well sampled, and it allows us to compare sea level related fields to both satellite and in situ analyses. The ensemble mean of the CORE-II simulations broadly agree with various global and regional observation-based analyses during this period, though with the global mean thermosteric sea level rise biased low relative to observation-based analyses. The simulations reveal a positive trend in dynamic sea level in the west Pacific and negative trend in the east, with this trend arising from wind shifts and regional changes in upper 700 m ocean heat content. The models also exhibit a thermosteric sea level rise in the subpolar North Atlantic associated with a transition around 1995/1996 of the North Atlantic Oscillation to its negative phase, and the advection of warm subtropical waters into the subpolar gyre. Sea level trends are predominantly associated with steric trends, with thermosteric effects generally far larger than halosteric effects, except in the Arctic and North Atlantic. There is a general anti-correlation between thermosteric and halosteric effects for much of the World Ocean, associated with density compensated changes.
Guillod, B P., B Orlowsky, D Miralles, A J Teuling, P D Blanken, N Buchmann, Philippe Ciais, Michael Ek, and Kirsten L Findell, et al., August 2014: Land surface controls on afternoon precipitation diagnosed from observational data: uncertainties, confounding factors and the possible role of vegetation interception. Atmospheric Chemistry and Physics, 14(16), doi:10.5194/acp-14-8343-2014. [ Abstract ]
The feedback between soil moisture and precipitation has long been a topic of interest due to its potential for improving weather and seasonal forecasts. The generally proposed mechanism assumes a control of soil moisture on precipitation via the partitioning of the surface turbulent heat fluxes, as assessed via the Evaporative Fraction, EF, i.e. the ratio of latent heat to the sum of latent and sensible heat, in particular under convective conditions. Our study investigates the poorly understood link between EF and precipitation by investigating the impact of before-noon EF on the frequency of afternoon precipitation over the contiguous US, using a statistical analysis of the relationship between multiple datasets of EF and precipitation. We analyze remote sensing data products (EF from GLEAM, Global Land Evaporation: the Amsterdam Methodology, based on satellite observations; and radar precipitation from NEXRAD, the NEXt generation weather RADar system), FLUXNET station data, and the North American Regional Reanalysis (NARR). While most datasets agree on the existence of regions of positive relationship between between EF and precipitation in the Eastern and Southwestern US, observation-based estimates (GLEAM, NEXRAD and to some extent FLUXNET) also indicate a strong relationship in the Central US which is not found in NARR. Investigating these differences, we find that much of these relationships can be explained by precipitation persistence alone, with ambiguous results on the additional role of EF in causing afternoon precipitation. Regional analyses reveal contrasting mechanisms over different regions. Over the Eastern US, our analyses suggest that the apparent EF-precipitation coupling takes place on a short day-to-day time scale and is either atmospherically controlled (from precipitation persistence and potential evaporation) or driven by vegetation interception and subsequent re-evaporation (rather than soil moisture and related plant transpiration/bare soil evaporation), in line with the high forest cover and the wet regime of that region. Over the Central and Southwestern US, the impact of EF on convection triggering is additionally linked to soil moisture variations, owing to the soil moisture–limited climate regime.
Han, G, X-F Zhang, and Shaoqing Zhang, et al., March 2014: Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model. Nonlinear Processes in Geophysics, 21(2), doi:10.5194/npg-21-357-2014. [ Abstract ]
Imperfect dynamical core is an important source of model biases that adversely impact on the model simulation and predictability of a coupled system. With a simple pycnocline prediction model, in this study, we show the mitigation of model biases through parameter optimization when the assimilation model consists of a "biased" time-differencing. Here, the "biased" time-differencing is defined by a different time-differencing scheme from the "truth" model that is used to produce "observations", which generates different mean values, climatology and variability of the assimilation model from the "truth" model. A series of assimilation experiments is performed to explore the impact of parameter optimization on model bias mitigation and climate estimation, as well as the role of different media parameter estimations. While the stochastic "physics" implemented by perturbing parameters can enhance the ensemble spread significantly and improve the representation of the model ensemble, signal-enhanced parameter estimation is able to mitigate the model biases on mean values and climatology, thus further improving the accuracy of estimated climate states, especially for the low-frequency signals. In addition, in a multiple timescale coupled system, parameters pertinent to low-frequency components have more impact on climate signals. Results also suggest that deep ocean observations may be indispensable for improving the accuracy of climate estimation, especially for low-frequency signals.
Hawkins, E, B Anderson, N S Diffenbaugh, I Mahlstein, Richard A Betts, Gabriele Hegerl, M Joshi, Reto Knutti, D McNeall, S Solomon, Rowan Sutton, J Syktus, and Gabriel A Vecchi, July 2014: Uncertainties in the timing of unprecedented climates. Nature, 511(7507), doi:10.1038/nature13523. [ Abstract ]
The question of when the signal of climate change will emerge from the background noise of climate variability—the ‘time of emergence’—is potentially important for adaptation planning. Mora et al.1 presented precise projections of the time of emergence of unprecedented regional climates. However, their methodology produces artificially early dates at which specific regions will permanently experience unprecedented climates and artificially low uncertainty in those dates everywhere. This overconfidence could impair the effectiveness of climate risk management decisions.
Holbrook, N J., J Li, Matthew Collins, Emanuele Di Lorenzo, Fei-Fei Jin, and Thomas R Knutson, et al., August 2014: Decadal Climate Variability and Cross-Scale Interactions: ICCL 2013 Expert Assessment Workshop. Bulletin of the American Meteorological Society, 95(8), doi:10.1175/BAMS-D-13-00201.1.
Irish, J L., A Sleath, M A Cialone, Thomas R Knutson, and R E Jensen, March 2014: Simulations of Hurricane Katrina (2005) under sea level and climate conditions for 1900. Climatic Change, 122, doi:10.1007/s10584-013-1011-1. [ Abstract ]
Global warming may result in substantial sea level rise and more intense hurricanes over the next century, leading to more severe coastal flooding. Here, observed climate and sea level trends over the last century (c. 1900s to 2000s) are used to provide insight regarding future coastal inundation trends. The actual impacts of Hurricane Katrina (2005) in New Orleans are compared with the impacts of a similar hypothetical hurricane occurring c. 1900. Estimated regional sea level rise since 1900 of 0.75 m, which contains a dominant land subsidence contribution (0.57 m), serves as a ‘prototype’ for future climate-change induced sea level rise in other regions. Landform conditions c. 1900 were estimated by changing frictional resistance based on expected additional wetlands at lower sea levels. Surge simulations suggest that flood elevations would have been 15 to 60 % lower c. 1900 than the conditions observed in 2005. This drastic change suggests that significantly more flood damage occurred in 2005 than would have occurred if sea level and climate conditions had been like those c. 1900. We further show that, in New Orleans, sea level rise dominates surge-induced flooding changes, not only by increasing mean sea level, but also by leading to decreased wetland area. Together, these effects enable larger surges. Projecting forward, future global sea level changes of the magnitude examined here are expected to lead to increased flooding in coastal regions, even if the storm climate is unchanged. Such flooding increases in densely populated areas would presumably lead to more widespread destruction.
The high mountains of Asia, including the Karakoram, Himalayas and Tibetan Plateau, combine to form a region of perplexing hydroclimate changes. Glaciers have exhibited mass stability or even expansion in the Karakoram region1, 2, 3, contrasting with glacial mass loss across the nearby Himalayas and Tibetan Plateau1, 4, a pattern that has been termed the Karakoram anomaly. However, the remote location, complex terrain and multi-country fabric of high-mountain Asia have made it difficult to maintain longer-term monitoring systems of the meteorological components that may have influenced glacial change. Here we compare a set of high-resolution climate model simulations from 1861 to 2100 with the latest available observations to focus on the distinct seasonal cycles and resulting climate change signatures of Asia’s high-mountain ranges. We find that the Karakoram seasonal cycle is dominated by non-monsoonal winter precipitation, which uniquely protects it from reductions in annual snowfall under climate warming over the twenty-first century. The simulations show that climate change signals are detectable only with long and continuous records, and at specific elevations. Our findings suggest a meteorological mechanism for regional differences in the glacier response to climate warming.
Karamperidou, C, Mark Cane, U Lall, and Andrew T Wittenberg, January 2014: Intrinsic modulation of ENSO predictability viewed through a local Lyapunov lens. Climate Dynamics, 42(1-2), doi:10.1007/s00382-013-1759-z. [ Abstract ]
The presence of rich ENSO variability in the long unforced simulation of GFDL’s CM2.1 motivates the use of tools from dynamical systems theory to study variability in ENSO predictability, and its connections to ENSO magnitude, frequency, and physical evolution. Local Lyapunov exponents (LLEs) estimated from the monthly NINO3 SSTa model output are used to characterize periods of increased or decreased predictability. The LLEs describe the growth of infinitesimal perturbations due to internal variability, and are a measure of the immediate predictive uncertainty at any given point in the system phase-space. The LLE-derived predictability estimates are compared with those obtained from the error growth in a set of re-forecast experiments with CM2.1. It is shown that the LLEs underestimate the error growth for short forecast lead times (less than 8 months), while they overestimate it for longer lead times. The departure of LLE-derived error growth rates from the re-forecast rates is a linear function of forecast lead time, and is also sensitive to the length of the time series used for the LLE calculation. The LLE-derived error growth rate is closer to that estimated from the re-forecasts for a lead time of 4 months. In the 2,000-year long simulation, the LLE-derived predictability at the 4-month lead time varies (multi)decadally only by 9–18 %. Active ENSO periods are more predictable than inactive ones, while epochs with regular periodicity and moderate magnitude are classified as the most predictable by the LLEs. Events with a deeper thermocline in the west Pacific up to five years prior to their peak, along with an earlier deepening of the thermocline in the east Pacific in the months preceding the peak, are classified as more predictable. Also, the GCM is found to be less predictable than nature under this measure of predictability.
Kessler, William S., Tong Lee, Matthew Collins, Eric Guilyardi, D Chen, Andrew T Wittenberg, Gabriel A Vecchi, William G Large, and D Anderson, January 2014: White Paper #3 -- ENSO research: The overarching science drivers and requirements for observations In Report of the Tropical Pacific Observing System 2020 (TPOS 2020) Workshop, Volume II, La Jolla, CA, WMO and Intergovernmental Oceanographic Commission, 27-30.
Global tropical cyclone (TC) activity is simulated by the Geophysical Fluid Dynamics Laboratory (GFDL) CM2.5, which is a fully coupled global climate model with horizontal resolution of about 50km for atmosphere and 25 km for ocean. The present climate simulation shows fairly realistic global TC frequency, seasonal cycle, and geographical distribution. The model has some notable biases in regional TC activity, including simulating too few TCs in the North Atlantic. The regional biases in TC activity are associated with simulation biases in the large-scale environment such as sea surface temperature, vertical wind shear, and vertical velocity. Despite these biases, the model simulates the large-scale variations of TC activity induced by El Nino/Southern Oscillation fairly realistically.
The response of TC activity in the model to global warming is investigated by comparing the present climate with a CO2 doubling experiment. Globally, TC frequency decreases (-19%) while the intensity increases (+2.7%) in response to CO2 doubling, consistent with previous studies. The average TC lifetime decreases by -4.6%, while the TC size and rainfall increase by about 3% and 12%, respectively. These changes are generally reproduced across the different basins in terms of the sign of the change, although the percent changes vary from basin to basin and within individual basins. For the Atlantic basin, although there is an overall reduction in frequency from CO2 doubling, the warmed climate exhibits increased interannual hurricane frequency variability so that the simulated Atlantic TC activity is enhanced more during unusually warm years in the CO2-warmed climate relative to that in unusually warm years in the control climate.
Kirtman, Ben P., and Anthony Rosati, et al., April 2014: The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction. Bulletin of the American Meteorological Society, 95(4), doi:10.1175/BAMS-D-12-00050.1. [ Abstract ]
The recent US National Academies report “Assessment of Intraseasonal to Interannual Climate Prediction and Predictability” was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.
The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model.
Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.
Kirtman, Ben P., Arlene M Fiore, and Gabriel A Vecchi, et al., March 2014: Near-term climate change: Projections and predictability In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 953-1028.
In this extended abstract, we report on progress in two areas of research at GFDL relating to Indian Ocean regional climate and climate change. The first topic is an assessment of regional surface temperature trends in the Indian Ocean and surrounding region. Here we illustrate the use of a multi-model approach (CMIP3 or CMIP5 model ensembles) to assess whether an anthropogenic warming signal has emerged in the historical data, including identification of where the observed trends are consistent or not with current climate models. Trends that are consistent with All Forcing runs but inconsistent with Natural Forcing Only runs are ones which we can attribute, at least in part, to anthropogenic forcing.
Knutson, Thomas R., Fanrong Zeng, and Andrew T Wittenberg, September 2014: Seasonal and Annual Mean Precipitation Extremes Occurring During 2013: A U.S. Focused Analysis [in "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 95(9), S19-S23. [ Abstract ]
Explaining Extreme Events of 2013 from a Climate Perspective. Bull. Amer. Meteor. Soc., 95, S1–S104.
doi: http://dx.doi.org/10.1175/1520-0477-95.9.S1.1
Knutson, Thomas R., Fanrong Zeng, and Andrew T Wittenberg, September 2014: Multimodel Assessment of Extreme Annual-Mean Warm Anomalies During 2013 over Regions of Australia and the Western Tropical Pacific [in "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 95(9), S26-S30. [ Abstract ]
Explaining Extreme Events of 2013 from a Climate Perspective. Bull. Amer. Meteor. Soc., 95, S1–S104.
doi: http://dx.doi.org/10.1175/1520-0477-95.9.S1.1
Kossin, James, Kerry A Emanuel, and Gabriel A Vecchi, May 2014: The poleward migration of the location of tropical cyclone maximum intensity. Nature, 509(7500), doi:10.1038/nature13278. [ Abstract ]
Temporally inconsistent and potentially unreliable global historical data hinder the detection of trends in tropical cyclone activity. This limits our confidence in evaluating proposed linkages between observed trends in tropical cyclones and in the environment. Here we mitigate this difficulty by focusing on a metric that is comparatively insensitive to past data uncertainty, and identify a pronounced poleward migration in the average latitude at which tropical cyclones have achieved their lifetime-maximum intensity over the past 30 years. The poleward trends are evident in the global historical data in both the Northern and the Southern hemispheres, with rates of 53 and 62 kilometres per decade, respectively, and are statistically significant. When considered together, the trends in each hemisphere depict a global-average migration of tropical cyclone activity away from the tropics at a rate of about one degree of latitude per decade, which lies within the range of estimates of the observed expansion of the tropics over the same period6. The global migration remains evident and statistically significant under a formal data homogenization procedure, and is unlikely to be a data artefact. The migration away from the tropics is apparently linked to marked changes in the mean meridional structure of environmental vertical wind shear and potential intensity, and can plausibly be linked to tropical expansion, which is thought to have anthropogenic contributions.
We developed a~process model LM3-TAN to assess the combined effects of direct human influences and climate change on Terrestrial and Aquatic Nitrogen (TAN) cycling. The model was developed by expanding NOAA's Geophysical Fluid Dynamics Laboratory land model LM3V-N of coupled terrestrial carbon and nitrogen (C-N) cycling and including new N cycling processes and inputs such as a~soil denitrification, point N sources to streams (i.e. sewage), and stream transport and microbial processes. Because the model integrates ecological, hydrological, and biogeochemical processes, it captures key controls of transport and fate of N in the vegetation-soil-river system in a comprehensive and consistent framework which is responsive to climatic variations and land use changes. We applied the model at 1/8° resolution for a study of the Susquehanna River basin. We simulated with LM3-TAN stream dissolved organic-N, ammonium-N, and nitrate-N loads throughout the river network, and we evaluated the modeled loads for 1986–2005 using data from 15 monitoring stations as well as a reported budget for the entire basin. By accounting for inter-annual hydrologic variability, the model was able to capture inter-annual variations of stream N loadings. While the model was calibrated with the stream N loads only at the last downstream station Marietta (40.02° N, 76.32° W), it captured the N loads well at multiple locations within the basin with different climate regimes, land use types, and associated N sources and transformations in the sub-basins. Furthermore, the calculated and previously reported N budgets agreed well at the level of the whole Susquehanna watershed. Here we illustrate how point and non-point N sources contribute to the various ecosystems are stored, lost, and exported via the river. Local analysis for 6 sub-basins showed combined effects of land use and climate on the soil denitrification rates, with the highest rates in the Lower Susquehanna sub-basin (extensive agriculture; Atlantic coastal climate) and the lowest rates in the West Branch Susquehanna sub-basin (mostly forest; Great Lakes and Midwest climate). In the re-growing secondary forests, most of the N from non-point sources was stored in the vegetation and soil, but in the agricultural lands most N inputs were removed by soil denitrification indicating that anthropogenic N applications could drive substantial increase of N2O emission, an intermediate of the denitrification process.
Lee, Sang-Ki, P DiNezio, E-S Chung, S-W Yeh, Andrew T Wittenberg, and Chunzai Wang, December 2014: Spring persistence, transition and resurgence of El Niño. Geophysical Research Letters, 41(23), doi:10.1002/2014GL062484. [ Abstract ]
We present a systematic exploration of differences in the spatio-temporal sea surface temperature (SST) evolution along the equatorial Pacific among observed El Niño events. This inter-El Niño variability is captured by two leading orthogonal modes, which explain more than 60% of the inter-event variance. The first mode illustrates the extent to which warm SST anomalies (SSTAs) in the eastern tropical Pacific (EP) persist into the boreal spring after the peak of El Niño. Our analysis suggests that a strong El Niño event tends to persist into the boreal spring in the EP, whereas a weak El Niño favors a rapid development of cold SSTAs in the EP shortly after its peak. The second mode captures the transition and resurgence of El Niño in the following year. An early-onset El Niño tends to favor a transition to La Niña, whereas a late-onset El Niño tends to persist long enough to produce another El Niño event. The spatio-temporal evolution of several El Niño events during 1949–2013 can be efficiently summarized in terms of these two modes, which are not mutually exclusive, but exhibit distinctive coupled atmosphere–ocean dynamics.
Liu, Y, Zhengyu Liu, and Shaoqing Zhang, et al., June 2014: Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method. Journal of Climate, 27(11), doi:10.1175/JCLI-D-13-00091.1. [ Abstract ]
Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system as a coupled ocean-atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, we propose an adaptive spatial average (ASA) algorithm to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; the “good” values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.
Liu, Y, Zhengyu Liu, and Shaoqing Zhang, et al., September 2014: Ensemble-Based Parameter Estimation in a Coupled General Circulation Model. Journal of Climate, 27(18), doi:10.1175/JCLI-D-13-00406.1. [ Abstract ]
Parameter estimation provides potentially a powerful approach to reduce model bias for complex climate models. Here, in the twin experiment framework, we perform the first parameter estimation in a fully-coupled ocean-atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. We first perform single parameter estimation and then multiple-parameter estimation. In the case of the single parameter estimation, the error of the parameter (solar penetration depth, SPD) is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than the single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Overall, our study suggests the feasibility of the ensemble based parameter estimation in a fully coupled general circulation model.
Lynch-Stieglitz, J, M Schmidt, L G Henry, W B Curry, L C Skinner, S Mulitza, Rong Zhang, and P Chang, February 2014: Muted change in Atlantic overturning circulation over some glacial-aged Heinrich events. Nature Geoscience, 7(2), doi:10.1038/ngeo2045. [ Abstract ]
Heinrich events—surges of icebergs into the North Atlantic Ocean—punctuated the last glacial period. The events are associated with millennial-scale cooling in the Northern Hemisphere. Fresh water from the melting icebergs is thought to have interrupted the Atlantic meridional overturning circulation, thus minimizing heat transport into the northern North Atlantic. The northward flow of warm water passes through the Florida Straits and is reflected in the distribution of seawater properties in this region. Here we investigate the northward flow through this region over the past 40,000 years using oxygen isotope measurements of benthic foraminifera from two cores on either side of the Florida Straits. These measurements allow us to estimate water density, which is related to flow through the thermal wind balance. We infer a substantial reduction of flow during Heinrich Event 1 and the Younger Dryas cooling, but little change during Heinrich Events 2 and 3, which occurred during an especially cold phase of the last glacial period. We speculate that because glacial circulation was already weakened before the onset of Heinrich Events 2 and 3, freshwater forcing had little additional effect. However, low-latitude climate perturbations were observed during all events. We therefore suggest that these perturbations may not have been directly caused by changes in heat transport associated with Atlantic overturning circulation as commonly assumed.
This paper provides an update on research in the relatively new and fast moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about six to nine years. Recent multi-model results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multi-model ensemble decadal hindcasts than in single model results, with multi-model initialized predictions for near term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño-Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
Coupled climate model simulations of volcanic eruptions and abrupt changes in CO2 concentration are compared in multiple realizations of Geophysical Fluid Dynamics Laboratory’s (GFDL) CM2.1. The change in global-mean surface temperature (GMST) is analyzed to determine whether a fast component of the climate sensitivity of relevance to the transient climate response (TCR, defined with the 1% yr−1 CO2-increase scenario) can be estimated from shorter-timescale climate changes. The fast component of the climate sensitivity estimated from the response of the climate model to volcanic forcing is similar to that of the simulations forced by abrupt CO2 changes, but is 5–15% smaller than the TCR. In addition, the partition between the top-of-atmosphere radiative restoring and ocean heat uptake is similar across radiative forcing agents. The possible asymmetry between warming and cooling climate perturbations, which may affect the utility of volcanic eruptions for estimating the TCR, is assessed by comparing simulations of abrupt CO2 doubling to abrupt CO2 halving. There is slightly less (~5%) GMST change in 0.5 × CO2 simulations than in 2 × CO2 simulations on the short (~10-yr) timescales relevant to the fast component of the volcanic signal. However, inferring the TCR from volcanic eruptions is more sensitive to uncertainties from internal climate variability and the estimation procedure.
The response of the GMST to volcanic eruptions is similar in GFDL’s CM2.1 and CM3, even though the latter has a higher TCR associated with a multidecadal timescale in its response. This is consistent with the expectation that the fast component of the climate sensitivity inferred from volcanic eruptions is a lower bound for the TCR.
“LM3” is a new model of terrestrial water, energy, and carbon, intended for use in global hydrologic analyses and as a component of earth-system and physical-climate models. It is designed to improve upon the performance and extend the scope of the predecessor Land Dynamics (LaD) and LM3V models, by quantifying better the physical controls of climate and biogeochemistry and by relating more directly to components of the global water system that touch human concerns. LM3 includes multi-layer representations of temperature, liquid-water content, and ice content of both snow pack and macroporous soil/bedrock; topography-based description of saturated area and groundwater discharge; and transport of runoff to the ocean via a global river and lake network. Sensible heat transport by water mass is accounted throughout for a complete energy balance. Carbon and vegetation dynamics and biophysics are represented as in the model LM3V. In numerical experiments, LM3 avoids some of the limitations of the LaD model and provides qualitatively (though not always quantitatively) reasonable estimates, from a global perspective, of observed spatial and/or temporal variations of vegetation density, albedo, streamflow, water-table depth, permafrost, and lake levels. Amplitude and phase of annual cycle of total water storage are simulated well. Realism of modeled lake levels varies widely. The water table tends to be consistently too shallow in humid regions. Biophysical properties have an artificial step-wise spatial structure, and equilibrium vegetation is sensitive to initial conditions. Explicit resolution of thick (>100 m) unsaturated zones and permafrost is possible, but only at the cost of long (>>300 y) model spin-up times.
Decadal prediction experiments were conducted as part of CMIP5 using the GFDL-CM2.1 forecast system. The abrupt warming of the North Atlantic subpolar gyre (SPG) that was observed in the mid 1990s is considered as a case study to evaluate our forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-90s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 90s show a warming of the SPG, however, only the ensemble mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predict the mid 90s warming. The successful initialized forecasts show an increased Atlantic Meridional Overturning Circulation and North Atlantic current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and subsequently a warming and spin down of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea-ice concentration over the Arctic, an enhanced warming over central US during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic Multidecadal Oscillation, which is encouraging for future predictions of North Atlantic climate.
We present seasonal predictions of Arctic sea ice extent (SIE) over the 1982-2013 period using two suites of retrospective forecasts initialized from a fully coupled ocean-atmosphere-sea ice assimilation system. High skill scores are found in predicting year-to-year fluctuations of Arctic SIE, with significant correlations up to 7 month ahead for September detrended anomalies. Predictions over the recent era, which coincides with an improved observational coverage, outperform the earlier period for most target months. We find, however, a degradation of skill in September during the last decade, a period of sea ice thinning in observations. The two prediction models, CM2.1 and FLOR, share very similar ocean and ice component and initialization but differ by their atmospheric component. FLOR has improved climatological atmospheric circulation and sea ice mean state but its skill is overall similar to CM2.1 for most seasons, which suggests a key role for initial conditions in predicting seasonal SIE fluctuations.
Rochetin, Nicolas, Benjamin R Lintner, Kirsten L Findell, Adam H Sobel, and Pierre Gentine, December 2014: Radiative–Convective Equilibrium over a Land Surface. Journal of Climate, 27(23), doi:10.1175/JCLI-D-13-00654.1. [ Abstract ]
Radiative–convective equilibrium (RCE) describes an idealized state of the atmosphere in which the vertical temperature profile is determined by a balance between radiative and convective fluxes. While RCE has been applied extensively over oceans, its application over the land surface has been limited. The present study explores the properties of RCE over land using an atmospheric single-column model (SCM) from the Laboratoire de Météorologie Dynamique–Zoom, version 5B (LMDZ5B) general circulation model coupled in temperature and moisture to a land surface model using a simplified bucket model with finite moisture capacity. Given the presence of a large-amplitude diurnal heat flux cycle, the resultant RCE exhibits multiple equilibria when conditions are neither strictly water nor energy limited. By varying top-of-atmosphere insolation (through changes in latitude), total system water content, and initial temperature conditions the sensitivity of the land RCE states is assessed, with particular emphasis on the role of clouds. Based on this analysis, it appears that a necessary condition for the model to exhibit multiple equilibria is the presence of low-level clouds coupled to the diurnal cycle of radiation. In addition the simulated surface precipitation rate varies nonmonotonically with latitude as a result of a tradeoff between in-cloud rain rate and subcloud rain reevaporation, thus underscoring the importance of subcloud layer processes and unsaturated downdrafts. It is shown that clouds, especially at low levels, are key elements of the internal variability of the coupled land–atmosphere system through their feedback on radiation.
Scoccimarro, E, Silvio Gualdi, Gabriele Villarini, Gabriel A Vecchi, and Ming Zhao, et al., June 2014: Intense Precipitation Events Associated with Landfalling Tropical Cyclones in response to a Warmer Climate and increased CO2. Journal of Climate, 27(12), doi:10.1175/JCLI-D-14-00065.1. [ Abstract ]
In this work the authors investigate possible changes in the intensity of rainfall events associated with tropical cyclones (TCs) under idealized forcing scenarios, including a uniformly warmer climate, with a special focus on landfalling storms. A new set of experiments designed within the U.S. Climate Variability and Predictability (CLIVAR) Hurricane Working Group allows disentangling the relative role of changes in atmospheric carbon dioxide from that played by sea surface temperature (SST) in changing the amount of precipitation associated with TCs in a warmer world. Compared to the present-day simulation, an increase in TC precipitation was found under the scenarios involving SST increases. On the other hand, in a CO2-doubling-only scenario, the changes in TC rainfall are small and it was found that, on average, TC rainfall tends to decrease compared to the present-day climate. The results of this study highlight the contribution of landfalling TCs to the projected increase in the precipitation changes affecting the tropical coastal regions.
Tietsche, Steffen, J J Day, Virginie Guemas, William J Hurlin, S P E Keeley, D Matei, and Rym Msadek, et al., February 2014: Seasonal to interannual Arctic sea-ice predictability in current GCMs. Geophysical Research Letters, 41(3), doi:10.1002/2013GL058755. [ Abstract ]
We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.
In our original paper (Vecchi et al., 2013, hereafter V13) we stated “the skill in the initialized forecasts comes in large part from the persistence of the mid-1990s shift by the initialized forecasts, rather than from predicting its evolution”. Smith et al (2013, hereafter S13) challenge that assertion, contending that DePreSys was able to make a successful retrospective forecast of that shift. We stand by our original assertion, and present additional analyses using output from DePreSys retrospective forecasts to support our assessment.
Tropical cyclones (TCs) are a hazard to life and property (1, 2), as was tragically apparent following Super Typhoon Haiyan's landfall in the Philippines in 2013 and Hurricane/extratropical system Sandy's landfall in the New York tri-state area in 2012. Yet TCs also provide vital water, sometimes relieving drought (3). Predictions of the path and intensity of individual TCs are usually sufficiently good several days in advance that action can be taken. In contrast, predictions of seasonal TC activity months in advance must still be made more regionally relevant to produce information that can be acted on, for example, to improve storm preparedness.
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system, therefore understanding and predicting TC location, intensity and frequency is of both societal and scientific significance. Methodologies exist to predict basin-wide, seasonally-aggregated TC activity months, seasons and even years in advance. We show that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basin-wide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal timescales, and is comprised of high-resolution (50km×50km) atmosphere and land components, and more moderate resolution (~100km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux-adjustment.” We perform a suite of 12-month duration retrospective forecasts over the 1981-2012 period, after initializing the climate model to observationally-constrained conditions at the start of each forecast period – using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basin-wide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally-aggregated regional TC activity months in advance are feasible.
Villarini, Gabriele, R Goska, James A Smith, and Gabriel A Vecchi, September 2014: North Atlantic Tropical Cyclones and U.S. Flooding. Bulletin of the American Meteorological Society, 95(9), doi:10.1175/BAMS-D-13-00060.1. [ Abstract ]
Riverine flooding associated with North Atlantic tropical cyclones (TCs) is responsible for large societal and economic impacts. The effects of TC flooding are not limited to the coastal regions, but affect large areas away from the coast, and often away from the center of the storm. Despite these important repercussions, inland TC flooding has received relatively little attention in the scientific literature, although there has been growing media attention following Hurricanes Irene (2011) and Sandy (2012). Based on discharge data from 1981 to 2011, we provide a climatological view of inland flooding associated with TCs, leveraging on the wealth of discharge measurements collected, archived, and disseminated by the U.S. Geological Survey (USGS). Florida and the eastern seaboard of the United States (from South Carolina to Maine and Vermont) are the areas that are the most susceptible to TC flooding, with typical TC flood peaks that are two to six times larger than the local 10-year flood peak, causing major flooding. We also identify a secondary swath of extensive TC-induced flooding in the central United States. These results indicate that flooding from TCs is not solely a coastal phenomenon, but affects much larger areas of the United States, as far inland as Illinois, Wisconsin and Michigan. Moreover, we highlight the dependence of the frequency and magnitude of TC flood peaks on large scale climate indices, and highlight the role played by the North Atlantic Oscillation and the El Niño-Southern Oscillation phenomenon (ENSO), suggesting potential sources of extended-range predictability.
Heavy rainfall and flooding associated with tropical cyclones (TCs) are responsible for a large number of fatalities and economic damage worldwide. Despite their large socio-economic impacts, research into heavy rainfall and flooding associated with TCs has received limited attention to date, and still represents a major challenge. Our capability to adapt to future changes in heavy rainfall and flooding associated with TCs is inextricably linked to and informed by our understanding of the sensitivity of TC rainfall to likely future forcing mechanisms. Here we use a set of idealized high-resolution atmospheric model experiments produced as part of the U.S. CLIVAR Hurricane Working Group activity to examine TC response to idealized global-scale perturbations: the doubling of CO2, uniform 2K increases in global sea surface temperature (SST), and their combined impact. As a preliminary but key step, daily rainfall patterns of composite TCs within climate model outputs are first compared and contrasted to the observational records. To assess similarities and differences across different regions in response to the warming scenarios, analyses are performed at the global and hemispheric scales and in six global TC ocean basins. The results indicate a reduction in TC daily precipitation rates in the doubling CO2 scenario (on the order of 5% globally), and an increase in TC rainfall rates associated with a uniform increase of 2K in SST (both alone and in combination with CO2 doubling; on the order of 10-20% globally).
Wang, H, L Long, Arun Kumar, Wanqui Wang, J-K E Shemm, Ming Zhao, and Gabriel A Vecchi, et al., August 2014: How well do global climate models simulate the variability of Atlantic tropical cyclones associated with ENSO?Journal of Climate, 27(15), doi:10.1175/JCLI-D-13-00625.1. [ Abstract ]
The variability of Atlantic tropical cyclones (TCs) associated with El Niño–Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. CLIVAR Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multi-model ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Niño events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Niño and stronger activity during La Niña. For CP El Niño, there is a slight increase in the number of TCs as compared with EP El Niño. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Niño as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Niño, as well as the model bias in the mean circulation.
We investigate the influence of ocean component resolution on simulation of climate sensitivity using variants of the GFDL CM2.5 climate model incorporating eddy-resolving (1/10o) and eddy-parameterizing (1o) ocean resolutions. Two parameterization configurations of the coarse-resolution model are used yielding a three-model suite with significant variation in the transient climate response (TCR). The variation of TCR in this suite and in an enhanced group of 10 GFDL models is found to be strongly associated with the control climate Atlantic meridional overturning circulation (AMOC) magnitude and its decline under forcing. We find it is the AMOC behavior rather than resolution per se that accounts for most of the TCR differences. A smaller difference in TCR stems from the eddy-resolving model having more Southern Ocean surface warming than the coarse models.
Observations and climate simulations exhibit epochs of extreme El Niño/Southern Oscillation (ENSO) behavior that can persist for decades. Previous studies have revealed a wide range of ENSO responses to forcings from greenhouse gases, aerosols, and orbital variations – but they have also shown that interdecadal modulation of ENSO can arise even without such forcings. The present study examines the predictability of this intrinsically-generated component of ENSO modulation, using a 4000-year unforced control run from a global coupled GCM (GFDL-CM2.1) with a fairly realistic representation of ENSO. Extreme ENSO epochs from the unforced simulation are reforecast using the same (“perfect”) model, but slightly-perturbed initial conditions. These 40-member reforecast ensembles display potential predictability of the ENSO trajectory, extending up to several years ahead. However, no decadal-scale predictability of ENSO behavior is found. This indicates that multidecadal epochs of extreme ENSO behavior can arise not only intrinsically, but delicately, and entirely at random. Previous work had shown that CM2.1 generates strong, reasonably-realistic, decadally-predictable high-latitude climate signals, as well as tropical and extratropical decadal signals that interact with ENSO. However, those slow variations appear not to lend significant decadal predictability to this model’s ENSO behavior, at least in the absence of external forcings. While the potential implications of these results are sobering for decadal predictability, they also suggest an expedited approach to model evaluation and development – in which large ensembles of short runs are executed in parallel, to quickly and robustly evaluate simulations of ENSO. Further implications are discussed for decadal prediction, attribution of past and future ENSO variations, and societal vulnerability.
Wu, Liang, C Chou, C-T Chen, Ronghui Huang, Thomas R Knutson, Joseph J Sirutis, Stephen T Garner, and Christopher Kerr, et al., May 2014: Simulations of the present and late 21st century western North Pacific tropical cyclone activity using a regional model. Journal of Climate, 27(9), doi:10.1175/JCLI-D-12-00830.1. [ Abstract ]
A high-resolution regional atmospheric model is used to simulate present-day western North Pacific (WNP) tropical cyclone (TC) activity and investigate the projected changes for the late 21st century. Compared to observations, the model can realistically simulate many basic features of the WNP TC activity climatology, such as the TC genesis location, track, and lifetime. A number of spatial and temporal features of observed TC interannual variability are captured, although observed variations in basin-wide TC number are not. A relatively well-simulated feature is the contrast of years when the Asian summer monsoon trough extends eastward (retreats westward), more (fewer) TCs form within the southeastern quadrant of the WNP, and the corresponding TC activity is above (below) normal over most parts of the WNP east of 125°E. Future projections with the Coupled Model Intercomparison Project 3 (CMIP3) A1B scenario show a weak tendency for decreases in the number of WNP TCs, and of increases in the more intense TCs; these simulated changes are significant at the 80% level. The present-day simulation of intensity is limited to storms of intensity less than about 55 m s-1. There is also a weak (80% significance level) tendency for projected WNP TC activity to shift poleward under global warming. A regional-scale feature is a projected increase of the TC activity north of Taiwan, which would imply an increase in TCs making landfall in North China, the Korean Peninsula and parts of Japan. However, given the weak statistical significance found for the simulated changes, an assessment of the robustness of such regional-scale projections will require further study.
Wu, Xinrong, Wei Li, G Han, and Shaoqing Zhang, et al., October 2014: A Compensatory Approach of the Fixed Localization in EnKF. Monthly Weather Review, 142(10), doi:10.1175/MWR-D-13-00369.1. [ Abstract ]
While fixed covariance localization can greatly increase the reliability of the background error covariance in filtering by suppressing the long-distance spurious correlations evaluated by a finite ensemble, it may degrade the assimilation quality in an ensemble Kalman filter (EnKF) as a result of restricted longwave information. Tuning an optimal cutoff distance is usually very expensive and time consuming, especially for a general circulation model (GCM). Here the authors present an approach to compensate the demerit in fixed localization. At each analysis step, after the standard EnKF is done, a multiple-scale analysis technique is used to extract longwave information from the observational residual (referred to the EnKF ensemble mean). Within a biased twin-experiment framework consisting of a global barotropical spectral model and an idealized observing system, the performance of the new method is examined. Compared to a standard EnKF, the hybrid method is superior when an overly small/large cutoff distance is used, and it has less dependence on cutoff distance. The new scheme is also able to improve short-term weather forecasts, especially when an overly large cutoff distance is used. Sensitivity studies show that caution should be taken when the new scheme is applied to a dense observing system with an overly small cutoff distance in filtering. In addition, the new scheme has a nearly equivalent computational cost to the standard EnKF; thus, it is particularly suitable for GCM applications.
Given a biased coupled model and the atmospheric and oceanic observing system, how to maintain balanced and coherent climate estimation is of critical importance for producing accurate climate analysis and prediction initialization. However, due to limitation of the observing system (most of the oceanic measurements are only available for the upper ocean, for instance), directly evaluating climate estimation with real observations is difficult. With two coupled models which are biased with respect to each other, a “biased” twin experiment is designed to simulate the problem. To do that, the atmospheric and oceanic “observations” drawn from one model based on the modern climate observing system are assimilated into the other. The model that produces “observations” serves as the “truth” and the degree by which an assimilation recovers the “truth” steadily and coherently is an assessment of the impact of the data constraint scheme on climate estimation. Given the assimilation model bias of warmer atmosphere and colder ocean, while the atmospheric-only (oceanic-only) data constraint produces an over-cooling (over-warming) ocean through the atmosphere-ocean interaction, the constraints with both atmospheric and oceanic data create a balanced and coherent ocean estimate as the observational model. Moreover, the consistent atmosphere-ocean constraint produces the most accurate estimate for North Atlantic Deep Water (NADW), while NADW is too strong (weak) as the system is only constrained by atmospheric (oceanic) data. These twin experiment results provide insights that consistent data constraints of multiple components are very important when a coupled model is combined with the climate observing system for climate estimation and prediction initialization.
When observations are assimilated into a high-resolution coupled model, a traditional scheme that preferably projects observations to correct large scale background tends to filter out small scale cyclones. Here we separately process the large scale background and small scale perturbations with low-resolution observations for reconstructing historical cyclone statistics in a cyclone-permitting model. We show that by maintaining the interactions between small scale perturbations and successively-corrected large scale background, a model can successfully retrieve the observed cyclone statistics that in return improve estimated ocean states. The improved ocean initial conditions together with the continuous interactions of cyclones and background flows are expected to reduce model forecast errors. Combined with convection-permitting cyclone initialization, the new high-resolution model initialization along with the progressively-advanced coupled models should contribute significantly to the ongoing research on seamless weather-climate predictions.
Barsugli, Joseph J., and Keith W Dixon, et al., November 2013: The Practitioner’s Dilemma: How to Assess the Credibility of Downscaled Climate Projections. EOS, 94(46), doi:10.1002/2013EO460005. [ Abstract ]
Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections downscaled from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing downscaling methods.
Berg, Alexis, Kirsten L Findell, Benjamin R Lintner, Pierre Gentine, and Christopher Kerr, June 2013: Precipitation sensitivity to surface heat fluxes over North America in reanalysis and model data. Journal of Hydrometeorology, 14(3), doi:10.1175/JHM-D-12-0111.1. [ Abstract ]
A new methodology for assessing the impact of surface heat fluxes on precipitation is applied to data from the North American Regional Reanalysis (NARR) and to output from the Geophysical Fluid Dynamics Laboratory’s model AM2.1. The method assesses the sensitivity of afternoon convective rainfall frequency and intensity to the late-morning partitioning of latent and sensible heating, quantified in terms of evaporative fraction (EF). Over North America, both NARR and AM2.1 indicate sensitivity of convective rainfall triggering to EF but no appreciable influence of EF on convective rainfall amounts. Functional relationships between the triggering feedback strength (TFS) metric and mean EF demonstrate the occurrence of stronger coupling for mean EF in the range of 0.6 to 0.8. To leading order, AM2.1 exhibits spatial distributions and seasonality of the EF impact on triggering resembling those seen in NARR: rainfall probability increases with higher EF over the Eastern US and Mexico and peaks in Northern Hemisphere summer. Over those regions, the impact of EF variability on afternoon rainfall triggering in summer can explain up to 50% of seasonal rainfall variability. However, the AM2.1 metrics also exhibit some features not present in NARR, e.g., strong coupling extending northwest from the central Great Plains into Canada. Sources of disagreement may include model hydroclimatic biases that affect the mean patterns and variability of surface flux partitioning, with EF variability typically much lower in NARR. Finally, we also discuss the consistency of our results with other assessments of land-precipitation coupling obtained from different methodologies.
Capotondi, Antonietta, and Andrew T Wittenberg, July 2013: ENSO diversity in climate models. U.S. CLIVAR Variations, 11(2), 10-14.
The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.
The El Niño/Southern Oscillation (ENSO) exhibits well-known asymmetries: (1) warm events are stronger than cold events; (2) strong warm events are more likely to be followed by cold events than vice versa; and (3) cold events are more persistent than warm events. Coupled GCM simulations, however, continue to underestimate many of these observed features.
To shed light on these asymmetries, we begin with a widely-used delayed-oscillator conceptual model for ENSO, and modify it so that wind stress anomalies depend more strongly on SSTAs during warm conditions - as is observed. We then explore the impact of this nonlinearity on three dynamical regimes for ENSO: self-sustained oscillations, stochastically driven oscillations, and self-sustained oscillations interrupted by stochastic forcings. In all three regimes, the nonlinear air-sea coupling preferentially strengthens the feedbacks (both positive and delayed negative) during the ENSO warm phase – producing El Niños that grow to larger amplitude and overshoot more rapidly and consistently into the opposite phase, than do the La Niñas. Finally, we apply the modified oscillator to observational records, and to control simulations from two global coupled ocean-atmosphere-land-ice models (GFDL-CM2.1 and GFDL-CM2.5), to elucidate the causes of their differing asymmetries.
We examine the change in tropical cyclone (TC) tracks that result from projected changes in the large-scale steering flow and genesis location due to increasing greenhouse gases. Tracks are first simulated using a Beta and Advection Model (BAM) and NCEP-NCAR Reanalysis winds for all TCs that formed in the North Atlantic main development region (MDR) for the period 1950-2010. Changes in genesis location and large-scale steering flow are then estimated from an ensemble mean of 17 CMIP3 models for the A1b emissions scenario. The BAM simulations are then repeated with these changes to estimate how the TC tracks would respond to increased greenhouse gases. As the climate warms, the models project a weakening of the subtropical easterlies as well as an eastward shift in genesis location. This results in a statistically significant decrease in straight-moving (westward) storm tracks of 5.5% and an increase in recurving (open ocean) tracks of 5.5%. These track changes decrease TC counts over the Southern Gulf of Mexico and Caribbean by 1-1.5 per decade and increase TC counts over the central Atlantic by 1-1.5 per decade. Changes in the large-scale steering flow account for a vast majority of the projected changes in TC trajectories.
DiNezio, P, Gabriel A Vecchi, and A C Clement, June 2013: Detectability of Changes in the Walker Circulation in Response to Global Warming. Journal of Climate, 26(12), doi:10.1175/JCLI-D-12-00531.1. [ Abstract ]
Changes in the gradients in sea level pressure (SLP) and sea surface temperature (SST) along the equatorial Pacific are analyzed in observations and 101 numerical experiments performed with 37 climate models participating the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). The ensemble of numerical experiments simulates changes in the Earth's climate during the 1870-2004 period in response to changes in natural (solar variations, volcanoes) and anthropogenic (well-mixed greenhouse gases, ozone, direct aerosol forcing, and land use) radiative forcings. A reduction in the zonal SLP gradient is present in observational records, and is the typical response of the ensemble; yet only 26 out of the 101 "all forcing" historical experiments are able to simulate a reduced SLP gradient within 95% statistical confidence of the observed value. The multi-model response indicates a reduction of the Walker circulation to 'historical' forcings an order of magnitude smaller than the observed value. There are multiple, non-exclusive interpretations of these results: i) the observed trend may not be entirely forced, and includes a substantial component from internal variability; ii) there are problems with the observational record that lead to a spuriously large trend; iii) the strength of the Walker circulation, as measured by the zonal SLP gradient, may be less sensitive to external forcing in models than in the real climate system. Analysis of a subset of experiments suggests that greenhouse gases act to weaken the circulation, but aerosol forcing drives a strengthening of the circulation, which appears to be overestimated by the models, resulting in a muted response to the combined anthropogenic forcings.
Response of climate conditions in the Atlantic Hurricane Main Development Region (MDR) to doubling of atmospheric CO2 has been explored, using the new high-resolution coupled Climate Model version 2.5 developed at the Geophysical Fluid Dynamics Laboratory (GFDL-CM2.5). In the annual mean, the SST in the MDR warms by about 2°C in the CO2 doubling run relative to the Control run, the trade winds become weaker in the northern tropical Atlantic, and the rainfall increases over the ITCZ and its northern region. The amplitude of the annual cycle of the SST over the MDR is not significantly changed by CO2 doubling. However, we find that the interannual variations show significant responses to CO2 doubling: the seasonal maximum peak of the interannual variations of the SST over the MDR is about 25% stronger than in the Control run. The enhancement of the interannual variations of the SST in the MDR is due to changes in effectiveness of the Wind-Evaporation-SST (WES) positive feedback: WES remains a positive feedback until boreal early summer in the CO2 doubling run. The enhancement of the interannual variability of the SST over the MDR in boreal early summer due to CO2 doubling could lead to serious damages associated with the Atlantic Hurricane count and drought (or flood) in the Sahel and South America in a future climate.
We describe carbon system formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate good climate fidelity as described in Part I while incorporating explicit and consistent carbon dynamics. The two models differ almost exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. On land, both ESMs include a revised land model to simulate competitive vegetation distributions and functioning, including carbon cycling among vegetation, soil and atmosphere. In the ocean, both models include new biogeochemical algorithms including phytoplankton functional group dynamics with flexible stoichiometry. Preindustrial simulations are spun up to give stable, realistic carbon cycle means and variability. Significant differences in simulation characteristics of these two models are described. Due to differences in oceanic ventilation rates (Part I) ESM2M has a stronger biological carbon pump but weaker northward implied atmospheric CO2 transport than ESM2G. The major advantages of ESM2G over ESM2M are: improved representation of surface chlorophyll in the Atlantic and Indian Oceans and thermocline nutrients and oxygen in the North Pacific. Improved tree mortality parameters in ESM2G produced more realistic carbon accumulation in vegetation pools. The major advantages of ESM2M over ESM2G are reduced nutrient and oxygen biases in the Southern and Tropical Oceans.
Emile-Geay, Julien, K M Cobb, M E Mann, and Andrew T Wittenberg, April 2013: Estimating Central Equatorial Pacific SST variability over the Past Millennium. Part 1: Methodology and Validation. Journal of Climate, 26(7), doi:10.1175/JCLI-D-11-00510.1. [ Abstract ]
Constraining the low-frequency (LF) behavior of general circulation models (GCMs) requires reliable observational estimates of LF variability. In this two-part paper we present multiproxy reconstructions of NINO3.4 sea-surface temperature over the last millennium, applying two techniques (composite plus scale (CPS) and hybrid RegEM TTLS) to a network of tropical, high-resolution proxy records. In this first part, we present the data and methodology before evaluating their predictive skill using frozen network analysis (FNA) and pseudoproxy experiments. FNA results suggest that about half of the NINO3.4 variance can be reconstructed back to 1000, but show little LF skill during certain intervals. More variance can be reconstructed in the interannual band where climate signals are strongest, but this band is affected by dating uncertainties (which are not formally addressed here). CPS reliably estimates interannual variability, while LF fluctuations are more faithfully reconstructed with RegEM, albeit with inevitable variance loss. The RegEM approach is also tested on representative pseudoproxy networks derived from two millennium-long integrations of a coupled GCM. The pseudoproxy study confirms that reconstruction skill is significant in both the interannual and LF bands, provided that sufficient variance is exhibited in the target NINO3.4 index. It also suggests that FNA severely underestimates LF skill, even when LF variability is strong, resulting in overly pessimistic performance assessments. The centennial-scale variance of the historical NINO3.4 index falls somewhere between the two model simulations, suggesting that our network and methodology would be able to capture the leading LF variations in NINO3.4 for much of the past millennium, with the caveats noted above.
Emile-Geay, Julien, K M Cobb, M E Mann, and Andrew T Wittenberg, April 2013: Estimating Central Equatorial Pacific SST variability over the Past Millennium. Part 2: Reconstructions and Implications. Journal of Climate, 26(7), doi:10.1175/JCLI-D-11-00511.1. [ Abstract ]
Constraining the low-frequency (LF) behavior of general circulation models (GCMs) requires reliable observational estimates of LF variability. This two-part paper presents multiproxy reconstructions of Niño-3.4 sea surface temperature over the last millennium, applying two techniques [composite plus scale (CPS) and hybrid regularized expectation maximization (RegEM) truncated total least squares (TTLS)] to a network of tropical, high-resolution proxy records. This first part presents the data and methodology before evaluating their predictive skill using frozen network analysis (FNA) and pseudoproxy experiments. The FNA results suggest that about half of the Niño-3.4 variance can be reconstructed back to A.D. 1000, but they show little LF skill during certain intervals. More variance can be reconstructed in the interannual band where climate signals are strongest, but this band is affected by dating uncertainties (which are not formally addressed here). The CPS reliably estimates interannual variability, while LF fluctuations are more faithfully reconstructed with RegEM, albeit with inevitable variance loss. The RegEM approach is also tested on representative pseudoproxy networks derived from two millennium-long integrations of a coupled GCM. The pseudoproxy study confirms that reconstruction skill is significant in both the interannual and LF bands, provided that sufficient variance is exhibited in the target Niño-3.4 index. It also suggests that FNA severely underestimates LF skill, even when LF variability is strong, resulting in overly pessimistic performance assessments. The centennial-scale variance of the historical Niño-3.4 index falls somewhere between the two model simulations, suggesting that the network and methodology presented here would be able to capture the leading LF variations in Niño-3.4 for much of the past millennium, with the caveats noted above.
Gentine, Pierre, A K Betts, Benjamin R Lintner, and Kirsten L Findell, et al., June 2013: A probabilistic-bulk model of mixed layer and convection: 1) Clear-sky case. Journal of the Atmospheric Sciences, 70(6), doi:10.1175/JAS-D-12-0145.1. [ Abstract ]
A new bulk model of the convective boundary layer, the probabilistic bulk convection model (PBCM), is presented. Unlike prior bulk approaches that have modeled the mixed-layer-top buoyancy flux as a constant fraction of the surface buoyancy flux, PBCM implements a new mixed-layer-top entrainment closure based on the mass flux of updrafts overshooting the inversion. This mass flux is related to the variability of the surface state (potential temperature θ and specific humidity q) of an ensemble of updraft plumes. We evaluate the model against observed clear-sky weak and strong inversion cases and show that PBCM performs well. The height, state and timing of the boundary layer growth are accurately reproduced. Sensitivity studies are performed highlighting the role of the main parameters (surface variances, lateral entrainment). The model is weakly sensitive to the exact specification of the variability at the surface and is most sensitive to the lateral entrainment of environmental air into the rising plumes. Apart from allowing time-dependent top-of-the boundary-layer entrainment rates expressed in terms of surface properties, which can be observed in situ, PBCM naturally takes into account the transition to the shallow convection regime, as described in a companion paper. Thus, PBCM represents an important step towards a unified framework bridging parameterizations of mixed layer entrainment velocity in both clear-sky and moist convective boundary layers.
Gentine, Pierre, A K Betts, Benjamin R Lintner, and Kirsten L Findell, et al., June 2013: A probabilistic-bulk model of coupled mixed layer and convection: 2) Shallow convection case. Journal of the Atmospheric Sciences, 70(16), doi:10.1175/JAS-D-12-0146.1. [ Abstract ]
The probabilistic bulk convection model (PBCM) developed in a companion paper is here extended to shallow non-precipitating convection. The PBCM unifies the clear-sky and shallow convection boundary layer regimes, by obtaining mixed-layer growth, cloud fraction and convective inhibition from a single parameterization based on physical principles. The evolution of the shallow convection PBCM is based on the statistical distribution of the surface thermodynamic state of convective plumes.
The entrainment velocity of the mixed layer is related to the mass flux of the updrafts overshooting the dry inversion capping the mixed layer. The updrafts overcoming the convective inhibition generate active cloud base mass flux, which is the boundary condition for the shallow cumulus scheme. The subcloud layer entrainment velocity is directly coupled to the cloud base mass flux through the distribution of vertical velocity and fractional cover of the updrafts.
Comparisons of the PBCM against large-eddy simulations from the Barbados Oceanographic and Meteorological Experiment (BOMEX) and from the Southern Great Plains Atmospheric Radiation Measurement (ARM) facility demonstrate good agreement in terms of thermodynamic structure, cloud base mass flux and cloud top.
The equilibrium between the cloud base mass flux and rate of growth of the mixed layer determines the equilibrium convective inhibition and cloud cover. This process is an important new insight on the coupling between the mixed-layer and cumulus dynamics. Given its relative simplicity and transparency, the PBCM represents a powerful tool for developing process-based understanding and intuition about the physical processes involved in boundary layer-convection interactions, as well as a testbed for diagnosing and validating shallow convection parameterizations.
Goddard, L M., Rym Msadek, and Thomas L Delworth, et al., January 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dynamics, 40(1-2), doi:10.1007/s00382-012-1481-2. [ Abstract ]
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.
Han, G, X Wu, and Shaoqing Zhang, et al., December 2013: Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model. Journal of Climate, 26(24), doi:10.1175/JCLI-D-13-00236.1. [ Abstract ]
Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium.
This study assesses the ability of a newly developed high-resolution coupled model from the Geophysical Fluid Dynamics Laboratory to simulate the cold-season hydroclimate in the present climate, and examines its response to climate change forcing. Output is assessed from a 280-yr control simulation based on 1990 atmospheric composition and an idealized 140-yr future simulation where atmospheric CO2 increases at 1% yr−1 until doubling in year 70 and then remains constant.
When compared to a low-resolution model, the high-resolution model is found to better represent the geographic distribution of snow variables in the present climate. In response to idealized radiative forcing changes, both models produce similar global-scale responses where global-mean temperature and total precipitation increase while snowfall decreases. Zonally, snowfall tends to decrease in the low to mid latitudes and increase in the mid to high latitudes.
At the regional scale, the high and low-resolution models sometimes diverge in the sign of projected snowfall changes; the high-resolution model exhibits future increases in a few select high altitude regions, notably the northwestern Himalaya region and small regions in the Andes and southwestern Yukon. Despite such local signals, there is an almost universal reduction in snowfall as a percent of total precipitation in both models. Using a simple multivariate model, temperature is shown to drive these trends by decreasing snowfall almost everywhere while precipitation increases snowfall in the high altitudes and mid to high latitudes. Mountainous regions of snowfall increases in the high-resolution model exhibit a unique dominance of the positive contribution from precipitation over temperature.
Regional surface temperature trends from the CMIP3 and CMIP5 20th century runs are compared with observations -- at spatial scales ranging from global averages to individual grid points -- using simulated intrinsic climate variability from pre-industrial control runs to assess whether observed trends are detectable and/or consistent with the models’ historical run trends. The CMIP5 models are also used to detect anthropogenic components of the observed trends, by assessing alternative hypotheses based on scenarios driven with either anthropogenic plus natural forcings combined, or with natural forcings only. Modeled variability is assessed via inspection of control run time series, standard deviation maps, spectral analyses, and low-frequency variance consistency tests. The models are found to provide plausible representations of internal climate variability, though there is room for improvement. The influence of observational uncertainty on the trends is assessed, and found to be generally small compared to intrinsic climate variability.
Observed temperature trends over 1901-2010 are found to contain detectable anthropogenic warming components over a large fraction (about 80%) of the analyzed global area. In several regions, the observed warming is significantly underestimated by the models, including parts of the southern Ocean, south Atlantic, far eastern Atlantic, and far west Pacific. Regions without detectable warming signals include the high latitude North Atlantic, the eastern U.S., and parts of the eastern Pacific. For 1981-2010, the observed warming trends over about 45% of the globe are found to contain a detectable anthropogenic warming; this includes much of the globe within about 40-45 degrees of the equator, except for the eastern Pacific.
Twenty-first-century projections of Atlantic climate change are downscaled to explore the robustness of potential changes in hurricane activity. Multimodel ensembles using the phase 3 of the Coupled Model Intercomparison Project (CMIP3)/Special Report on Emissions Scenarios A1B (SRES A1B; late-twenty-first century) and phase 5 of the Coupled Model Intercomparison Project (CMIP5)/representative concentration pathway 4.5 (RCP4.5; early- and late-twenty-first century) scenarios are examined. Ten individual CMIP3 models are downscaled to assess the spread of results among the CMIP3 (but not the CMIP5) models. Downscaling simulations are compared for 18-km grid regional and 50-km grid global models. Storm cases from the regional model are further downscaled into the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model (9-km inner grid spacing, with ocean coupling) to simulate intense hurricanes at a finer resolution.
A significant reduction in tropical storm frequency is projected for the CMIP3 (−27%), CMIP5-early (−20%) and CMIP5-late (−23%) ensembles and for 5 of the 10 individual CMIP3 models. Lifetime maximum hurricane intensity increases significantly in the high-resolution experiments—by 4%–6% for CMIP3 and CMIP5 ensembles. A significant increase (+87%) in the frequency of very intense (categories 4 and 5) hurricanes (winds ≥ 59 m s−1) is projected using CMIP3, but smaller, only marginally significant increases are projected (+45% and +39%) for the CMIP5-early and CMIP5-late scenarios. Hurricane rainfall rates increase robustly for the CMIP3 and CMIP5 scenarios. For the late-twenty-first century, this increase amounts to +20% to +30% in the model hurricane’s inner core, with a smaller increase (~10%) for averaging radii of 200 km or larger. The fractional increase in precipitation at large radii (200–400 km) approximates that expected from environmental water vapor content scaling, while increases for the inner core exceed this level.
Knutson, Thomas R., Fanrong Zeng, and Andrew T Wittenberg, September 2013: The Extreme March-2012 Warm Anomaly over the Eastern United States: Global Context and Multimodel Trend Analysis [in “Explaining Extreme Events of 2012 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 94(9), S13-S17.
Kosaka, Yu, Shang-Ping Xie, Ngar-Cheung Lau, and Gabriel A Vecchi, May 2013: Origin of seasonal predictability for summer climate over the Northwestern Pacific. Proceedings of the National Academy of Sciences, 110(19), doi:10.1073/pnas.1215582110. [ Abstract ]
Summer climate in the Northwestern Pacific (NWP) displays large year-to-year variability, affecting densely populated Southeast and East Asia by impacting precipitation, temperature, and tropical cyclones. The Pacific–Japan (PJ) teleconnection pattern provides a crucial link of high predictability from the tropics to East Asia. Using coupled climate model experiments, we show that the PJ pattern is the atmospheric manifestation of an air–sea coupled mode spanning the Indo-NWP warm pool. The PJ pattern forces the Indian Ocean (IO) via a westward propagating atmospheric Rossby wave. In response, IO sea surface temperature feeds back and reinforces the PJ pattern via a tropospheric Kelvin wave. Ocean coupling increases both the amplitude and temporal persistence of the PJ pattern. Cross-correlation of ocean–atmospheric anomalies confirms the coupled nature of this PJIO mode. The ocean–atmosphere feedback explains why the last echoes of El Niño–Southern Oscillation are found in the IO-NWP in the form of the PJIO mode. We demonstrate that the PJIO mode is indeed highly predictable; a characteristic that can enable benefits to society.
Using simulations performed with 18 coupled atmosphere-ocean global climate models from the CMIP5 project, projections of Northern Hemisphere snowfall under the RCP4.5 scenario are analyzed for the period 2006-2100. These models perform well in simulating 20th century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the 21st century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the -10 °C isotherm of the late 20th century mean surface air temperature such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur – even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the 21st century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.
Kunkel, Kenneth E., and Thomas R Knutson, et al., April 2013: Monitoring and Understanding Trends in Extreme Storms: State of Knowledge. Bulletin of the American Meteorological Society, 94(4), doi:10.1175/BAMS-D-11-00262.1. [ Abstract ]
The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hailstorms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make using the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical link- ages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multidecadal trends in the areal percentage of the contiguous United States impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the United States as a whole since 1950.
The impact of climate warming on the upper layer of the Bering Sea is investigated by using a high-resolution coupled global climate model. The model is forced by increasing atmospheric CO2 at a rate of 1% per year until CO2 reaches double its initial value (after 70 years), after which it is held constant. In response to this forcing, the upper layer of the Bering Sea warms by about 2�C in the southeastern shelf and by a little more than 1�C in the western basin. The wintertime ventilation to the permanent thermocline weakens in the western Bering Sea. After CO2 doubling, the southeastern shelf of the Bering Sea becomes almost ice-free in March, and the stratification of the upper layer strengthens in May and June. Changes of physical condition due to the climate warming would impact the pre-condition of spring bio-productivity in the southeastern shelf.
Leech, P J., J Lynch-Stieglitz, and Rong Zhang, February 2013: Western Pacific Thermocline Structure and the Pacific Marine Intertropical Convergence Zone during the Last Glacial Maximum. Earth and Planetary Science Letters, 363, doi:10.1016/j.epsl.2012.12.026. [ Abstract ]
Paleoclimate proxy evidence suggests a southward shift of the Intertropical Convergence Zone (ITCZ) during times of Northern Hemisphere cooling, including the Last Glacial Maximum, 19–23 ka before present. However, evidence for movement over the Pacific has mainly been limited to precipitation reconstructions near the continents, and the position of the Pacific marine ITCZ is less well constrained. In this study, we address this problem by taking advantage of the fact that the upper ocean density structure reflects the overlying wind field. We reconstruct changes in the upper ocean density structure during the LGM using oxygen isotope measurements on the planktonic foraminifera G. ruber and G. tumida in a transect of sediment cores from the Western Tropical Pacific. The data suggests a ridge in the thermocline just north of the present-day ITCZ persists for at least part of the LGM, and a structure in the Southern Hemisphere that differs from today. The reconstructed structure is consistent with that produced in a General Circulation Model with both a Northern and Southern Hemisphere ITCZ.
Lintner, Benjamin R., Pierre Gentine, and Kirsten L Findell, et al., April 2013: An idealized prototype for large-scale land-atmosphere coupling. Journal of Climate, 26(7), doi:10.1175/JCLI-D-11-00561.1. [ Abstract ]
A process-based, semi-analytic prototype model for understanding large-scale land-atmosphere coupling is developed here. The metric for quantifying the coupling is the sensitivity of precipitation (P) to soil moisture (W), defined as . For a range of prototype parameters typical of conditions found over tropical or summertime continents, the sensitivity measure exhibits a broad minimum at intermediate soil moisture values. This minimum is attributed to a tradeoff between evaporation (or evapotranspiration) E and large-scale moisture convergence across the range of soil moisture states. For low soil moisture, water-limited conditions, is dominated by evaporative sensitivity , reflecting high potential evaporation (Ep) arising from relatively warm surface conditions and a moisture-deficient atmospheric column under dry surface conditions. By contrast, under high soil moisture (or energy-limited) conditions, becomes slightly negative as Ep decreases. However, because convergence and precipitation increase strongly with decreasing (drying) moisture advection, while soil moisture slowly saturates, is large. Variation of key parameters is shown to impact the magnitude of , e.g., increasing the timescale for deep convective adjustment lowers at a given W, especially on the moist side of the profile where convergence dominates. While the prototype applicability’s for direct quantitative comparison to either observations or models is clearly limited, it nonetheless demonstrates how the complex interplay of surface turbulent and column radiative fluxes, deep convection, and horizontal and vertical moisture transport influences the coupling of the land surface and atmosphere that may be expected to occur in either more realistic models or observations.
Liu, Zhengyu, S Wu, Shaoqing Zhang, Y Liu, and X Rong, September 2013: Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction. Advances in Atmospheric Sciences, 30(5), doi:10.1007/s00376-013-2268-z. [ Abstract ]
A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.
McGregor, Shayne, Axel Timmermann, Matthew H England, O Timm, and Andrew T Wittenberg, October 2013: Inferred changes in El Nino-Southern Oscillation variance over the past six centuries. Climate of the Past, 9(5), doi:10.5194/cp-9-2269-2013. [ Abstract ]
It is vital to understand how the El Niño–Southern Oscillation (ENSO) has responded to past changes in natural and anthropogenic forcings, in order to better understand and predict its response to future greenhouse warming. To date, however, the instrumental record is too brief to fully characterize natural ENSO variability, while large discrepancies exist amongst paleo-proxy reconstructions of ENSO. These paleo-proxy reconstructions have typically attempted to reconstruct ENSO's temporal evolution, rather than the variance of these temporal changes. Here a new approach is developed that synthesizes the variance changes from various proxy data sets to provide a unified and updated estimate of past ENSO variance. The method is tested using surrogate data from two coupled general circulation model (CGCM) simulations. It is shown that in the presence of dating uncertainties, synthesizing variance information provides a more robust estimate of ENSO variance than synthesizing the raw data and then identifying its running variance. We also examine whether good temporal correspondence between proxy data and instrumental ENSO records implies a good representation of ENSO variance. In the climate modeling framework we show that a significant improvement in reconstructing ENSO variance changes is found when combining information from diverse ENSO-teleconnected source regions, rather than by relying on a single well-correlated location. This suggests that ENSO variance estimates derived from a single site should be viewed with caution. Finally, synthesizing existing ENSO reconstructions to arrive at a better estimate of past ENSO variance changes, we find robust evidence that the ENSO variance for any 30 yr period during the interval 1590–1880 was considerably lower than that observed during 1979–2009.
Msadek, Rym, W E Johns, Stephen G Yeager, Gokhan Danabasoglu, Thomas L Delworth, and Anthony Rosati, June 2013: The Atlantic Meridional Heat transport at 26.5° N and its relationship with the MOC in the RAPID array and the GFDL and NCAR coupled models. Journal of Climate, 26(12), doi:10.1175/JCLI-D-12-00081.1. [ Abstract ]
The link at 26.5° N between the Atlantic meridional heat transport (MHT) and the Atlantic meridional overturning circulation (MOC) is investigated in two climate models, GFDL CM2.1 and NCAR CCSM4, and compared with the recent observational estimates from the RAPID-MOCHA array. Despite a stronger than observed MOC magnitude, both models underestimate the mean MHT at 26.5° N due to an overly diffuse thermocline. Biases result from errors in both overturning and gyre components of the MHT. The observed linear relationship between MHT and MOC at 26.5° N is realistically simulated by the two models and is mainly due to the overturning component of the MHT. Fluctuations in overturning MHT are dominated by Ekman transport variability in CM2.1 and CCSM4, whereas baroclinic geostrophic transport variability plays a larger role in RAPID. CCSM4 which has a parameterization of Nordic Sea overflows and thus a more realistic North Atlantic Deep Water (NADW) penetration shows smaller biases in the overturning heat transport than CM2.1 due to deeper NADW at colder temperatures. The horizontal gyre heat transport and its sensitivity to the MOC are poorly represented in both models. The wind-driven gyre heat transport is northward in observations at 26.5° N whereas it is weakly southward in both models, reducing the total MHT. We emphasize model biases that are responsible for the too weak MHT, particularly at the western boundary. The use of direct MHT observations through RAPID allows us to identify the source of the too weak MHT in the two models, a bias shared by a number of CMIP5 coupled models.
Ogata, Tomomichi, Shang-Ping Xie, Andrew T Wittenberg, and D-Z Sun, September 2013: Interdecadal Amplitude Modulation of El Nino/Southern Oscillation and its Impacts on Tropical Pacific Decadal Variability. Journal of Climate, 26(18), doi:10.1175/JCLI-D-12-00415.1. [ Abstract ]
The amplitude of El Nino/Southern Oscillation (ENSO) displays pronounced interdecadal modulations in observations. The mechanisms for the amplitude modulation are investigated using a 2000-year pre-industrial control integration from the Geophysical Fluid Dynamics Laboratory Climate Model 2.1 (CM2.1). ENSO amplitude modulation is highly correlated with the second empirical orthogonal function (EOF) mode of tropical Pacific decadal variability (TPDV), which features equatorial zonal dipoles in sea surface temperature (SST) and subsurface temperature along the thermocline. Experiments with an ocean general circulation model indicate that both interannual and decadal-scale wind variability are required to generate decadal-scale tropical Pacific temperature anomalies at the sea surface and along the thermocline. Even a purely interannual and sinusoidal wind forcing can produce substantial decadal-scale effects in the equatorial Pacific, with SST cooling in the west, subsurface warming along the thermocline, and enhanced upper-ocean stratification in the east. A mechanism is proposed by which ENSO’s residual effects could serve to alter subsequent ENSO stability, possibly contributing to long-lasting epochs of extreme ENSO behavior via a coupled feedback with TPDV.
Climate models simulate a wide range of climate changes at high northern latitudes in response to increased CO2. They also have substantial disagreement on projected changes of the Atlantic meridional overturning circulation (AMOC). Here we use two pairs of closely related climate models - each containing members with large and small AMOC declines - to explore the influence of AMOC decline on the high latitude response to increased CO2. The models with larger AMOC decline have less high latitude warming and sea ice decline than their small AMOC decline counterpart. By examining differences in the perturbation heat budget of the 40�90�N region, it is shown that AMOC decline diminishes the warming by weakening poleward ocean heat transport and increasing the ocean heat uptake. The cooling impact of this AMOC forced surface heat flux perturbation difference is enhanced by shortwave feedback and diminished by longwave feedback and atmospheric heat transport differences. The magnitude of the AMOC decline within model pairs is positively related to the magnitudes of control climate AMOC and Labrador Sea convection. Because the 40degree 90degree N region accounts for up to 40% of the simulated global ocean heat uptake over one hundred years, the process described here influences the global heat uptake efficiency.
Seneviratne, Sonia I., Alexis Berg, Kirsten L Findell, and Sergey Malyshev, et al., October 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophysical Research Letters, 40(19), doi:10.1002/grl.50956. [ Abstract ]
GLACE-CMIP5 is a multi-model experiment investigating the impact of soil moisture-climate feedbacks in CMIP5 projections. We present here first GLACE-CMIP5 results based on five Earth System Models, focusing on impacts of projected changes in regional soil moisture dryness (mostly increases) on late 21st-century climate. Projected soil moisture changes substantially impact climate in several regions in both boreal and austral summer. Strong and consistent effects are found on temperature, especially for extremes (about 1–1.5 K for mean temperature and 2–2.5 K for extreme daytime temperature). In the Northern Hemisphere, effects on mean and heavy precipitation are also found in most models, but the results are less consistent than for temperature. A direct scaling between soil moisture-induced changes in evaporative cooling and resulting changes in temperature mean and extremes is found in the simulations. In the Mediterranean region, the projected soil moisture changes affect about 25% of the projected changes in extreme temperature.
We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.
Su, Hua, R Dickinson, Kirsten L Findell, and Benjamin R Lintner, June 2013: How are spring snow conditions in central Canada related to early warm season precipitation?Journal of Hydrometeorology, 14(3), doi:10.1175/JHM-D-12-029.1. [ Abstract ]
The response of the warm season atmosphere to antecedent snow anomalies has long been an area of study. This paper explores how the spring snow depth relates to subsequent precipitation in central Canada using ground observations, reanalysis datasets and offline land surface model estimates. After removal of low-frequency ocean influences, April snow depth is found to correlate negatively with early warm season (May-June) precipitation across a large portion of the study area. A chain of mechanisms is hypothesized to account for this observed negative relation: (1) a snow depth anomaly leads to a soil moisture anomaly; (2) the subsequent soil moisture anomaly affects ground turbulent fluxes; and (3) the atmospheric vertical structure allows dry soil to promote local convection. A detailed analysis supports this chain of mechanisms for those portions of the domain manifesting statistically significant negative snow-precipitation correlation. For a portion of the study area, large-scale atmospheric circulation patterns also affect the early warm season rainfall, indicating that the snow-precipitation feedback may depend on large-scale atmospheric dynamical features. This analysis suggests that spring snow conditions can contribute to warm-season precipitation predictability on a sub-seasonal to seasonal scale, but that the strength of such predictability varies geographically as it depends on the interplay of hydro-climatological conditions across multiple spatial scales.
Impacts of tropical temperature changes in the upper troposphere (UT) and the tropical tropopause layer (TTL) on tropical cyclone (TC) activity are explored. UT and lower TTL cooling both lead to an overall increase in potential intensity (PI), while temperatures 70hPa and higher have negligible effect. Idealized experiments with a high-resolution global model show that lower temperatures in the UT are associated with increases in global and North Atlantic TC frequency, but modeled TC frequency changes are not significantly affected by TTL temperature changes nor do they scale directly with PI.
Future projections of hurricane activity have been made with models that simulate the recent upward Atlantic TC trends while assuming or simulating very different tropical temperature trends. Recent Atlantic TC trends have been simulated by: i) high-resolution global models with nearly moist-adiabatic warming profiles, and ii) regional TC downscaling systems that impose the very strong UT and TTL trends of the NCEP Reanalysis, an outlier among observational estimates. Impact of these differences in temperature trends on TC activity is comparable to observed TC changes, affecting assessments of the connection between hurricanes and climate. Therefore, understanding the character of and mechanisms behind changes in UT and TTL temperature is important to understanding past and projecting future TC activity changes. We conclude that the UT and TTL temperature trends in NCEP are unlikely to be accurate, and likely drive spuriously positive TC and PI trends, and an inflated connection between absolute surface temperature warming and TC activity increases.
Retrospective predictions of multi-year North Atlantic hurricane frequency are explored, by applying a hybrid statistical-dynamical forecast system to initialized and non-initialized multi-year forecasts of tropical Atlantic and tropical mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of five-year mean and nine-year mean tropical Atlantic hurricane frequency show significant correlation relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts hurricane frequency over 1961-2011 yielding correlation coefficients that approach 0.9.
These encouraging retrospective multi-year hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits our confidence in distinguishing between the skill due to external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is due to improved representation of the multi-year tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994-1995 remains a critical issue for the success of multi-year forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.
Villarini, Gabriele, James A Smith, and Gabriel A Vecchi, January 2013: Changing Frequency of Heavy Rainfall Over the Central United States. Journal of Climate, 26(1), doi:10.1175/JCLI-D-12-00043.1. [ Abstract ]
Records of daily rainfall accumulations from 447 rain gage stations over the central United States (Minnesota, Wisconsin, Michigan, Iowa, Illinois, Indiana, Missouri, Kentucky, Tennessee, Arkansas, Louisiana, Alabama, and Mississippi) are used to assess past changes in the frequency of heavy rainfall. Each station has a record of at least 50 years, and the data cover most of the twentieth century and the first decade of the twenty-first century. Analyses are performed using a peaks-over-threshold approach, and, for each station, the 95th percentile is used as threshold. Because of the count nature of the data and to account for both abrupt and slowly varying changes in the heavy rainfall distribution, we use segmented regression to detect change-points at unknown points in time. The presence of trends is assessed by means of a Poisson regression model, in which we examine whether the rate of occurrence parameter is a linear function of time (by means of a logarithmic link function). Our results point to increasing trends in heavy rainfall over the northern part of our domain. Examination of the surface temperature record suggests that these increasing trends occur over the area with largest increasing trends in temperature and, consequently, with an increase in atmospheric water vapor.
Villarini, Gabriele, and Gabriel A Vecchi, May 2013: Projected Increases in North Atlantic Tropical Cyclone Intensity from CMIP5 Models. Journal of Climate, 26(10), doi:10.1175/JCLI-D-12-00441.1. [ Abstract ]
Tropical cyclones—particularly intense ones—are a hazard to life and property, so an assessment of the changes in North Atlantic tropical cyclone intensity has important socioeconomic implications. In this study, the authors focus on the seasonally integrated power dissipation index (PDI) as a metric to project changes in tropical cyclone intensity. Based on a recently developed statistical model, this study examines projections in North Atlantic PDI using output from 17 state-of-the-art global climate models and three radiative forcing scenarios. Overall, the authors find that North Atlantic PDI is projected to increase with respect to the 1986–2005 period across all scenarios. The difference between the PDI projections and those of the number of North Atlantic tropical cyclones, which are not projected to increase significantly, indicates an intensification of North Atlantic tropical cyclones in response to both greenhouse gas (GHG) increases and aerosol changes over the current century. At the end of the twenty-first century, the magnitude of these increases shows a positive dependence on projected GHG forcing. The projected intensification is significantly enhanced by non-GHG (primarily aerosol) forcing in the first half of the twenty-first century.
Villarini, Gabriele, and Gabriel A Vecchi, June 2013: Multiseason Lead Forecast of the North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE). Journal of Climate, 26(11), doi:10.1175/JCLI-D-12-00448.1. [ Abstract ]
By considering the intensity, duration, and frequency of tropical cyclones, the power dissipation index (PDI) and accumulated cyclone energy (ACE) are concise metrics routinely used to assess tropical storm activity. This study focuses on the development of a hybrid statistical–dynamical seasonal forecasting system for the North Atlantic Ocean’s PDI and ACE over the period 1982–2011. The statistical model uses only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational record, reflecting the role of both local and nonlocal effects on the genesis and development of tropical cyclones in the North Atlantic basin. SSTs are predicted using a 10-member ensemble of the Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), an experimental dynamical seasonal-to-interannual prediction system. To assess prediction skill, a set of retrospective predictions is initialized for each month from November to April, over the years 1981–2011. The skill assessment indicates that it is possible to make skillful predictions of ACE and PDI starting from November of the previous year: skillful predictions of the seasonally integrated North Atlantic tropical cyclone activity for the coming season could be made even while the current one is still under way. Probabilistic predictions for the 2012 North Atlantic tropical cyclone season are presented.
We examine the influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities by comparing three GFDL climate models used for the CMIP5. The base model ESM2M is closely related to GFDL's CMIP3 climate model CM2.1, and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. We compare the impact of this "ocean swap" with an "atmosphere swap" that produces the CM3 climate model by replacing the AM2 atmosphere with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multi-decadal response timescale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early 20th century and an associated cooling which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.
The influence of changing ocean currents on climate change is evaluated by comparing an earth system model’s response to increased CO2 with and without an ocean circulation response. Inhibiting the ocean circulation response, by specifying a seasonally-varying preindustrial climatology of currents, has a much larger influence on the heat storage pattern than on the carbon storage pattern. The heat storage pattern without circulation changes resembles carbon storage (either with or without circulation changes) more than it resembles the heat storage when currents are allowed to respond. This is shown to be due to the larger magnitude of the redistribution transport – the change in transport due to circulation anomalies acting on control climate gradients – for heat than for carbon. The net ocean heat and carbon uptake are slightly reduced when currents are allowed to respond. Hence, ocean circulation changes potentially act to warm the surface climate. However, the impact of the reduced carbon uptake on radiative forcing is estimated to be small while the redistribution heat transport shifts ocean heat uptake from low to high latitudes increasing its cooling power. Consequently, global surface warming is significantly reduced by circulation changes. Circulation changes also shift the pattern of warming from broad northern hemisphere amplification to a more structured pattern with reduced warming at subpolar latitudes in both hemispheres and enhanced warming near the equator.
Observational information has a strong geographic
dependence that may directly influence the quality of
parameter estimation in a coupled climate system. Using an
intermediate atmosphere-ocean-land coupled model, the
impact of geographic dependent observing system on
parameter estimation is explored within a ‘‘twin’’ experiment
framework. The ‘‘observations’’ produced by a ‘‘truth’’
model are assimilated into an assimilation model in which
the most sensitive model parameter has a different geographic
structure from the ‘‘truth’’, for retrieving the ‘‘truth’’
geographic structure of the parameter. To examine the
influence of data-sparse areas on parameter estimation, the
twin experiment is also performed with an observing system
in which the observations in some area are removed. Results
show that traditional single-valued parameter estimation
(SPE) attains a global mean of the ‘‘truth’’, while geographic
dependent parameter optimization (GPO) can retrieve the
‘‘truth’’ structure of the parameter and therefore significantly
improves estimated states and model predictability. This is
especially true when an observing system with data-void
areas is applied, where the error of state estimate is reduced
by 31 % and the corresponding forecast skill is doubled by
GPO compared with SPE.
The decadal predictability of sea surface temperature (SST) and 2m air temperature (T2m) in Geophysical Fluid Dynamics Laboratory (GFDL)'s decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL's Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model, allows identification of internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an inter-hemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bi-polar seesaw, with warm anomalies centered in Greenland, and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observations, indicates that the IMP of SST may be predictable up to 4 (10) year lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) years at 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments, in which radiative forcing is returned to 1961 values. These results point towards the possibility of meaningful decadal climate outlooks using dynamical coupled models, if they are appropriately initialized from a sustained climate observing system.
The non-Gaussian probability distribution of sea-ice concentration makes difficulties for directly assimilating sea-ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea-ice state, any direct assimilation adjustment on sea-ice states is easily overridden by model physics.A new approach implements sea-ice data assimilation in enthalpy space where a sea-ice model represents a nonlinear function that transforms a positive-definite space into the sea-ice concentration subspace.Results from observation-assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea-ice on the decadal variability of the climate system show that the new scheme efficiently assimilates “sea-ice observations” into the model – while improving “sea-ice” variability itself, it consistently improves the estimates of all “climate” components.The resulted coupled initialization that is physically consistent among all coupled components significantly improves decadal-scale predictability of the coupled model.
Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently Booth et al (2012) showed that the HadGEM2-ES climate model closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the 20th century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al (2012) concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anti-correlation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.
Zhang, Rong, and Thomas R Knutson, September 2013: The role of global climate change in the extreme low summer Arctic sea ice extent in 2012 [in “Explaining Extreme Events of 2012 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 94(9), S23-S26.
Zhao, Ming, Isaac M Held, and Gabriel A Vecchi, et al., September 2013: Robust direct effect of increasing atmospheric CO2 concentration on global tropical cyclone frequency: a multi-model inter-comparison. U.S. CLIVAR Variations, 11(3), 17-23.
Zwiers, F, L V Alexander, Gabriele Hegerl, and Thomas R Knutson, et al., 2013: Climate Extremes: Challenges in Estimating and Understanding Recent Changes in the Frequency and Intensity of Extreme Climate and Weather Events In Climate Science for Serving Society: Research, Modeling and Prediction Priorities, Springer, 339-389. [ Abstract ]
This paper focuses primarily on extremes in the historical instrumental period. We consider a range of phenomena, including temperature and precipitation extremes, tropical and extra-tropical storms, hydrological extremes, and transient extreme sea-level events. We also discuss the extent to which detection and attribution research has been able to link observed changes to external forcing of the climate system. Robust results are available that detect and often attribute changes in frequency and intensity of temperature extremes to external forcing. There is also some evidence that on a global scale, precipitation extremes have intensified due to forcing. However, robustly detecting and attributing forced changes in other important extremes, such as tropical and extratropical storms or drought remains challenging.
In our review we find that there are multiple challenges that constrain advances in research on extremes. These include the state of the historical observational record, limitations in the statistical and other tools that are used for analyzing observed changes in extremes, limitations in the understanding of the processes that are involved in the production of extreme events, and in the ability to describe the natural variability of extremes with models and other tools.
Despite these challenges, it is clear that enormous progress is being made in the quest to improve the understanding of extreme events, and ultimately, to produce predictive products that will help society to manage the associated risks.
Brantstator, G, H Teng, Gerald A Meehl, J R Knight, M Latif, and Anthony Rosati, March 2012: Systematic estimates of initial-value decadal predictability for six AOGCMs. Journal of Climate, 25(6), doi:10.1175/JCLI-D-11-00227.1. [ Abstract ]
Initial-value predictability measures the degree to which the initial state can influence predictions. In this
paper, the initial-value predictability of six atmosphere–ocean general circulation models in the North Pacific
and North Atlantic is quantified and contrasted by analyzing long control integrations with time invariant
external conditions. Through the application of analog and multivariate linear regression methodologies,
average predictability properties are estimated for forecasts initiated from every state on the control trajectories.
For basinwide measures of predictability, the influence of the initial state tends to last for roughly
a decade in both basins, but this limit varies widely among the models, especially in the North Atlantic. Within
each basin, predictability varies regionally by as much as a factor of 10 for a given model, and the locations of
highest predictability are different for each model. Model-to-model variations in predictability are also seen
in the behavior of prominent intrinsic basin modes. Predictability is primarily determined by the mean of
forecast distributions rather than the spread about the mean. Horizontal propagation plays a large role in the
evolution of these signals and is therefore a key factor in differentiating the predictability of the variousmodels.
We present results for simulated climate and climate change from a newly developed high-resolution global climate model (GFDL CM2.5). The GFDL CM2.5 model has an atmospheric resolution of approximately 50 Km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 Km in the tropics to 8 Km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes.
We present analyses based on the output of a 280 year control simulation; we also present results based on a 140 year simulation in which atmospheric CO2 increases at 1% per year until doubling after 70 years.
Results are compared to the GFDL CM2.1 climate model, which has somewhat similar physics but coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5.
The response of CM2.5 to a doubling of atmospheric CO2 has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.
Delworth, Thomas L., and Fanrong Zeng, July 2012: Multicentennial variability of the Atlantic Meridional Overturning Circulation and its climatic influence in a 4000 year simulation of the GFDL CM2.1 climate model. Geophysical Research Letters, 39, L13702, doi:10.1029/2012GL052107. [ Abstract ]
We investigate decadal to multicentennial variability of Northern Hemisphere surface air temperature in a 4000-year control simulation of the GFDL CM2.1 climate model. Spectral analysis shows the presence of a distinct multicentennial timescle of temperature variability. The associated spatial pattern is broad, covering the entire Northern Hemisphere extratropics, but with enhanced amplitude in the Atlantic and Arctic sectors. This variability appears to be driven by interhemispheric fluctuations in oceanic heat transport associated with the Atlantic Meridional Overturning Circulation (AMOC). The AMOC variability is associated with century-scale propagation of salinity anomalies from the Southern Ocean to the subpolar North Atlantic, with out of phase transport variations between the upper ocean and deeper layers of the Atlantic. When positive (negative) upper ocean salinity anomalies reach the subpolar North Atlantic they strengthen (weaken) the AMOC by modulating upper ocean density and vertical stratification. The large-scale warming also appears to be enhanced by reductions in surface albedo associated with reduced sea-ice and low-level cloudiness, thereby increasing the absorption of shortwave radiation and amplifying the warming from AMOC changes. We speculate that such multicentennial variations in the AMOC could contribute to long-time scale climate fluctuations in the observed paleo record. This could arise purely as internal variability of the climate system, or through radiatively-induced changes to atmospheric circulation patterns, such as the NAO, that would in turn influence the AMOC.
Climate model experiments are analyzed to elucidate if and how the changes in mean climate in response to doubling of atmospheric CO2 (2xCO2) influence ENSO. The processes involved the development, transition, and decay of simulated ENSO events are quantified through a multi-model heat budget analysis. The simulated changes in ENSO amplitude in response to 2xCO2 are directly related to changes in the anomalous ocean heat flux convergence during the development, transition, and decay of ENSO events. This consistency relationship results from the Bjerknes feedback and cannot be used to attribute the changes in ENSO. In order to avoid a circular argument, we compute the anomalous heat flux convergence due to the interaction of the ENSO anomalies in the pre-industrial climate with the 2xCO2 changes in mean climate. The weakening of the Walker circulation and the increased thermal stratification, both robust features of the mean climate response to 2xCO2, play opposing roles in ENSO - mean climate interactions. Weaker upwelling in response to a weaker Walker circulation drives a reduction in thermocline-driven ocean heat flux convergence (i.e., thermocline feedback), and thus reduces the ENSO amplitude. Conversely, a stronger zonal subsurface temperature gradient, associated with the increased thermal stratification, drives an increase in zonal current-induced ocean heat flux convergence (i.e., zonal advection feedback), and thus increases the ENSO amplitude. These opposing processes explain the lack of model agreement in whether ENSO is going to weaken or strengthen in response to increasing greenhouse gases, but also why ENSO appears to be relatively insensitive to 2xCO2 in most models.
Using two fully coupled ocean-atmosphere models of CM2.1 (the Climate Model version 2.1 developed at the Geophysical Fluid Dynamics Laboratory) and CM2.5 (a new high-resolution climate model based on CM2.1), the characteristics and sources of SST and precipitation biases associated with the Atlantic ITCZ have been investigated.
CM2.5 has an improved simulation of the annual mean and the annual cycle of the rainfall over the Sahel and the northern South America, while CM2.1 shows excessive Sahel rainfall and lack of northern South America rainfall in boreal summer. This marked improvement in CM2.5 is due to not only high-resolved orography, but also a significant reduction of biases in the seasonal meridional migration of the ITCZ. In particular, the seasonal northward migration of the ITCZ in boreal summer is coupled to the seasonal variation of the SST and a subsurface doming of the thermocline in the northeastern tropical Atlantic, known as the Guinea Dome. Improvements in the ITCZ allow for better representation of the coupled processes that are important for an abrupt seasonally phase-locked decay of the interannual SST anomaly in the northern tropical Atlantic.
Nevertheless, the differences between CM2.5 and CM2.1 were not sufficient to reduce the warm SST biases in the eastern equatorial region and Angola-Benguela Area. The weak bias of southerly winds along the southwestern African coast associated with the excessive southward migration bias of the ITCZ may be a key to improve the warm SST biases there.
We describe the physical climate formulation and simulation characteristics of two new global coupled carbon-climate Earth System Models, ESM2M and ESM2G. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous CM2.1 climate model while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4.1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in the El Niño-Southern Oscillation being overly strong in ESM2M and overly weak ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to: total heat content variability given its lack of long term drift, gyre circulation and ventilation in the North Pacific, tropical Atlantic and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to: surface circulation given its superior surface temperature, salinity and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. Our overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords us the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon-climate models.
Gentine, Pierre, T J Troy, Benjamin R Lintner, and Kirsten L Findell, March 2012: Scaling in Surface Hydrology: Progress and Challenges. Journal of Contemporary Water Research & Education, 147(1), doi:10.1111/j.1936-704X.2012.03105.x. [ Abstract ]
This paper presents a review of the challenges in spatial and temporal scales in surface hydrology. Fundamental issues and gaps in our understanding of hydrologic scaling are highlighted and shown to limit predictive skill, with heterogeneities, nonlinearities, and non-local transport processes among the most significant difficulties faced in scaling. The discrepancy between the physical process scale and the measurement scale has played a major role in restricting the development of theories, for example, relating observational scales to scales of climate and weather models. Progress in our knowledge of scaling in hydrology requires systematic determination of critical scales and scale invariance of physical processes. In addition, viewing the surface hydrologic system as composed of interacting dynamical subsystems should facilitate the definition of scales observed in nature. Such an approach would inform the development of careful, resolution-dependent, physical law formulation based on mathematical techniques and physical laws.
Guilyardi, Eric, Wenju Cai, Matthew Collins, Alexey Fedorov, Fei-Fei Jin, Arun Kumar, D-Z Sun, and Andrew T Wittenberg, February 2012: New strategies for evaluating ENSO processes in climate models. Bulletin of the American Meteorological Society, 93(2), doi:10.1175/BAMS-D-11-00106.1. [ Abstract ]
50 ENSO experts, including 15 graduate students and early career postdocs met to discuss existing approaches to assess ENSO in coupled GCMs, review the recent progress, and propose recommendations for future research.
Guilyardi, Eric, H Bellenger, Matthew Collins, S Ferrett, Wenju Cai, and Andrew T Wittenberg, February 2012: A first look at ENSO in CMIP5. Clivar Exchanges, 17(1), 29-32.
Jiang, Xianan, D E Waliser, D Kim, Ming Zhao, Kenneth R Sperber, and William F Stern, et al., August 2012: Simulation of the intraseasonal variability over the Eastern Pacific ITCZ in climate models. Climate Dynamics, 39(3-4), doi:10.1007/s00382-011-1098-x. [ Abstract ]
During boreal summer, convective activity over the eastern Pacific (EPAC) inter-tropical convergence zone (ITCZ) exhibits vigorous intraseasonal variability (ISV). Previous observational studies identified two dominant ISV modes over the EPAC, i.e., a 40-day mode and a quasi-biweekly mode (QBM). The 40-day ISV mode is generally considered a local expression of the Madden-Julian Oscillation. However, in addition to the eastward propagation, northward propagation of the 40-day mode is also evident. The QBM mode bears a smaller spatial scale than the 40-day mode, and is largely characterized by northward propagation. While the ISV over the EPAC exerts significant influences on regional climate/weather systems, investigation of contemporary model capabilities in representing these ISV modes over the EPAC is limited. In this study, the model fidelity in representing these two dominant ISV modes over the EPAC is assessed by analyzing six atmospheric and three coupled general circulation models (GCMs), including one super-parameterized GCM (SPCAM) and one recently developed high-resolution GCM (GFDL HIRAM) with horizontal resolution of about 50 km. While it remains challenging for GCMs to faithfully represent these two ISV modes including their amplitude, evolution patterns, and periodicities, encouraging simulations are also noted. In general, SPCAM and HIRAM exhibit relatively superior skill in representing the two ISV modes over the EPAC. While the advantage of SPCAM is achieved through explicit representation of the cumulus process by the embedded 2-D cloud resolving models, the improved representation in HIRAM could be ascribed to the employment of a strongly entraining plume cumulus scheme, which inhibits the deep convection, and thus effectively enhances the stratiform rainfall. The sensitivity tests based on HIRAM also suggest that fine horizontal resolution could also be conducive to realistically capture the ISV over the EPAC, particularly for the QBM mode. Further analysis illustrates that the observed 40-day ISV mode over the EPAC is closely linked to the eastward propagating ISV signals from the Indian Ocean/Western Pacific, which is in agreement with the general impression that the 40-day ISV mode over the EPAC could be a local expression of the global Madden-Julian Oscillation (MJO). In contrast, the convective signals associated with the 40-day mode over the EPAC in most of the GCM simulations tend to originate between 150°E and 150°W, suggesting the 40-day ISV mode over the EPAC might be sustained without the forcing by the eastward propagating MJO. Further investigation is warranted towards improved understanding of the origin of the ISV over the EPAC.
Lee, Tsz-Cheung, and Thomas R Knutson, et al., May 2012: Impacts of climate change on tropical cyclones in the western North Pacific basin, Part I: Past observations. Tropical Cyclone Research and Review, 1(2), 213-230. [ Abstract ]
This paper reviews the current state of the science on the relationship
between climate change and historical tropical cyclone (TC) activity in
the western North Pacific (WNP) basin, which is the region of the
ESCAP/WMO Typhoon Committee members. Existing studies of observed
changes of TC activity in this basin, such as frequency, intensity,
precipitation, genesis location and track pattern are summarized.
Results from a survey on impacts of past TC activity on various members
of Typhoon Committee are reported, along with a review of studies of
past WNP landfalling TCs.
With considerable interannual and interdecadal variations in the TC
activity in this basin, it remains uncertain whether there has been any
detectable human influence on tropical cyclone frequency, intensity,
precipitation, track, or related aggregated storm activity metrics.
Also, the issues on of homogeneity and consistency of best track data
sets in the WNP further add uncertainty to relevant research studies.
Observations indicate some regional shifts in TC activity in the basin,
such as a decreasing trend in TC occurrence in part of the South China
Sea and an increasing trend along the east coast of China during the
past 40 years. This change is apparently related to local circulation
changes in the eastern Asia and WNP, though the cause of the
circulation changes remains unknown.
http://tcrr.typhoon.gov.cn/EN/10.6057/2012TCRR02.08
Lintner, Benjamin R., M Biasutti, N S Diffenbaugh, J E Lee, M J Niznik, and Kirsten L Findell, June 2012: Amplification of wet and dry month occurrence over tropical land regions in response to global warming. Journal of Geophysical Research: Atmospheres, 117, D11106, doi:10.1029/2012JD017499. [ Abstract ]
Quantifying how global warming impacts the spatiotemporal distribution of precipitation represents a key scientific challenge with profound implications for human systems. Utilizing monthly precipitation data from Coupled Model Intercomparison Project (CMIP3) climate change simulations, the results here show that the occurrence of very dry (<0.5 mm/day) and very wet (>10 mm/day) months comprises a straightforward, robust metric of anthropogenic warming on tropical land region rainfall. In particular, differencing tropicswide precipitation frequency histograms for 25-year periods over the late 21st and 20th centuries shows increased late-21st-century occurrence of both histogram extremes in the model ensemble and across individual models. Mechanistically, such differences are consistent with the view of enhanced tropical precipitation spatial gradients. Similar diagnostics are calculated for two 15-year subperiods over 1979-2008 for the CMIP3 models and three observational precipitation products to assess whether the signature of late-21st-century warming has already emerged in response to recent warming. While both the observations and CMIP3 ensemble-mean hint at similar amplification in the warmer (1994-2008) subinterval, the changes are not robust, as substantial differences are evident among the observational products and the intraensemble spread is large. Comparing histograms computed from the warmest and coolest years of the observational period further demonstrates effects of internal variability, notably the El Niño/Southern Oscillation, which appear to oppose the impact quasi-uniform anthropogenic warming on the wet tail of the monthly precipitation distribution. These results identify the increase of very dry and wet occurrences in monthly precipitation as a potential signature of anthropogenic global warming but also highlight the continuing dominance of internal climate variability on even bulk measures of tropical rainfall.
Richter, I, Shang-Ping Xie, Andrew T Wittenberg, and Y Matsumoto, March 2012: Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation. Climate Dynamics, 38(5-6), doi:10.1007/s00382-011-1038-9. [ Abstract ]
Most coupled general circulation models
(GCMs) perform poorly in the tropical Atlantic in terms of
climatological seasonal cycle and interannual variability.
The reasons for this poor performance are investigated in a
suite of sensitivity experiments with the Geophysical Fluid
Dynamics Laboratory (GFDL) coupled GCM. The experiments
show that a significant portion of the equatorial SST
biases in the model is due to weaker than observed equatorial
easterlies during boreal spring. Due to these weak
easterlies, the tilt of the equatorial thermocline is reduced,
with shoaling in the west and deepening in the east. The
erroneously deep thermocline in the east prevents cold
tongue formation in the following season despite vigorous
upwelling, thus inhibiting the Bjerknes feedback. It is
further shown that the surface wind errors are due, in part,
to deficient precipitation over equatorial South America
and excessive precipitation over equatorial Africa, which
already exist in the uncoupled atmospheric GCM. Additional
tests indicate that the precipitation biases are highly
sensitive to land surface conditions such as albedo and soil
moisture. This suggests that improving the representation
of land surface processes in GCMs offers a way of
improving their performance in the tropical Atlantic. The
weaker than observed equatorial easterlies also contribute
remotely, via equatorial and coastal Kelvin waves, to the
severe warm SST biases along the southwest African coast.
However, the strength of the subtropical anticyclo
Srokosz, M, and Thomas L Delworth, et al., November 2012: Past, present and future change in the Atlantic meridional overturning circulation. Bulletin of the American Meteorological Society, 93(11), doi:10.1175/BAMS-D-11-00151.1. [ Abstract ]
Observations and numerical modelling experiments provide evidence for links between variability in the Atlantic Meridional Overturning Circulation (AMOC) and global climate patterns. Reduction in the strength of the overturning circulation is thought to have played a key role in rapid climate change in the past and may have the potential to significantly influence climate change in the future, as noted in the last two IPCC assessment reports (2001, 2007). Both IPCC reports also highlighted the significant uncertainties that exist regarding the future behaviour of the AMOC under global warming. Model results suggest that changes in the AMOC can impact surface air temperature, precipitation patterns and sea level, particularly in areas bordering the North Atlantic, thus affecting human populations. Here current understanding of past, present and future change in the AMOC and the effects of such changes on climate are reviewed. The focus is on observations of the AMOC, how the AMOC influences climate and in what way the AMOC is likely to change over the next few decades and the 21st century. The potential for decadal prediction of the AMOC is also discussed. Finally, the outstanding challenges and possible future directions for AMOC research are outlined.
Matei et al. (Reports, 6 January 2012, p. 76) claim to show skillful multiyear predictions of the
Atlantic Meridional Overturning Circulation (AMOC). However, these claims are not justified,
primarily because the predictions of AMOC transport do not outperform simple reference forecasts
based on climatological annual cycles. Accordingly, there is no justification for the "confident"
prediction of a stable AMOC through 2014.
Villarini, Gabriele, and Gabriel A Vecchi, January 2012: North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE): Statistical modeling and sensitivity to sea surface temperature changes. Journal of Climate, 25(2), doi:10.1175/JCLI-D-11-00146.1. [ Abstract ]
This study focuses on the statistical modeling of the Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE) for the North Atlantic basin over the period 1949-2008, which are metrics routinely used to assess tropical storm activity, and their sensitivity to sea surface temperature (SST) changes. To describe the variability exhibited by the data, four different statistical distributions are considered (gamma, Gumbel, lognormal, and Weibull), and tropical Atlantic and tropical mean SSTs are used as predictors. Model selection, both in terms of significant covariates and their functional relation to the parameters of the statistical distribution, is performed using two penalty criteria. Two different SST data sets are considered (UK Met Offices HadISSTv1 and NOAAs Extended Reconstructed ERSSTv3b) to examine the sensitivity of the results to the input data.
The statistical models presented in this study are able to well describe the variability in the observations according to several goodness-of-fit diagnostics. Both tropical Atlantic and tropical mean SSTs are significant predictors, independently of the SST input data, penalty criterion, and tropical storm activity metric. The application of these models to centennial reconstructions and seasonal forecasting is illustrated.
The sensitivity of North Atlantic tropical cyclone frequency, duration, and intensity is examined for both uniform and non-uniform SST changes. Under uniform SST warming, these results indicate that there is a modest sensitivity of intensity, and a decrease in tropical storm and hurricane frequencies. On the other hand, increases of tropical Atlantic SST relative to the tropical mean SST suggest an increase in intensity and frequency of North Atlantic tropical storms and hurricanes.
Villarini, Gabriele, Gabriel A Vecchi, and James A Smith, January 2012: U.S. landfalling and North Atlantic hurricanes: Statistical modeling of their frequencies and ratios. Monthly Weather Review, 140(1), doi:10.1175/MWR-D-11-00063.1. [ Abstract ]
Time series of US landfalling and North Atlantic hurricane counts and their ratios over the period 1878–2008 are modeled using tropical Atlantic sea surface temperature (SST), tropical mean SST, North Atlantic Oscillation (NAO), and Southern Oscillation Index (SOI). Two SST input data are employed to examine the uncertainties in the reconstructed SST data on the modeling results. Due to the likely undercount of recorded hurricanes in the earliest part of the record, we consider both the uncorrected hurricane record (HURDAT), and a time series with a recently proposed undercount correction.
Modeling of the count data is performed using a conditional Poisson regression model, in which the rate of occurrence can depend linearly or nonlinearly on the climate indices. Model selection is performed following a stepwise approach and using two penalty criteria. These results do not allow identifying a single “best” model due to the different model configurations (different SST data, corrected versus uncorrected datasets, penalty criteria). Despite the lack of an objectively identified unique final model, we recommend a set of models in which the parameter of the Poisson distribution depends linearly on tropical Atlantic and tropical mean SSTs.
Modeling of the fractions of North Atlantic hurricanes making US landfall is performed using a binomial regression model. Similar to the count data, it is not possible to identify a single “best” model, but different model configurations are obtained depending on the SST data, undercount correction, and penalty criterion. These results suggest that these fractions are controlled by local (related to the NAO) and remote (SOI and tropical mean SST) effects.
Villarini, Gabriele, and Gabriel A Vecchi, August 2012: Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models. Nature Climate Change, 2(8), doi:10.1038/nclimate1530. [ Abstract ]
Assessing potential changes in North Atlantic (NA) tropical storm (TS) activity this century is of paramount societal and economic significance, and the topic of intense scientific research1. We explore projections of NA TS changes over the twenty-first century by applying a statistical downscaling methodology2, 3 to a suite of experiments with the latest state-of-the-art global coupled climate models4. We also apply a methodology5 to partition the dominant sources of uncertainty in the TS projections. We find that over the first half of the twenty-first century radiative forcing changes act to increase NA TS frequency; this increase arises from radiative forcings other than increasing CO2 (probably aerosols). However, NA TS trends over the entire twenty-first century are of ambiguous sign. We find that for NA TS frequency, in contrast to sea surface temperature (SST), the largest uncertainties are driven by the chaotic nature of the climate system and by the climate response to radiative forcing. These results highlight the need to better understand the processes controlling patterns of SST change in response to radiative forcing and internal climate variability to constrain estimates of future NA TS activity. Coordinated experiments isolating forcing agents in projections should improve our understanding, and would enable better assessment of future TS activity.
Watanabe, M, and Andrew T Wittenberg, July 2012: A Method for Disentangling El Niño-Mean State Interaction. Geophysical Research Letters, 39, L14702, doi:10.1029/2012GL052013. [ Abstract ]
The amplitude of the El Niño-Southern Oscillation (ENSO) is known to fluctuate in long records derived from observations and general circulation models (GCMs), even when driven by constant external forcings. This involves an interaction between the ENSO cycle and the background mean state, which affects the climatological precipitation over the eastern equatorial Pacific. The changes in climatological rainfall may be ascribed to several factors: changes in mean sea surface temperature (SST), changes in SST variability, and changes in the sensitivity of precipitation to SST. We propose a method to separate these effects in model ensembles. A case study with a single GCM demonstrates that the method works well, and suggests that each factor plays a role in changing mean precipitation. Applying the method to 16 pre-industrial control simulations archived in the Coupled Model Intercomparison Project phase 5 (CMIP5) reveals that the inter-model diversity in mean precipitation arises mostly from differences in the mean SST and atmospheric sensitivity to SST, rather than from differences in ENSO amplitude.
Watanabe, M, Jong-Seong Kug, Fei-Fei Jin, Matthew Collins, Masamichi Ohba, and Andrew T Wittenberg, October 2012: Uncertainty in the ENSO amplitude change from the past to the future. Geophysical Research Letters, 39, L20703, doi:10.1029/2012GL053305. [ Abstract ]
Due to errors in complex coupled feedbacks that compensate differently in different global climate models, as well as nonlinear nature of El Niño-Southern Oscillation (ENSO), there remain difficulties in detecting and evaluating the reason for the past and future changes in the ENSO amplitude, σnino. Here we use physics parameter ensembles, in which error compensation was eliminated by perturbing model parameters, to explore relationships between mean climate and variability. With four such ensembles we find a strong relationship between σnino and the mean precipitation over the eastern equatorial Pacific (Pnino). This involves a two-way interaction, in which the wetter mean state with greater Pnino acts to increase the ENSO amplitude by strengthening positive coupled feedbacks. Such a relationship is also identified in 11 single-model historical climate simulations in the Coupled Model Intercomparison Project phase 5 despite mean precipitation biases apparently masking the relationship in the multi-model ensemble (MME). Taking changes in σnino and Pnino between pre-industrial and recent periods eliminates the bias, and therefore results in a robust σnino-Pnino connection in MME, which suggests a 10-15% increase in the ENSO amplitude since pre-industrial era mainly due to changing mean state. However, the σnino-Pnino connection is less clear for their future changes, which are still greatly uncertain.
Williams, S J., and Thomas R Knutson, et al., 2012: Physical climate forces In Coastal Impacts, Adaptation, and Vulnerability: A Technical Input to the 2012 National Climate Assessment, Cooperative Report to the 2013 National Climate Assessment, Washington, DC, Island Press, 10-51.
Wu, X, Shaoqing Zhang, Zhengyu Liu, Anthony Rosati, Thomas L Delworth, and Y Liu, December 2012: Impact of Geographic Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model. Monthly Weather Review, 140(12), doi:10.1175/MWR-D-11-00298.1. [ Abstract ]
Due to the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. Four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root mean square errors as 41%, 23%, 62% and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature and land surface temperature respectively. Consistently, the new parameter optimization greatly improves the model predictability due to the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.
Xue, Y, and Anthony Rosati, et al., October 2012: A Comparative Analysis of Upper-Ocean Heat Content Variability from an Ensemble of Operational Ocean Reanalyses. Journal of Climate, 25(20), doi:10.1175/jcli-d-11-00542.1. [ Abstract ]
Ocean heat content (HC) is one of the key indicators of climate variability and also provides ocean memory critical for seasonal and decadal predictions. The availability of multiple operational ocean analyses (ORAs) now routinely produced around the world is an opportunity for estimation of uncertainties in HC analysis and development of ensemble-based operational HC climate indices. In this context, the spread across the ORAs is used to quantify uncertainties in HC analysis and the ensemble mean of ORAs to identify, and to monitor, climate signals. Toward this goal, this study analyzed 10 ORAs, two objective analyses based on in situ data only, and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability, and long-term trend of HC in the upper 300 m (HC300) from 1980 to 2009 are compared.
The spread across HC300 analyses generally decreased with time and reached a minimum in the early 2000s when the Argo data became available. There was a good correspondence between the increase of data counts and reduction of the spread. The agreement of HC300 anomalies among different ORAs, measured by the signal-to-noise ratio (S/N), is generally high in the tropical Pacific, tropical Indian Ocean, North Pacific, and North Atlantic but low in the tropical Atlantic and extratropical southern oceans where observations are very sparse. A set of climate indices was derived as HC300 anomalies averaged over the areas where the covariability between SST and HC300 represents the major climate modes such as ENSO, Indian Ocean dipole, Atlantic Niño, Pacific decadal oscillation, and Atlantic multidecadal oscillation.
Ying, M, and Thomas R Knutson, et al., May 2012: Impacts of Climate Change on Tropical Cyclones in the Western North Pacific Basin, Part II: Late Twenty-First Century Projections. Tropical Cyclone Research and Review, 1(2), doi:10.6057/2012TCRR02.09231-241. [ Abstract ]
This paper reviews the latest studies on the relationship between
projected late 21st century climate changes and tropical cyclone (TC)
activity in the western North Pacific (WNP) basin, which is the region
of the United Nations Economic and Social Commission for Asia and the
Pacific (ESCAP)/ World Meteorological Organization (WMO) Typhoon
Committee members. Existing studies of projected changes of TC
activity in this basin, such as frequency, intensity, precipitation,
genesis location and track pattern are summarized, based on an assumed
A1B future climate change scenario. A review of available studies on
projected future changes in WNP landfalling TC activity is also
included.
While it remains uncertain whether there has been any detectable human
influence on tropical cyclone frequency, intensity, precipitation,
track, or related aggregated storm activity metrics in the basin,
modeling studies suggest changes in future tropical cyclone activity
for the WNP basin. More models project decreases than increases in
tropical storm frequency (range −70% to +60%); most studies project an
increase in the TC intensity (range −3% to +18%); and all six available
studies that include the WNP basin project increases in TC
precipitation rates (range +5 to +30%).
http://tcrr.typhoon.gov.cn/EN/10.6057/2012TCRR02.09
Uncertainties in physical parameters of coupled models are an important source of model bias and adversely impact initialisation for climate prediction. Data assimilation using error covariances derived from model dynamics to extract observational information provides a promising approach to optimise parameter values so as to reduce such bias. However, effective parameter estimation in a coupled model is usually difficult because the error covariance between a parameter and the model state tends to be noisy due to multiple sources of model uncertainties. Using a simple coupled model consisting of the 3-variable Lorenz model and a slowly varying slab ‘ocean’, this study first investigated how to enhance the signal-to-noise ratio in covariances between model states and parameters, and then designed a data assimilation scheme for enhancive parameter correction (DAEPC). In DAEPC, parameter estimation is facilitated after state estimation reaches a ‘quasiequilibrium’ where the uncertainty of coupled model states is sufficiently constrained by observations so that the covariance between a parameter and the model state is signal dominant. The observation-updated parameters are applied to improving the next cycle of state estimation and the refined covariance of parameter and model state further improves parameter correction. Performing dynamically adaptive state and parameter estimations with speedy convergence, DAEPC provides a systematic way to estimate the whole array of coupled model parameters using observations, and produces more accurate state estimates. Forecast experiments show that the DAEPC initialisation with observation-estimated parameters greatly improves the model predictability - while valid ‘atmospheric’ forecasts are extended two times longer, the ‘oceanic’ predictability is almost tripled. The simple model results here provide some insights for improving climate estimation and prediction with a coupled general circulation model.
Chang, You-Soon, Anthony Rosati, and Shaoqing Zhang, February 2011: A construction of pseudo salinity profiles for the global ocean: Method and evaluation. Journal of Geophysical Research: Oceans, 116, C02002, doi:10.1029/2010JC006386. [ Abstract ]
This study demonstrates a reconstruction of salinity profiles for the global ocean
for the period 1993-2008. All available T-S profiles from the GTSPP and Argo data are
divided in two subsets; one half used for producing the vertical coupled T-S EOF modes
and the other for the verification. We employ a weighted least square method that
minimizes the misfits between the predetermined EOF structures and independent
observed temperature and altimetry data. Verification shows that the South Indian and
North Atlantic Oceans maintain good correlations to 900 m depth between the observed
and reconstructed salinity with altimetry data. Meanwhile, the Pacific and Antarctic
Oceans below 500 m show significant negative correlations, which is associated with the
relationship between steric height and salinity variability in these basins. In order to
guarantee general agreements with observations for all ocean depths, we calculate a
regional correlation index considering the impact of altimetry data and employ it for our
final products. Except for the surface ocean, the pseudo salinity profiles show general
improvements compared to the existing climatology and the reanalysis outputs from the
GFDL’s ensemble coupled data assimilation system. Near the surface layer, reanalysis
outputs show a relatively high performance due to the coupling between the atmosphere
and ocean. Assimilation system produces reliable surface flux variability not accounted
for the construction of the global pseudo salinity profiles. These results encourage the
application of the global pseudo salinity profiles into an assimilation system for the 20th
century when the observed salinity data are sparse.
Chang, You-Soon, Shaoqing Zhang, and Anthony Rosati, July 2011: Improvement of salinity representation in an ensemble coupled data assimilation system using pseudo salinity profiles. Geophysical Research Letters, 38, L13609, doi:10.1029/2011GL048064. [ Abstract ]
The scarcity of salinity observations prior to the Argo period makes it tremendously
difficult to estimate ocean states. By using the so-called pseudo salinity profiles constructed from
temperature and altimetry information, here we show the improvement of salinity representation
estimated by the ensemble coupled data assimilation system of the Geophysical Fluid Dynamics
Laboratory. The comparisons with climatology and independent observations show that the
pseudo salinity data considerably improve the assimilation skill for the pre-Argo period (1993-
2001). For the Argo period (2002-2007), there is little degradation of the assimilation skill using
pseudo salinity instead of Argo observations. This result ensures the robustness of the new
assimilation fields with pseudo salinity for the pre-Argo period when salinity observations are
sparse. We also suggest that the interannual variability of the existing reanalysis products could
suffer from erroneously-estimated discontinuities due to the non-stationary nature of the salinity
observing system.
Chen, C-K, C Wang, K-L Ma, and Andrew T Wittenberg, March 2011: Static correlation visualization for large time-varying volume data. Pacific Visualization Symposium (PacificVis), 2011 IEEE, doi:10.1109/PACIFICVIS.2011.5742369. [ Abstract ]
Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant.
The response of the Walker circulation to Last Glacial Maximum (LGM) forcing
is analyzed using an ensemble of six coordinated coupled climate model experiments.
The tropical atmospheric overturning circulation strengthens in all models in a manner
that is dictated by the response of the hydrological cycle to tropical cooling. This
response arises from the same mechanism that has been found to explain the weakening
of the tropical circulation in response to anthropogenic global warming, but with opposite
sign. Analysis of the model differences shows that the ascending branch of the Walker
circulation strengthens via this mechanism, but vertical motion also weakens over areas
of the Maritime Continent exposed due to lower sea level. Each model exhibits a
different balance between these two mechanisms, and the result is a Pacific Walker
circulation response that is not robust. Further, even those models that simulate a stronger
Walker circulation during the LGM do not simulate clear patterns of surface cooling,
such as La Niña-like cooling or enhanced equatorial cooling, as proposed by previous
studies. In contrast, the changes in the Walker circulation have a robust and distinctive
signature on the tilt of the equatorial thermocline, as expected from zonal momentum
balance. The changes in the Walker circulation also have a clear signature on the spatial
pattern of the precipitation changes. A reduction of the east-west salinity contrast in the
Indian Ocean is related to the precipitation changes resulting from a weakening of the
Indian Walker circulation. These results indicate that proxies of thermocline depth and
sea surface salinity can be used to detect actual LGM changes in the Pacific and Indian
Walker circulations, respectively and help constrain the sensitivity of the Walker
circulation to tropical cooling.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical-system component of earth-system models and models for decadal prediction in the near-term future, for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model.
Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud-droplet activation by aerosols, sub-grid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with eco-system dynamics and hydrology.
Most basic circulation features in AM3 are simulated as realistically, or more so, than in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks and the intensity distributions of precipitation remain problematic, as in AM2.
The last two decades of the 20th century warm in CM3 by .32°C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of .56°C and .52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol cloud interactions, and its warming by late 20th century is somewhat less realistic than in CM2.1, which warmed .66°C but did not include aerosol cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming in a way that is consistent with observed global temperature changes.
Air pollution (ozone and particulate matter in surface air) is strongly linked to synoptic weather and thus is likely sensitive to climate change. In order to isolate the responses of air pollutant transport and wet removal to a warming climate, we examine a simple carbon monoxide (CO)–like tracer (COt) and a soluble version (SAt), both with the 2001 CO emissions, in simulations with the GFDL chemistry-climate model (AM3) for present (1981-2000) and future (2081-2100) climates. In 2081-2100, projected reductions in lower tropospheric ventilation and wet deposition exacerbate surface air pollution as evidenced by higher surface COt and SAt concentrations. However, the average horizontal general circulation patterns in 2081-2100 are similar to 1981-2000, so the spatial distribution of COt changes little. Precipitation is an important factor controlling soluble pollutant wet removal, but the total global precipitation change alone does not necessarily indicate the sign of the soluble pollutant response to climate change. Over certain latitudinal bands, however, the annual wet deposition change can be explained mainly by the simulated changes in large-scale (LS) precipitation. In regions such as North America, differences in the seasonality of LS precipitation and tracer burdens contribute to an apparent inconsistency of changes in annual wet deposition versus annual precipitation. As a step towards an ultimate goal of developing a simple index that can be applied to infer changes in soluble pollutants directly from changes in precipitation fields as projected by physical climate models, we explore here a “Diagnosed Precipitation Impact” (DPI) index. This index captures the sign and magnitude (within 50%) of the relative annual mean changes in the global wet deposition of the soluble pollutant. DPI can only be usefully applied in climate models in which LS precipitation dominates wet deposition and horizontal transport patterns change little as climate warms. Our findings support the need for tighter emission regulations, for both soluble and insoluble pollutants, to obtain a desired level of air quality as climate warms.
Findell, Kirsten L., Pierre Gentine, Benjamin R Lintner, and Christopher Kerr, June 2011: Probability of afternoon precipitation in eastern United States and Mexico enhanced by high evaporation. Nature Geoscience, 4(7), doi:10.1038/ngeo1174. [ Abstract ]
Moisture and heat fluxes from the land surface to the atmosphere form a critical nexus between surface hydrology and atmospheric processes, particularly those relevant to precipitation. Although current theory suggests that soil moisture generally has a positive impact on subsequent precipitation, individual studies have shown support both for and against this positive feedback. Broad assessment of the coupling between soil moisture and evapotranspiration, and evapotranspiration and precipitation, has been limited by a lack of large-scale observations. Quantification of the influence of evapotranspiration on precipitation remains particularly uncertain. Here, we develop and apply physically based, objective metrics for quantifying the impacts of surface evaporative and sensible heat fluxes on the frequency and intensity of convective rainfall during summer, using North American reanalysis data. We show that high evaporation enhances the probability of afternoon rainfall east of the Mississippi and in Mexico. Indeed, variations in surface fluxes lead to changes in afternoon rainfall probability of between 10 and 25% in these regions. The intensity of rainfall, by contrast, is largely insensitive to surface fluxes. We suggest that local surface fluxes represent an important trigger for convective rainfall in the eastern United States and Mexico during the summer, leading to a positive evaporation–precipitation feedback.
The distribution of radiocarbon (14C) in the ocean and atmosphere has fluctuated on timescales ranging from seasons to millennia. It is thought that these fluctuations partly reflect variability in the climate system, offering a rich potential source of information to help understand mechanisms of past climate change. Here, a long simulation with a new, coupled model is used to explore the mechanisms that redistribute 14C within the Earth system on inter-annual to centennial timescales. The model, CM2Mc, is a lower-resolution version of the Geophysical Fluid Dynamics Laboratory's CM2M model, uses no flux adjustments, and incorporates a simple prognostic ocean biogeochemistry model including 14C. The atmospheric 14C and radiative boundary conditions are held constant, so that the oceanic distribution of 14C is only a function of internal climate variability. The simulation displays previously-described relationships between tropical sea surface 14C and the model-equivalents of the El Niño Southern Oscillation and Indonesian Throughflow. Sea surface 14C variability also arises from fluctuations in the circulations of the subarctic Pacific and Southern Ocean, including North Pacific decadal variability, and episodic ventilation events in the Weddell Sea that are reminiscent of the Weddell Polynya of 1974–1976. Interannual variability in the air-sea balance of 14C is dominated by exchange within the belt of intense Southern Westerly winds, rather than at the convective locations where the surface 14C is most variable. Despite significant interannual variability, the simulated impact on air-sea exchange is an order of magnitude smaller than the recorded atmospheric 14C variability of the past millennium. This result partly reflects the importance of variability in the production rate of 14C in determining atmospheric 14C, but may also reflect an underestimate of natural climate variability, particularly in the Southern Westerly winds.
This paper documents time mean simulation characteristics from the ocean and sea ice components in a new coupled climate model developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The climate model, known as CM3, is formulated with effectively the same ocean and sea ice components as the earlier GFDL climate model, CM2.1, yet with extensive developments made to the atmosphere and land model components. Both CM2.1 and CM3 show stable mean climate indices, such as large scale circulation and sea surface temperatures (SSTs). There are notable improvements in the CM3 climate simulation relative to CM2.1, including a modified SST bias pattern and reduced biases in the Arctic sea ice cover. We anticipate SST differences between CM2.1 and CM3 in lower latitudes through analysis of the atmospheric fluxes at the ocean surface in corresponding Atmospheric Model Intercomparison Project (AMIP) simulations. In contrast, SST changes in the high latitudes are dominated by ocean and sea ice effects absent in AMIP simulations. The ocean interior simulation in CM3 is generally warmer than CM2.1, which adversely impacts the interior biases.
Kirtman, Ben P., and Gabriel A Vecchi, 2011: Why climate modelers should worry about atmospheric and oceanic weather In The Global Monsoon System: Research and Forecast, 2nd Edition, Singapore, World Scientific, 511-523.
Koster, Randal D., C Tony Gordon, and Sergey Malyshev, et al., October 2011: The second phase of the global land-atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. Journal of Hydrometeorology, 12(5), doi:10.1175/2011JHM1365.1. [ Abstract ]
The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multi-model “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.
Lee, June-Yi, and William F Stern, et al., September 2011: How predictable is the northern hemisphere summer upper-tropospheric circulation?Climate Dynamics, 37(5-6), doi:10.1007/s00382-010-0909-9. [ Abstract ]
The retrospective forecast skill of three coupled climate models (NCEP CFS, GFDL CM2.1, and CAWCR POAMA 1.5) and their multi-model ensemble (MME) is evaluated, focusing on the Northern Hemisphere (NH) summer upper-tropospheric circulation along with surface temperature and precipitation for the 25-year period of 1981–2005. The seasonal prediction skill for the NH 200-hPa geopotential height basically comes from the coupled models’ ability in predicting the first two empirical orthogonal function (EOF) modes of interannual variability, because the models cannot replicate the residual higher modes. The first two leading EOF modes of the summer 200-hPa circulation account for about 84% (35.4%) of the total variability over the NH tropics (extratropics) and offer a hint of realizable potential predictability. The MME is able to predict both spatial and temporal characteristics of the first EOF mode (EOF1) even at a 5-month lead (January initial condition) with a pattern correlation coefficient (PCC) skill of 0.96 and a temporal correlation coefficient (TCC) skill of 0.62. This long-lead predictability of the EOF1 comes mainly from the prolonged impacts of El Niño-Southern Oscillation (ENSO) as the EOF1 tends to occur during the summer after the mature phase of ENSO. The second EOF mode (EOF2), on the other hand, is related to the developing ENSO and also the interdecadal variability of the sea surface temperature over the North Pacific and North Atlantic Ocean. The MME also captures the EOF2 at a 5-month lead with a PCC skill of 0.87 and a TCC skill of 0.67, but these skills are mainly obtained from the zonally symmetric component of the EOF2, not the prominent wavelike structure, the so-called circumglobal teleconnection (CGT) pattern. In both observation and the 1-month lead MME prediction, the first two leading modes are accompanied by significant rainfall and surface air temperature anomalies in the continental regions of the NH extratropics. The MME’s success in predicting the EOF1 (EOF2) is likely to lead to a better prediction of JJA precipitation anomalies over East Asia and the North Pacific (central and southern Europe and western North America).
Lloyd, I D., and Gabriel A Vecchi, February 2011: Observational evidence for oceanic controls on hurricane intensity. Journal of Climate, 24(4), doi:10.1175/2010JCLI3763.1. [ Abstract ]
The influence of oceanic changes on tropical cyclone activity is investigated using observational estimates of Sea Surface Temperature (SST), air-sea fluxes and ocean subsurface thermal structure over the period 1998–2007. SST conditions are examined before, during, and after the passage of tropical cyclones, through Lagrangian composites along cyclone tracks across all ocean basins, with particular focus on the North Atlantic. We explore the influence of translation speed by separating tropical cyclones according to the translation speed divided by the coriolis parameter. On average for tropical cyclones up to category 2, SST cooling becomes larger as cyclone intensity increases, peaking at 1.8K in the North Atlantic. Beyond category 2 hurricanes, however, the cooling no longer follows an increasing monotonic relationship with intensity. In the North Atlantic, the cooling for stronger hurricanes decreases, while in other ocean basins the cyclone-induced cooling does not significantly differ from Category 2 to Category 5 tropical cyclones, with the exception of the South Pacific. Since the SST response is non-monotonic, with stronger cyclones producing more cooling up to category 2, but producing less or approximately equal cooling for categories 3–5, the observations indicate that oceanic feedbacks can inhibit intensification of cyclones. This result implies that large-scale oceanic conditions are important for tropical cyclone intensity, since they control oceanic sensitivity to atmospheric forcing. Ocean sub-surface thermal data provides additional support for this dependence, showing weaker upper ocean stratification for stronger tropical cyclones. Intensification is suppressed due to negative ocean feedback when stratification favors large SST cooling, but the ability of tropical cyclones to intensify is not inhibited when stratification is weak and cyclone-induced SST cooling is small. Thus, after accounting for tropical cyclone translation speeds and latitudes, it is argued that reduced cooling under extreme tropical cyclones is the manifestation of oceanic conditions on the ability of tropical cyclones to intensify.
Lloyd, I D., Timothy Marchok, and Gabriel A Vecchi, November 2011: Diagnostics comparing sea surface temperature feedbacks from operational hurricane forecasts to observations. Journal of Advances in Modeling Earth Systems, 3, M11002, doi:10.1029/2011MS000075. [ Abstract ]
This paper examines the ability of recent versions of the Geophysical Fluid Dynamics Laboratory
Operational Hurricane Forecast Model (GHM) to reproduce the observed relationship between hurricane
intensity and hurricane-induced Sea Surface Temperature (SST) cooling. The analysis was performed by
taking a Lagrangian composite of all hurricanes in the North Atlantic from 1998–2009 in observations and
2005–2009 for the GHM. A marked improvement in the intensity-SST relationship for the GHM compared
to observations was found between the years 2005 and 2006–2009 due to the introduction of warm-core
eddies, a representation of the loop current, and changes to the drag coefficient parameterization for bulk
turbulent flux computation. A Conceptual Hurricane Intensity Model illustrates the essential steady-state
characteristics of the intensity-SST relationship and is explained by two coupled equations for the
atmosphere and ocean. The conceptual model qualitatively matches observations and the 2006–2009 period
in the GHM, and presents supporting evidence for the conclusion that weaker upper oceanic thermal
stratification in the Gulf of Mexico, caused by the introduction of the loop current and warm core eddies, is
crucial to explaining the observed SST-intensity pattern. The diagnostics proposed by the conceptual model
offer an independent set of metrics for comparing operational hurricane forecast models to observations.
Mahajan, S, Rong Zhang, and Thomas L Delworth, December 2011: Impact of the Atlantic Meridional Overturning Circulation (AMOC) on Arctic surface air temperature and sea-ice variability. Journal of Climate, 24(24), doi:10.1175/2011JCLI4002.1. [ Abstract ]
The simulated impact of the Atlantic Meridional Overturning Circulation (AMOC) on the low frequency variability of the Arctic Surface Air temperature (SAT) and sea-ice extent is studied with a 1000 year-long segment of a control simulation of GFDL CM2.1 climate model. The simulated AMOC variations in the control simulation are found to be significantly anti-correlated with the Arctic sea-ice extent anomalies and significantly correlated with the Arctic SAT anomalies on decadal timescales in the Atlantic sector of the Arctic. The maximum anti-correlation with the Arctic sea-ice extent and the maximum correlation with the Arctic SAT occur when the AMOC Index leads by one year. An intensification of the AMOC is associated with a sea-ice decline in the Labrador, Greenland and Barents Seas in the control simulation, with the largest change occurring in the winter. The recent declining trend in the satellite observed sea-ice extent also shows a similar pattern in the Atlantic sector of the Arctic in the winter, suggesting the possibility of a role of the AMOC in the recent Arctic sea-ice decline in addition to anthropogenic greenhouse gas induced warming. However, in the summer, the simulated sea-ice response to the AMOC in the Pacific sector of the Arctic is much weaker than the observed declining trend, indicating a stronger role for other climate forcings or variability in the recently observed summer sea-ice decline in the Chukchi, Beaufort, East Siberian and Laptev Seas.
Recent studies have suggested that the leading modes of North Atlantic subsurface temperature (Tsub) and sea surface height (SSH) anomalies are induced by Atlantic meridional overturning circulation (AMOC) variations and can be used as fingerprints of AMOC variability. Based on these fingerprints of the AMOC in the GFDL CM2.1 coupled climate model, a linear statistical predictive model of observed fingerprints of AMOC variability is developed in this study. The statistical model predicts a weakening of AMOC strength in a few years after its peak around 2005. Here, we show that in the GFDL coupled climate model assimilated with observed subsurface temperature data, including recent Argo network data (2003–2008), the leading mode of the North Atlantic Tsub anomalies is similar to that found with the objectively analyzed Tsub data and highly correlated with the leading mode of altimetry SSH anomalies for the period 1993–2008. A statistical auto-regressive (AR) model is fit to the time-series of the leading mode of objectively analyzed detrended North Atlantic Tsub anomalies (1955–2003) and is applied to assimilated Tsub and altimetry SSH anomalies to make predictions. A similar statistical AR model, fit to the time-series of the leading mode of modeled Tsub anomalies from the 1000-year GFDL CM2.1 control simulation, is applied to predict modeled Tsub, SSH, and AMOC anomalies. The two AR models show comparable skills in predicting observed Tsub and modeled Tsub, SSH and AMOC variations.
Mehta, V M., and Anthony Rosati, et al., May 2011: Decadal climate predictability and prediction: Where are we?Bulletin of the American Meteorological Society, 92(5), doi:10.1175/2010BAMS3025.1.
Milly, P C., and Krista A Dunne, January 2011: On the hydrologic adjustment of climate-model projections: The potential pitfall of potential evapotranspiration. Earth Interactions, 15(1), doi:10.1175/2010EI363.1. [ Abstract ]
Hydrologic models often are applied to adjust projections of
hydroclimatic change that come from climate models. Such adjustment includes
climate-bias correction, spatial refinement (‘‘downscaling’’), and consideration
of the roles of hydrologic processes that were neglected in the climate model.
Described herein is a quantitative analysis of the effects of hydrologic adjustment
on the projections of runoff change associated with projected twenty-first-century
climate change. In a case study including three climatemodels and 10 river basins
in the contiguous United States, the authors find that relative (i.e., fractional or
percentage) runoff change computed with hydrologic adjustment more often
than not was less positive (or, equivalently, more negative) than what was projected
by the climate models. The dominant contributor to this decrease in runoff
was a ubiquitous change in runoff (median 211%) caused by the hydrologic
model’s apparent amplification of the climate-model-implied growth in potential
evapotranspiration. Analysis suggests that the hydrologic model, on the basis of
the empirical, temperature-based modified Jensen–Haise formula, calculates a
change in potential evapotranspiration that is typically 3 times the change implied
by the climate models, which explicitly track surface energy budgets. In comparison
with the amplification of potential evapotranspiration, central tendencies
of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings
highlight the need for caution when projecting changes in potential evapotranspiration
for use in hydrologic models or drought indices to evaluate climate change
impacts on water.
Santanello, J A., Craig Ferguson, Michael Ek, Paul A Dirmeyer, O Tuinenburg, C Jacobs, Chiel C van Heerwaarden, and Kirsten L Findell, et al., November 2011: Local land-atmosphere coupling (LoCo) research: Status and results. GEWEX News, 21(4), 7-9. [ Abstract ]
We assess the vertical distribution of cloud feedbacks in coupled climate models, taking care to distinguish between cloud feedbacks and a change in cloud forcing. We show that the effect of cloud changes on the longwave fluxes provides a strong positive feedback that is broadly consistent across models. In contrast, the effect of cloud changes on the shortwave fluxes ranges from a modest negative to a strong positive feedback, and is responsible for most of the intermodel spread in net cloud feedback. The feedback from high clouds is positive in all models, and is consistent with that anticipated by the Proportionately Higher Anvil Temperature hypothesis over the tropics. In contrast, low cloud cover is responsible for roughly three-quarters of the difference in global mean net cloud feedback among models, with the largest contributions from regions associated with low-level subtropical marine cloud systems.
Solomon, Amy, and Thomas L Delworth, et al., February 2011: Distinguishing the roles of natural and anthropogenically forced decadal climate variability: Implications for prediction US CLIVAR Decadal Predictability Working Group. Bulletin of the American Meteorological Society, 92(2), doi:10.1175/2010BAMS2962.1. [ Abstract ]
Given that over the course of the next 10–30 years the magnitude of natural decadal variations may rival that of anthropogenically forced climate change on regional scales, it is envisioned that initialized decadal predictions will provide important information for climate-related management and adaptation decisions. Such predictions are presently one of the grand challenges for the climate community. This requires identifying those physical phenomena—and their model equivalents—that may provide additional predictability on decadal time scales, including an assessment of the physical processes through which anthropogenic forcing may interact with or project upon natural variability. Such a physical framework is necessary to provide a consistent assessment (and insight into potential improvement) of the decadal prediction experiments planned to be assessed as part of the IPCC's Fifth Assessment Report.
The study of climate impacts on Living Marine Resources (LMRs) has increased rapidly in recent years with the availability of climate model simulations contributed to the assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Collaboration between climate and LMR scientists and shared understanding of critical challenges for such applications are essential for developing robust projections of climate impacts on LMRs. This paper assesses present approaches for generating projections of climate impacts on LMRs using IPCC-class climate models, recommends practices that should be followed for these applications, and identifies priority developments that could improve current projections. Understanding of the climate system and its representation within climate models has progressed to a point where many climate model outputs can now be used effectively to make LMR projections. However, uncertainty in climate model projections (particularly biases and inter-model spread at regional to local scales), coarse climate model resolution, and the uncertainty and potential complexity of the mechanisms underlying the response of LMRs to climate limit the robustness and precision of LMR projections. A variety of techniques including the analysis of multi-model ensembles, bias corrections, and statistical and dynamical downscaling can ameliorate some limitations, though the assumptions underlying these approaches and the sensitivity of results to their application must be assessed for each application. Developments in LMR science that could improve current projections of climate impacts on LMRs include improved understanding of the multi-scale mechanisms that link climate and LMRs and better representations of these mechanisms within more holistic LMR models. These developments require a strong baseline of field and laboratory observations including long time-series and measurements over the broad range of spatial and temporal scales over which LMRs and climate interact. Priority developments for IPCC-class climate models include improved model accuracy (particularly at regional and local scales), inter-annual to decadal-scale predictions, and the continued development of earth system models capable of simulating the evolution of both the physical climate system and biosphere. Efforts to address these issues should occur in parallel and be informed by the continued application of existing climate and LMR models.
Skillfully predicting North Atlantic hurricane activity months in advance is of potential
societal significance and a useful test of our understanding of the factors controlling
hurricane activity. We describe a statistical-dynamical hurricane forecasting system,
based on a statistical hurricane model, with explicit uncertainty estimates, built from a
suite of high-resolution global atmospheric dynamical model integrations spanning a
broad range of climate states. The statistical model uses two climate predictors: the sea
surface temperature (SST) in the tropical North Atlantic and SST averaged over the
global tropics. The choice of predictors is motivated by physical considerations, results of
high-resolution hurricane modeling and of statistical modeling of the observed record.
The statistical hurricane model is applied to a suite of initialized dynamical global
climate model forecasts of SST to predict North Atlantic hurricane frequency, which
peaks in the August-October season, from different starting dates. Retrospective forecasts
of the 1982-2009 period indicate that skillful predictions can be made from as early as
November of the previous year – that is, skillful forecasts for the coming North Atlantic
hurricane season could be made as the current one is closing. Based on forecasts
initialized between November 2009 and March 2010, the model system predicts that the
upcoming 2010 North Atlantic hurricane season will likely be more active than the 1982-
2009 climatology, with the forecasts initialized in March 2010 predicting an expected
hurricane count of eight and a 50% probability of counts between six (the 1966-2009
median) and nine.
In this study we assess the impact of imperfect sampling in the pre-satellite era (between
1878 and 1965) on North Atlantic hurricane activity measures, and on the long-term
trends in those measures. Our results suggest that a substantial upward adjustment of
hurricane counts is needed prior to 1965 to account for likely ‘missed’ hurricanes due to
sparse density of reporting ship traffic. After adjusting for our estimate of ‘missed’
hurricanes in the basin, the long-term (1878-2008) trend in hurricane counts changes
from significantly positive to no significant change (with a nominally negative trend).
The adjusted hurricane count record is more strongly connected to the difference between
main development region (MDR) sea surface temperature (SST) and tropical-mean SST,
than with MDR SST. Our results do not support the notion that the warming of the
tropical North Atlantic due to anthropogenic greenhouse gas emissions has caused
Atlantic hurricane frequency to increase.
The impact of future anthropogenic forcing on the frequency of tropical storms in the North Atlantic basin has been the subject of intensive investigation. However, whether the number of North Atlantic tropical storms will increase or decrease in a warmer climate is still heavily debated and a consensus has yet to be reached. To shed light on this issue, the authors use a recently developed statistical model, in which the frequency of North Atlantic tropical storms is modeled by a conditional Poisson distribution with rate of occurrence parameter that is a function of tropical Atlantic and mean tropical sea surface temperatures (SSTs). It is shown how the disagreement among dynamical modeling projections of late-twenty-first-century tropical storm frequency can be largely explained by differences in large-scale SST patterns from the different climate model projections used in these studies. The results do not support the notion of large (~200%) increases in tropical storm frequency in the North Atlantic basin over the twenty-first century in response to increasing greenhouse gases (GHGs). Because the statistical model is computationally inexpensive, it is used to examine the impact of different climate models and climate change scenarios on the frequency of North Atlantic tropical storms. The authors estimate that the dominant drivers of uncertainty in projections of tropical storm frequency over the twenty-first century are internal climate variations and systematic intermodel differences in the response of SST patterns to increasing GHGs. Relative to them, uncertainties in total GHG emissions or other climate forcings, within the scenarios explored here, represent a minor source of uncertainty in tropical storm frequency projections. These results suggest that reducing uncertainty in future projections of North Atlantic tropical storm frequency may depend as critically on reducing the uncertainty in the sensitivity of tropical Atlantic warming relative to the tropical mean, in response to GHG increase, as on improving dynamical or statistical downscaling techniques. Moreover, the large uncertainties on century-scale trends that are due to internal climate variability are likely to remain irreducible for the foreseeable future.
As a further illustration of the statistical model’s utility, the authors model projected changes in U.S. landfalling tropical storm activity under a variety of different climate change scenarios and climate models. These results are similar to those for the overall number of North Atlantic tropical storms, and do not point to a large increase in U.S. landfalling tropical storms over the twenty-first century in response to increasing GHGs.
Villarini, Gabriele, Gabriel A Vecchi, Thomas R Knutson, and James A Smith, May 2011: Is the recorded increase in short duration North Atlantic tropical storms spurious?Journal of Geophysical Research: Atmospheres, 116, D10114, doi:10.1029/2010JD015493. [ Abstract ]
The number of North Atlantic tropical storms lasting two days or less exhibits a
very large increase starting from the middle of the twentieth century. Still lacking are
quantitative analyses to assess whether this behavior is more likely associated with
climate variability or with changes in the observational system. By using statisti-
cal methods combined with the current understanding of the physical processes, we
provide further supporting evidence that the trend in North Atlantic tropical storms
lasting two days or less is likely to be spurious. These results imply that studies ex-
amining trends in the frequency of North Atlantic tropical storms from the nineteenth
century should focus on storms of duration greater than about two days.
Villarini, Gabriele, James A Smith, Mary Lynn Baeck, Timothy Marchok, and Gabriel A Vecchi, December 2011: Characterization of rainfall distribution and flooding associated with U.S. landfalling tropical cyclones: Analyses of hurricanes Frances, Ivan, and Jeanne (2004). Journal of Geophysical Research: Atmospheres, 116, D23116, doi:10.1029/2011JD016175. [ Abstract ]
Rainfall and flooding associated with landfalling tropical cyclones are examined through empirical analyses of three hurricanes (Frances, Ivan, and Jeanne) that affected large portions of the eastern U.S. during September 2004. Three rainfall products are considered for the analyses: NLDAS, Stage IV, and TMPA. Each of these products has strengths and weaknesses related to their spatio-temporal resolution and accuracy in estimating rainfall. Based on our analyses, we recommend using the Stage IV product when studying rainfall distribution in landfalling tropical cyclones due to its fine spatial and temporal resolutions (about 4-km and hourly) and accuracy, and the capability of estimating rainfall up to 150 km from the coast. Lagrangian analyses of rainfall distribution relative to the track of the storm are developed to represent evolution of the temporal and spatial structure of rainfall. Analyses highlight the profound changes in rainfall distribution near landfall, the changing contributions to the rainfall field from eyewall convection, inner rain bands and outer rain bands, and the key role of orographic amplification of rainfall. We also present new methods for examining spatial extreme of flooding from tropical cyclones and illustrate the links between evolving rainfall structure and spatial extent of flooding.
Winton, Michael, August 2011: Do climate models underestimate the sensitivity of Northern Hemisphere sea ice cover?Journal of Climate, 24(15), doi:10.1175/2011JCLI4146.1. [ Abstract ]
The sensitivity of Northern Hemisphere sea ice cover to global temperature change is examined in a group of climate models and in the satellite era observations. The models are found to have well defined, distinguishable sensitivities in climate change experiments. The satellite era observations show a larger sensitivity - a larger decline per degree warming - than any of the models. To evaluate the role of natural variability in this discrepancy, the sensitivity PDF is constructed based upon the observed trends and natural variability of multi-decadal ice cover and global temperature trends in a long control run of the GFDL CM2.1 climate model. This comparison shows that the model sensitivities range from about one to more than two pseudo-standard deviations of the variability smaller than observations indicate. The impact of natural Atlantic multi-decadal temperature trends (as simulated by the GFDL model) on the sensitivity distribution is examined and found to be minimal.
Wu, S, Zhengyu Liu, Rong Zhang, and Thomas L Delworth, February 2011: On the observed relationship between the Pacific Decadal Oscillation and the Atlantic Multi-decadal Oscillation. Journal of Oceanography, 67(1), doi:10.1007/s10872-011-0003-x. [ Abstract ]
We studied the relationship between the dominant patterns of sea surface temperature (SST) variability in the North Pacific and the North Atlantic. The patterns are known as the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO). In the analysis we used two different observational data sets for SST. Due to the high degree of serial correlation in the PDO and AMO time series, various tests were carried out to assess the significance of the correlations. The results demonstrated that the correlations are significant when the PDO leads the AMO by 1 year and when the AMO leads the PDO by 11–12 years. The possible physical processes involved are discussed, along with their potential implication for decadal prediction.
Zhang, Shaoqing, January 2011: Impact of observation-optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model. Geophysical Research Letters, 38, L02702, doi:10.1029/2010GL046133. [ Abstract ]
A skillful decadal prediction that foretells varying regional climate conditions over seasonal-interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate observing system tend to drift away from observed states towards the imperfect model climate due to model biases arising from imperfect model equations, numeric schemes and physical parameterizations, as well as the errors in the values of model parameters. Here I show how to mitigate the model bias through optimizing model parameters using observations so as to constrain the model drift in climate predictions with a simple decadal prediction model. Results show that the coupled state-parameter optimization with observations greatly enhances the predictability of the coupled model. While valid “atmospheric” forecasts are extended by more than 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time scale predictions.
Zhang, D, Rym Msadek, Michael J McPhaden, and Thomas L Delworth, April 2011: Multidecadal variability of the North Brazil Current and its connection to the Atlantic Meridional Overturning Circulation. Journal of Geophysical Research: Oceans, 116, C04012, doi:10.1029/2010JC006812. [ Abstract ]
The North Brazil Current (NBC) connects the North and South Atlantic and is the major pathway for the surface return flow of the Atlantic meridional overturning circulation (AMOC). Here, we calculate the NBC geostrophic transport time series based on 5 decades of observations near the western boundary off the coast of Brazil. Results reveal a multidecadal NBC variability that lags Labrador Sea deep convection by a few years. The NBC transport time series is coherent with the Atlantic Multidecadal Oscillation in sea surface temperature, which also has been widely linked to AMOC fluctuations in previous modeling studies. Our results thus suggest that the observed multidecadal NBC transport variability is a useful indicator for AMOC variations. The suggested connection between the NBC and AMOC is assessed in a 700 year control simulation of the Geophysical Fluid Dynamics Laboratory's CM2.1 coupled climate model. The model results are in agreement with observations and further demonstrate that the variability of NBC transport is a good index for tracking AMOC variations. Concerning the debate about whether a slowdown of AMOC has already occurred under global warming, the observed NBC transport time series suggests strong multidecadal variability but no significant trend.
Zhang, Shaoqing, December 2011: A study of impacts of coupled model initial shocks and state-parameter optimization on climate predictions using a simple pycnocline prediction model. Journal of Climate, 24(23), doi:10.1175/JCLI-D-10-05003.1. [ Abstract ]
A skillful decadal prediction that foretells varying regional climate conditions over seasonal-interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate observing system tend to drift away from observed states towards the imperfect model climate due to model biases arising from imperfect model equations, numeric schemes and physical parameterizations, as well as the errors in the values of model parameters. Here a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce “observation”-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal-interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time scale predictions. The coupled model state-parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time scale predictions.
The sensitivity of the North Atlantic Ocean Circulation to an abrupt change in the Nordic Sea overflow is investigated for the first time using a high resolution eddy-permitting global coupled ocean-atmosphere model (GFDL CM2.5). The Nordic Sea overflow is perturbed through the change of the bathymetry in GFDL CM2.5. We analyze the Atlantic Meridional Overturning Circulation (AMOC) adjustment process and the downstream oceanic response to the perturbation. The results suggest that north of 34N, AMOC changes induced by changes in the Nordic Sea overflow propagate on the slow tracer advection time scale, instead of the fast Kelvin wave time scale, resulting in a time lead of several years between subpolar and subtropical AMOC changes. The results also show that a stronger and deeper-penetrating Nordic Sea overflow leads to stronger and deeper AMOC, stronger northward ocean heat transport, reduced Labrador Sea deep convection, stronger cyclonic Northern Recirculation Gyre (NRG), westward shift of the North Atlantic Current (NAC) and southward shift of the Gulf Stream, warmer sea surface temperature (SST) east of Newfoundland and colder SST south of the Grand Banks, stronger and deeper NAC and Gulf Stream, and stronger oceanic eddy activities along the NAC and the Gulf Stream paths. A stronger/weaker Nordic Sea overflow also leads to a contracted/expanded subpolar gyre (SPG). This sensitivity study points to the important role of the Nordic Sea overflow in the large scale North Atlantic ocean circulation, and it is crucial for climate models to have a correct representation of the Nordic Sea overflow.
Several recent models suggest that the frequency of Atlantic tropical cyclones could decrease as the climate warms. However, these models are unable to reproduce storms of category 3 or higher intensity. We explored the influence of future global warming on Atlantic hurricanes with a downscaling strategy by using an operational hurricane-prediction model that produces a realistic distribution of intense hurricane activity for present-day conditions. The model projects nearly a doubling of the frequency of category 4 and 5 storms by the end of the 21st century, despite a decrease in the overall frequency of tropical cyclones, when the downscaling is based on the ensemble mean of 18 global climate-change projections. The largest increase is projected to occur in the Western Atlantic, north of 20°N.
Based on independent observations, we estimate the sea level budget and linear trends for individual ocean basins and the world ocean during 2004–2007. Even though it is confirmed that the seasonal variation of global sea level is balanced by the different sea level components (total sea level change from satellite altimetry equals to the sum of the steric height contribution obtained by Argo profiles and any variability in ocean mass observed from GRACE), basin-scale sea level budgets show very different characteristics. Sea level budgets over the South Pacific and Antarctic Ocean maintain a good balance both on seasonal to interannual time scales. Meanwhile, only the satellite altimeter data exhibits a large 4-year trend over the South Indian Ocean. This basin significantly impacts the magnitude of the disagreement for the global sea level budget. Large differences among the 3 different gravity fields related to the hydrologic signals in the Atlantic and Indian Ocean could be one of the major causes of the imbalance in the global sea level budget.
Chen, M-T, and Rong Zhang, et al., December 2010: Dynamic millennial-scale climate changes in the Northwestern Pacific over the past 40,000 years. Geophysical Research Letters, 37, L23603, doi:10.1029/2010GL045202. [ Abstract ]
Ice core records of polar temperatures and greenhouse gases document abrupt
millennial-scale oscillations that suggest the reduction or shutdown of thermohaline
Circulation (THC) in the North Atlantic Ocean may induce the abrupt cooling in the northern hemisphere. It remains unknown, however, whether the sea surface
temperature (SST) is cooling or warming in the Kuroshio of the Northwestern Pacific
during the cooling event. Here we present an AMS 14C-dated foraminiferal Mg/Ca SST
record from the central Okinawa Trough and document that the SST variations exhibit
two steps of warming since 21 ka --- at 14.7 ka and 12.8 ka, and a cooling (~1.5°C)
during the interval of the Younger Dryas. By contrast, we observed no SST change or
oceanic warming (~1.5-2°C) during the episodes of Northern Hemisphere cooling
between ~21-40 ka. We therefore suggest that the “Antarctic-like” timing and amplitude
of millennial-scale SST variations in the subtropical Northwestern Pacific between 20-
40 ka may have been determined by rapid ocean adjustment processes in response to
abrupt wind stress and meridional temperature gradient changes in the North Pacific.
Collins, Matthew, Gabriel A Vecchi, and Andrew T Wittenberg, et al., June 2010: The impact of global warming on the tropical Pacific and El Niño Prepared on behalf of the CLIVAR Pacific Panel. Nature Geoscience, 3(6), doi:10.1038/ngeo868. [ Abstract ]
The El Niño–Southern Oscillation (ENSO) is a naturally occurring fluctuation that originates in the tropical Pacific region and affects ecosystems, agriculture, freshwater supplies, hurricanes and other severe weather events worldwide. Under the influence of global warming, the mean climate of the Pacific region will probably undergo significant changes. The tropical easterly trade winds are expected to weaken; surface ocean temperatures are expected to warm fastest near the equator and more slowly farther away; the equatorial thermocline that marks the transition between the wind-mixed upper ocean and deeper layers is expected to shoal; and the temperature gradients across the thermocline are expected to become steeper. Year-to-year ENSO variability is controlled by a delicate balance of amplifying and damping feedbacks, and one or more of the physical processes that are responsible for determining the characteristics of ENSO will probably be modified by climate change. Therefore, despite considerable progress in our understanding of the impact of climate change on many of the processes that contribute to El Niño variability, it is not yet possible to say whether ENSO activity will be enhanced or damped, or if the frequency of events will change.
DiNezio, P, A C Clement, and Gabriel A Vecchi, April 2010: Reconciling differing views of tropical Pacific climate change. EOS, 91(16), 141-142. [ PDF ]
Simulations from a fine-resolution global coupled model, the Geophysical Fluid Dynamics Laboratory
Climate Model, version 2.4 (CM2.4), are presented, and the results are compared with a coarse version of the
same coupled model, CM2.1, under idealized climate change scenarios. A particular focus is given to the
dynamical response of the Southern Ocean and the role played by the eddies—parameterized or permitted—
in setting the residual circulation and meridional density structure. Compared to the case in which eddies are
parameterized and consistent with recent observational and idealized modeling studies, the eddy-permitting
integrations of CM2.4 show that eddy activity is greatly energized with increasing mechanical and buoyancy
forcings, buffering the ocean to atmospheric changes, and the magnitude of the residual oceanic circulation
response is thus greatly reduced. Although compensation is far from being perfect, changes in poleward eddy
fluxes partially compensate for the enhanced equatorward Ekman transport, leading to weak modifications in
local isopycnal slopes, transport by the Antarctic Circumpolar Current, and overturning circulation. Since the
presence of active ocean eddy dynamics buffers the oceanic response to atmospheric changes, the associated
atmospheric response to those reduced ocean changes is also weakened. Further, it is hypothesized that
present numerical approaches for the parameterization of eddy-induced transports could be too restrictive
and prevent coarse-resolution models from faithfully representing the eddy response to variability and change
in the forcing fields.
Farneti, Riccardo, and Thomas L Delworth, October 2010: The role of mesoscale eddies in the remote oceanic response to altered Southern Hemisphere winds. Journal of Physical Oceanography, 40(10), doi:10.1175/2010JPO4480.1. [ Abstract ]
It has been suggested that a strengthening of the Southern Hemisphere winds would
induce a more vigorous overturning through an increased northward Ekman flux, bringing
more light waters into the oceanic basins and enhancing the upwelling of North
Atlantic Deep Water in the Southern Ocean, thereby increasing ocean ventilation. We
present here simulations from a coarse and a fine resolution version of a coupled model
subject to idealized wind stress changes in the Southern Ocean. In the fine resolution
eddy-permitting model, we find that changes in poleward eddy fluxes largely compensate
for the enhanced equatorward Ekman transport in the Southern Ocean. As a consequence,
northward transport of light waters, pycnocline depth, Northern Hemisphere
overturning and Southern Ocean upwelling anomalies are much reduced compared with
simulations in the coarse resolution model with parameterized eddies. These results
point to a relatively weak sensitivity of present-day global ocean
Climate model simulations run as part of the Climate Variability and Predictability (CLIVAR) Drought Working Group initiative were analyzed to determine the impact of three patterns of sea surface temperature (SST) anomalies on drought and pluvial frequency and intensity around the world. The three SST forcing patterns include a global pattern similar to the background warming trend, a pattern in the Pacific, and a pattern in the Atlantic. Five different global atmospheric models were forced by fixed SSTs to test the impact of these SST anomalies on droughts and pluvials relative to a climatologically forced control run.
The five models generally yield similar results in the locations of drought and pluvial frequency changes throughout the annual cycle in response to each given SST pattern. In all of the simulations, areas with an increase in the mean drought (pluvial) conditions tend to also show an increase in the frequency of drought (pluvial) events. Additionally, areas with more frequent extreme events also tend to show higher intensity extremes. The cold Pacific anomaly increases drought occurrence in the United States and southern South America and increases pluvials in Central America and northern and central South America. The cold Atlantic anomaly increases drought occurrence in southern Central America, northern South America, and central Africa and increases pluvials in central South America. The warm Pacific and Atlantic anomalies generally lead to reversals of the drought and pluvial increases described with the corresponding cold anomalies. More modest impacts are seen in other parts of the world. The impact of the trend pattern is generally more modest than that of the two other anomaly patterns.
Because ocean color alters the absorption of sunlight, it can produce changes in sea surface temperatures with further impacts on atmospheric circulation. These changes can project onto fields previously recognized to alter the distribution of tropical cyclones. If the North Pacific subtropical gyre contained no absorbing and scattering materials, the result would be to reduce subtropical cyclone activity in the subtropical Northwest Pacific by 2/3, while concentrating cyclone tracks along the equator. Predicting tropical cyclone activity using coupled models may thus require consideration of the details of how heat moves into the upper thermocline as well as biogeochemical cycling.
The fast and slow components of global warming in a comprehensive climate model are isolated by examining the response to an instantaneous return to pre-industrial forcing. The response is characterized by an initial fast exponential decay with an e-folding time smaller than 5 years, leaving behind a remnant that evolves more slowly. The slow component is estimated to be small at present, as measured by the global mean near-surface air temperature, and, in the model examined, grows to 0.4C by 2100 in the A1B SRES scenario and then to 1.4C by 2300 if one holds radiative forcing fixed after 2100. The dominance of the fast component at present is supported by examining the response to an instantaneous doubling of CO2 and by the excellent fit to the model's ensemble mean 20th century evolution with a simple one-box model with no long times scales.
Hurrell, J W., Gerald A Meehl, David Bader, Thomas L Delworth, Ben P Kirtman, and B A Wielicki, December 2010: Reply to Comments on “A Unified Modeling Approach to Climate System Prediction”. Bulletin of the American Meteorological Society, 91(12), doi:10.1175/2010BAMS3118.1.
Joyce, Terrence M., and Rong Zhang, June 2010: On the path of the Gulf Stream and the Atlantic Meridional overturning circulation. Journal of Climate, 23(11), doi:10.1175/2010JCLI3310.1. [ Abstract ]
The Atlantic meridional overturning circulation (AMOC) simulated in various ocean-only and coupled atmosphere–ocean numerical models often varies in time because of either forced or internal variability. The path of the Gulf Stream (GS) is one diagnostic variable that seems to be sensitive to the amplitude of the AMOC, yet previous modeling studies show a diametrically opposed relationship between the two variables. In this note this issue is revisited, bringing together ocean observations and comparisons with the GFDL Climate Model version 2.1 (CM2.1), both of which suggest a more southerly (northerly) GS path when the AMOC is relatively strong (weak). Also shown are some examples of possible diagnostics to compare various models and observations on the relationship between shifts in GS path and changes in AMOC strength in future studies.
Knutson, Thomas R., Christopher Landsea, and Kerry A Emanuel, May 2010: Tropical cyclones and climate change: A review In Global Perspectives on Tropical Cyclones: From Science to Mitigation, Singapore, World Scientific Publishing Company, 243-284. [ Abstract ]
A review of the science on the relationship between climate change and tropical cyclones (TCs) is presented. Topics include changes in aspects of tropical climate that are relevant to TC activity; observed trends and low-frequency variability of TC activity; paleoclimate proxy studies; theoretical and modeling studies; future projections; roadblocks to resolution of key issues; and recommendations for making future progress.
Knutson, Thomas R., J McBride, Johnny C L Chan, Kerry A Emanuel, G Holland, Christopher Landsea, Isaac M Held, James Kossin, A K Srivastava, and M Sugi, March 2010: Tropical cyclones and climate change. Nature Geoscience, 3, doi:doi:10.1038/ngeo779. [ Abstract ]
Whether the characteristics of tropical cyclones have changed or will change in a warming climate — and if so, how — has been the subject of considerable investigation, often with conflicting results. Large amplitude fluctuations in the frequency and intensity of tropical cyclones greatly complicate both the detection of long-term trends and their attribution to rising levels of atmospheric greenhouse gases. Trend detection is further impeded by substantial limitations in the availability and quality of global historical records of tropical cyclones. Therefore, it remains uncertain whether past changes in tropical cyclone activity have exceeded the variability expected from natural causes. However, future projections based on theory and high-resolution dynamical models consistently indicate that greenhouse warming will cause the globally averaged intensity of tropical cyclones to shift towards stronger storms, with intensity increases of 2–11% by 2100. Existing modelling studies also consistently project decreases in the globally averaged frequency of tropical cyclones, by 6–34%. Balanced against this, higher resolution modelling studies typically project substantial increases in the frequency of the most intense cyclones, and increases of the order of 20% in the precipitation rate within 100 km of the storm centre. For all cyclone parameters, projected changes for individual basins show large variations between different modelling studies.
Koster, Randal D., C Tony Gordon, and Sergey Malyshev, et al., January 2010: Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophysical Research Letters, 37, L02402, doi:10.1029/2009GL041677. [ Abstract ]
The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is aimed at quantifying, with a suite of long-range forecast systems, the degree to which realistic land surface initialization contributes to the skill of subseasonal precipitation and air temperature forecasts. Results, which focus here on North America, show significant contributions to temperature prediction skill out to two months across large portions of the continent. For precipitation forecasts, contributions to skill are much weaker but are still significant out to 45 days in some locations. Skill levels increase markedly when calculations are conditioned on the magnitude of the initial soil moisture anomaly.
Kug, Jong-Seong, J Choi, S I An, Fei-Fei Jin, and Andrew T Wittenberg, March 2010: Warm pool and cold tongue El Niño events as simulated by the GFDL 2.1 coupled GCM. Journal of Climate, 23(5), doi:10.1175/2009JCLI3293.1. [ Abstract ]
Recent studies report that two types of El Niño events have been observed. One is the cold tongue (CT) El Niño, which is characterized by relatively large sea surface temperature (SST) anomalies in the eastern Pacific, and the other is the warm pool (WP) El Niño, in which SST anomalies are confined to the central Pacific. Here, both types of El Niño events are analyzed in a long-term coupled GCM simulation. The present model simulates the major observed features of both types of El Niño, incorporating the distinctive patterns of each oceanic and atmospheric variable. It is also demonstrated that each type of El Niño has quite distinct dynamic processes, which control their evolutions. The CT El Niño exhibits strong equatorial heat discharge poleward and thus the dynamical feedbacks control the phase transition from a warm event to a cold event. On the other hand, the discharge process in the WP El Niño is weak because of its spatial distribution of ocean dynamic field. The positive SST anomaly of WP El Niño is thermally damped through the intensified evaporative cooling.
Records of Atlantic basin tropical cyclones (TCs) since the late nineteenth century indicate a very large upward trend in storm frequency. This increase in documented TCs has been previously interpreted as resulting from anthropogenic climate change. However, improvements in observing and recording practices provide an alternative interpretation for these changes: recent studies suggest that the number of potentially missed TCs is sufficient to explain a large part of the recorded increase in TC counts. This study explores the influence of another factor—TC duration—on observed changes in TC frequency, using a widely used Atlantic hurricane database (HURDAT). It is found that the occurrence of short-lived storms (duration of 2 days or less) in the database has increased dramatically, from less than one per year in the late nineteenth–early twentieth century to about five per year since about 2000, while medium- to long-lived storms have increased little, if at all. Thus, the previously documented increase in total TC frequency since the late nineteenth century in the database is primarily due to an increase in very short-lived TCs.
The authors also undertake a sampling study based upon the distribution of ship observations, which provides quantitative estimates of the frequency of missed TCs, focusing just on the moderate to long-lived systems with durations exceeding 2 days in the raw HURDAT. Upon adding the estimated numbers of missed TCs, the time series of moderate to long-lived Atlantic TCs show substantial multidecadal variability, but neither time series exhibits a significant trend since the late nineteenth century, with a nominal decrease in the adjusted time series.
Thus, to understand the source of the century-scale increase in Atlantic TC counts in HURDAT, one must explain the relatively monotonic increase in very short-duration storms since the late nineteenth century. While it is possible that the recorded increase in short-duration TCs represents a real climate signal, the authors consider that it is more plausible that the increase arises primarily from improvements in the quantity and quality of observations, along with enhanced interpretation techniques. These have allowed National Hurricane Center forecasters to better monitor and detect initial TC formation, and thus incorporate increasing numbers of very short-lived systems into the TC database.
Lee, June-Yi, Bin Wang, I-S Kang, J Shukla, Arun Kumar, Jong-Seong Kug, C E Schemm, J-J Luo, T Yamagata, X Fu, Oscar Alves, William F Stern, Anthony Rosati, and C-K Park, August 2010: How are seasonal prediction skills related to models’ performance on mean state and annual cycle?Climate Dynamics, 35(2-3), doi:10.1007/s00382-010-0857-4. [ Abstract ]
Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.
Lee, Tong, and Anthony Rosati, et al., August 2010: Consistency and fidelity of Indonesian-throughflow total volume transport estimated by 14 ocean data assimilation products. Dynamics of Atmospheres and Oceans, 50(2), doi:10.1016/j.dynatmoce.2009.12.004. [ Abstract ]
Monthly averaged total volume transport of the Indonesian throughflow (ITF) estimated by 14 global ocean data assimilation (ODA) products that are decade to multi-decade long are compared among themselves and with observations from the INSTANT Program (2004–2006). The main goals of the comparisons are to examine the consistency and evaluate the skill of different ODA products in simulating ITF transport. The ensemble averaged, time-mean value of ODA estimates is 13.6 Sv (1 Sv = 106 m3/s) for the common 1993–2001 period and 13.9 Sv for the 2004–2006 INSTANT Program period. These values are close to the 15-Sv estimate derived from INSTANT observations. All but one ODA time-mean estimate fall within the range of uncertainty of the INSTANT estimate. In terms of temporal variability, the scatter among different ODA estimates averaged over time is 1.7 Sv, which is substantially smaller than the magnitude of the temporal variability simulated by the ODA systems. Therefore, the overall “signal-to-noise” ratio for the ensemble estimates is larger than one. The best consistency among the products occurs on seasonal-to-interannual time scales, with generally stronger (weaker) ITF during boreal summer (winter) and during La Nina (El Nino) events. The scatter among different products for seasonal-to-interannual time scales is approximately 1 Sv. Despite the good consistency, systematic difference is found between most ODA products and the INSTANT observations. All but the highest-resolution (18 km) ODA product show a dominant annual cycle while the INSTANT estimate and the 18-km product exhibit a strong semi-annual signal. The coarse resolution is an important factor that limits the level of agreement between ODA and INSTANT estimates. Decadal signals with periods of 10–15 years are seen. The most conspicuous and consistent decadal change is a relatively sharp increase in ITF transport during 1993–2000 associated with the strengthening tropical Pacific trade wind. Most products do not show a weakening ITF after the mid-1970s’ associated with the weakened Pacific trade wind. The scatter of ODA estimates is smaller after than before 1980, reflecting the impact of the enhanced observations after the 1980s. To assess the representativeness of using the average over a three-year period (e.g., the span of the INSTANT Program) to describe longer-term mean, we investigate the temporal variations of the three-year low-pass ODA estimates. The average variation is about 3.6 Sv, which is largely due to the increase of ITF transport from 1993 to 2000. However, the three-year average during the 2004–2006 INSTANT Program period is within 0.5 Sv of the long-term mean for the past few decades.
Lengaigne, Matthieu, and Gabriel A Vecchi, August 2010: Contrasting the termination of moderate and extreme El Niño events in coupled general circulation models. Climate Dynamics, 35(2-3), doi:10.1007/s00382-009-0562-3. [ Abstract ]
As in the observed record, the termination of El Niño in the coupled IPCC-AR4 climate models involves meridional processes tied to the seasonal cycle. These meridional processes both precondition the termination of El Niño events in general and lead to a peculiar termination of extreme El Niño events (such as those of 1982–83 and 1997–98), in which the eastern equatorial Pacific warm sea surface temperature anomalies (SSTA) persist well into boreal spring/early-summer. The mechanisms controlling the peculiar termination of extreme El Niño events, which involves to the development of an equatorially centred intertropical convergence zone, are consistent across the four models that exhibit extreme El Niños and observational record, suggesting that this peculiar termination represents a general feature of extreme El Niños. Further, due to their unusual termination, extreme El Niños exhibit an apparent eastward propagation of their SSTA, which can strongly influence estimates of the apparent propagation of ENSO over multi-decadal periods. Interpreting these propagation changes as evidence of changes in the underlying dynamical feedbacks behind El Niño could therefore be misleading, given the strong influence of a single extreme event.
Understanding the plausible causes for the observed high dust concentrations in Antarctic ice cores during
the Last Glacial Maximum (LGM) is crucial for interpreting the Antarctic dust records in the past climates
and could provide insights into dust variability in future climates. Using the Geophysical Fluid Dynamics
Laboratory (GFDL) General Circulation Models, we conduct an investigation into the various factors
modulating dust emission, transport and deposition, with a view towards an improved quantification of the
LGM dust enhancements in the Antarctic ice cores. The model simulations show that the expansion of
source areas and changes in the Antarctic ice accumulation rates together can account for most of the
observed increase of dust concentrations in the Vostok, Dome C and Taylor Dome cores, but there is an
overestimate of the LGM-to-present ratio in the case of the Byrd core. The source expansion due to the
lowering of sea level yields a factor of 2–3 higher contribution than that due to the reduction of continental
vegetation. The changes in other climate parameters (e.g., SH precipitation change) are estimated to be
relatively less important within the context of this sensitivity study, while the model-simulated LGM
surface winds yield a 20–30 % reduction rather than an increase in dust deposition in Antarctica. This
research yields insights towards a fundamental understanding of the causes for the significant enhancement
of the dust deposition in the Antarctic ice cores during the LGM.
Lloyd, I D., and Gabriel A Vecchi, February 2010: Submonthly Indian Ocean cooling events and their interaction with large-scale conditions. Journal of Climate, 23(3), doi:10.1175/2009JCLI3067.1. [ Abstract ]
The Indian Ocean exhibits strong variability on a number of time scales, including prominent intraseasonal variations in both the atmosphere and ocean. Of particular interest is the south tropical Indian Ocean thermocline ridge, a region located between 12° and 5°S, which exhibits prominent variability in sea surface temperature (SST) due to dominant winds that raise the thermocline and shoal the mixed layer. In this paper, submonthly (less than 30 day) cooling events in the thermocline ridge region are diagnosed with observations and models, and are related to large-scale conditions in the Indo-Pacific region. Observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite were used to identify 16 cooling events in the period 1998–2007, which on average cannot be fully accounted for by air–sea enthalpy fluxes. Analysis of observations and a hierarchy of models, including two coupled global climate models (GFDL CM2.1 and GFDL CM2.4), indicates that ocean dynamical changes are important to the cooling events. For extreme cooling events (above 2.5 standard deviations), air–sea enthalpy fluxes account for approximately 50% of the SST signature, and oceanic processes cannot in general be neglected. For weaker cooling events (1.5–2.5 standard deviations), air–sea enthalpy fluxes account for a larger fraction of the SST signature. Furthermore, it is found that cooling events are preconditioned by large-scale, low-frequency changes in the coupled ocean–atmosphere system. When the thermocline is unusually shallow in the thermocline ridge region, cooling events are more likely to occur and are stronger; these large-scale conditions are more (less) likely during La Niña (El Niño/Indian Ocean dipole) events. Strong cooling events are associated with changes in atmospheric convection, which resemble the Madden–Julian oscillation, in both observations and the models.
Lowe, J A., and Thomas R Knutson, et al., 2010: Past and future changes in extreme sea levels and waves In Understanding Sea-Level Rise and Variability, Oxford, UK, Wiley-Blackwell, 326-375.
Milly, P C., et al., 2010: Terrestrial water-storage contributions to sea-level rise and variability In Understanding Sea-Level Rise and Variability, Oxford, UK, Wiley-Blackwell, 226-255.
The North Atlantic is among the few places where decadal climate variations are considered potentially predictable. The physical mechanisms of the decadal variability are hypothesized to be associated with fluctuations of the Atlantic meridional overturning circulation (AMOC). Perfect model predictability experiments using the GFDL CM2.1 climate model are analyzed to investigate the potential predictability of the AMOC. Results indicate that the AMOC is predictable up to 20 years. We further connect AMOC predictability to readily observable fields. We show that modeled surface and subsurface signatures of AMOC variations defined by characteristic patterns of sea surface height, subsurface temperature, and upper ocean heat content anomalies, have a potential predictability similar to the AMOC's. Since we have longer observational records for these quantities than for direct measurements of the AMOC, our study highlights a potentially new promising method for monitoring AMOC variations, and hence assessing the predictability of the real climate system.
Rienecker, M M., Stephen M Griffies, and Anthony Rosati, et al., September 2010: Synthesis and Assimilation Systems: Essential Adjuncts to the Global Ocean Observing System In OceanObs’09: Sustained Ocean Observations and Information for Society, Vol. 2, ESA Publication, doi:doi:10.5270/OceanObs09.pp.31.
Seager, Richard, and Gabriel A Vecchi, December 2010: Greenhouse warming and the 21st century hydroclimate of southwestern North America. Proceedings of the National Academy of Sciences, 107(50), doi:10.1073/pnas.0910856107. [ Abstract ]
Climate models robustly predict that the climate of southwestern
North America, defined as from the western Great Plains to the
Pacific Ocean and from the Oregon border to southern Mexico,
will dry throughout the current century as a consequence of rising
greenhouse gases. This regional drying is part of a general
drying of the subtropics and poleward expansion of the subtropical
dry zones. It is shown that the drying is driven by a reduction
of of winter season precipitation associated with a poleward
broadening of the North Pacific storm track and increased moisture
divergence by transient eddies. Observations to date cannot
confirm that this transition to a drier climate is already underway
due to the presence of large amplitude decadal variations of presumed
natural origin but it is anticipated that the anthropogenic
drying will reach the amplitude of natural decadal variability by
mid-century. In addition to this drop in total precipitation warming
is already causing a decline in mountain snow mass and an
advance of spring snow melt disrupting the natural water storage
systems that are part of the regions water supply system. Uncertainties
in how radiative forcing will impact the tropical Pacific
climate system create uncertainties in the amplitude of drying in
southwest North America with a La Niña-like response creating a
worst case scenario of greater drying.
Seager, Richard, N Naik, and Gabriel A Vecchi, September 2010: Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. Journal of Climate, 23(17), doi:10.1175/2010JCLI3655.1. [ Abstract ]
The mechanisms of changes in the large-scale hydrological cycle projected by 15 models participating in the
Coupled Model Intercomparison Project phase 3 and used for the Intergovernmental Panel on Climate
Change’s Fourth Assessment Report are analyzed by computing differences between 2046 and 2065 and 1961
and 2000. The contributions to changes in precipitation minus evaporation, P2E, caused thermodynamically
by changes in specific humidity, dynamically by changes in circulation, and by changes in moisture transports
by transient eddies are evaluated. The thermodynamic and dynamic contributions are further separated into
advective and divergent components. The nonthermodynamic contributions are then related to changes in the
mean and transient circulation. The projected change in P 2 E involves an intensification of the existing
pattern of P2E with wet areas [the intertropical convergence zone (ITCZ) and mid- to high latitudes] getting
wetter and arid and semiarid regions of the subtropics getting drier. In addition, the subtropical dry zones
expand poleward. The accentuation of the twentieth-century pattern of P2E is in part explained by increases
in specific humidity via both advection and divergence terms. Weakening of the tropical divergent circulation
partially opposes the thermodynamic contribution by creating a tendency to decreased P2E in the ITCZ and
to increased P2E in the descending branches of the Walker and Hadley cells. The changing mean circulation
also causes decreased P 2 E on the poleward flanks of the subtropics because the descending branch of the
Hadley Cell expands and the midlatitude meridional circulation cell shifts poleward. Subtropical drying and
poleward moistening are also contributed to by an increase in poleward moisture transport by transient
eddies. The thermodynamic contribution to changing P 2 E, arising from increased specific humidity, is
almost entirely accounted for by atmospheric warming under fixed relative humidity.
El Niño and La Niña comprise the dominant mode of tropical climate variability: the El Niño and Southern Oscillation (ENSO) phenomenon. ENSO variations influence climate, ecosystems, and societies around the globe. It is, therefore, of great interest to understand the character of past and future ENSO variations. In this brief review, we explore our current understanding of these issues. The amplitude and character of ENSO have been observed to exhibit substantial variations on timescales of decades to centuries; many of these changes over the past millennium resemble those that arise from internally generated climate variations in an unforced climate model. ENSO activity and characteristics have been found to depend on the state of the tropical Pacific climate system, which is expected to change in the 21st century in response to changes in radiative forcing (including increased greenhouse gases) and internal climate variability. However, the extent and character of the response of ENSO to increased in greenhouse gases are still a topic of considerable research, and given the results published to date, we cannot yet rule out possibilities of an increase, decrease, or no change in ENSO activity arising from increases in CO2. Yet we are fairly confident that ENSO variations will continue to occur and influence global climate in the coming decades and centuries. Changes in continental climate, however, could alter the remote impacts of El Niño and La Niña.
Villarini, Gabriele, Gabriel A Vecchi, and James A Smith, July 2010: Modeling the dependence of tropical storm counts in the North Atlantic basin on climate indices. Monthly Weather Review, 138(7), doi:10.1175/2010MWR3315.1. [ Abstract ]
The authors analyze and model time series of annual counts of tropical storms lasting more than 2 days in
the North Atlantic basin and U.S. landfalling tropical storms over the period 1878–2008 in relation to different
climate indices. The climate indices considered are the tropical Atlantic sea surface temperature (SST),
tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Given
the uncertainties associated with a possible tropical storm undercount in the presatellite era, two different
time series of counts for the North Atlantic basin are employed: one is the original (uncorrected) tropical
storm record maintained by the National Hurricane Center and the other one is with a correction for the
estimated undercount associated with a changing observation network. Two different SST time series are
considered: the Met Office’s HadISSTv1 and NOAA’s Extended Reconstructed SST.
Given the nature of the data (counts), a Poisson regression model is adopted. The selection of statistically
significant covariates is performed by penalizing models for adding extra parameters and two penalty functions
are used. Depending on the penalty function, slightly different models, both in terms of covariates and
dependence of themodel’s parameter, are obtained, showing that there is not a ‘‘single best’’model.Moreover,
results are sensitive to the undercount correction and the SST time series.
Suggestions concerning the model to use are provided, driven by both the outcomes of the statistical
analyses and the current understanding of the underlying physical processes responsible for the genesis,
development, and tracks of tropical storms in the North Atlantic basin. Although no single model is unequivocally
superior to the others, the authors suggest a very parsimonious family ofmodels using as covariates
tropical Atlantic and tropical mean SSTs.
We propose a modification to the standard forcing/feedback diagnostic energy balance model to account for 1) differences between effective and equilibrium climate sensitivities and 2) the variation of effective sensitivity over time in climate change experiments with coupled atmosphere-ocean climate models. In the spirit of Hansen et al (2005) we introduce an efficacy factor to the ocean heat uptake. Comparing the time-evolution of the surface warming in high and low efficacy models demonstrates the role of this efficacy in the transient response to CO2 forcing. Abrupt CO2 increase experiments show that the large efficacy of the Geophysical Fluid Dynamics Laboratory's CM2.1 model sets up in the first two decades following the increase in forcing. The use of an efficacy is necessary to fit this model's global mean temperature evolution in periods with both increasing and stable forcing. The inter-model correlation of transient climate response with ocean heat uptake efficacy is greater than its correlation with equilibrium climate sensitivity in an ensemble of climate models used for the 3rd and 4th IPCC assessments. When computed at the time of doubling in the standard experiment with 1%/yr increase in CO2, the efficacy is variable amongst the models but is generally greater than 1, averages between 1.3 and 1.4, and is as large as 1.75 in several models.
Spatial variations in sea surface temperature (SST) and rainfall changes over the tropics are investigated based on ensemble simulations for the first half of the twenty-first century under the greenhouse gas (GHG) emission scenario A1B with coupled ocean–atmosphere general circulation models of the Geophysical Fluid Dynamics Laboratory (GFDL) and National Center for Atmospheric Research (NCAR). Despite a GHG increase that is nearly uniform in space, pronounced patterns emerge in both SST and precipitation. Regional differences in SST warming can be as large as the tropical-mean warming. Specifically, the tropical Pacific warming features a conspicuous maximum along the equator and a minimum in the southeast subtropics. The former is associated with westerly wind anomalies whereas the latter is linked to intensified southeast trade winds, suggestive of wind–evaporation–SST feedback. There is a tendency for a greater warming in the northern subtropics than in the southern subtropics in accordance with asymmetries in trade wind changes. Over the equatorial Indian Ocean, surface wind anomalies are easterly, the thermocline shoals, and the warming is reduced in the east, indicative of Bjerknes feedback. In the midlatitudes, ocean circulation changes generate narrow banded structures in SST warming. The warming is negatively correlated with wind speed change over the tropics and positively correlated with ocean heat transport change in the northern extratropics. A diagnostic method based on the ocean mixed layer heat budget is developed to investigate mechanisms for SST pattern formation.
Tropical precipitation changes are positively correlated with spatial deviations of SST warming from the tropical mean. In particular, the equatorial maximum in SST warming over the Pacific anchors a band of pronounced rainfall increase. The gross moist instability follows closely relative SST change as equatorial wave adjustments flatten upper-tropospheric warming. The comparison with atmospheric simulations in response to a spatially uniform SST warming illustrates the importance of SST patterns for rainfall change, an effect overlooked in current discussion of precipitation response to global warming. Implications for the global and regional response of tropical cyclones are discussed.
Zhang, Rong, Sarah M Kang, and Isaac M Held, January 2010: Sensitivity of climate change induced by the weakening of the Atlantic Meridional Overturning Circulation to cloud feedback. Journal of Climate, 23(2), doi:10.1175/2009JCLI3118.1. [ Abstract ]
A variety of observational and modeling studies show that changes in the Atlantic Meridional Overturning Circulation (AMOC) can induce rapid global scale climate change. In particular, a substantially weakened AMOC leads to a southward shift of the Intertropical Convergence Zone (ITCZ) in both the Atlantic and the Pacific. However, the simulated amplitudes of the AMOC induced tropical climate change differ substantially among different models. In this paper, we study the sensitivity to cloud feedback of the climate response to a change in the AMOC using a coupled ocean-atmosphere model (GFDL CM2.1). Without cloud feedback, the simulated AMOC-induced climate change in this model is weakened substantially. Low cloud feedback has a strong amplifying impact on the tropical ITCZ shift in this model, while the effects of high cloud feedback are weaker. We conclude that cloud feedback is an important contributor to the uncertainty in the global response to AMOC changes.
The Atlantic Meridional Overturning Circulation (AMOC) has an important influence on climate, and yet we lack adequate observations of this circulation. Here we assess the adequacy of past and current widely deployed routine observing systems for monitoring the AMOC and associated North Atlantic climate. To do so we draw on two independent simulations of the 20th century using an IPCC AR4 coupled climate model. We treat one simulation as “truth” and sample it according to the observing system we are evaluating. We then assimilate these synthetic “observations” into the second simulation within a fully-coupled system that instantaneously exchanges information among all coupled components and produces a nearly balanced and coherent estimate for global climate states including the North Atlantic climate system. The degree to which the assimilation recovers the “truth” is an assessment of the adequacy of the observing system being evaluated. As the coupled system responds to the constraint of the atmosphere or ocean, the assessment of the recovery for climate quantities such as Labrador Sea Water (LSW) and the North Atlantic Oscillation increases our understanding for the factors that determine AMOC variability. For example, we found the low-frequency sea-surface forcings provided by the atmospheric and sea-surface temperature observations can excite a LSW variation that governs the long time scale variability of the AMOC. When we use the most complete modern observing system consisting of atmospheric winds and temperature, along with Argo ocean temperature and salinity down to 2000 meters, a skill estimate of AMOC reconstruction is 90% (out of 100% maximum). Similarly encouraging results hold for other quantities, such as LSW. The past XBT observing system, in which deep ocean temperature and salinity were not available, has a lesser ability to recover the “truth” AMOC (the skill is reduced to 52%). While these results raise concerns about our ability to properly characterize past variations of the AMOC, they also hold promise for future monitoring of the AMOC and for initializing prediction models.
Zhang, Rong, August 2010: Latitudinal dependence of Atlantic Meridional Overturning Circulation (AMOC) variations. Geophysical Research Letters, 37, L16703, doi:10.1029/2010GL044474. [ Abstract ]
AMOC variations are often thought to propagate with the Kelvin wave speed, resulting in a short time lead between high and low latitudes AMOC variations. However as shown in this paper using a coupled climate model (GFDL CM2.1), with the existence of interior pathways of North Atlantic Deep Water (NADW) from Flemish Cap to Cape Hatteras as that observed recently, AMOC variations estimated in density space propagate with the advection speed in this region, resulting in a much longer time lead (several years) between subpolar and subtropical AMOC variations and providing a more useful predictability. The results suggest that AMOC variations have significant meridional coherence in density space, and monitoring AMOC variations in density space at higher latitudes might reveal a stronger signal with a several-year time lead.
Zhang, Rong, December 2010: Northward intensification of anthropogenically forced changes in the Atlantic meridional overturning circulation (AMOC). Geophysical Research Letters, 37, L24603, doi:10.1029/2010GL045054. [ Abstract ]
Extensive modeling studies show that changes in the anthropogenic forcing
due to increasing greenhouse gases might lead to a slowdown of the Atlantic
Meridional Overturning Circulation (AMOC) in the 21st century, but the AMOC
weakening estimated in most previous modeling studies is in depth space. Us-
ing a coupled ocean atmosphere model (GFDL CM2.1), this paper shows that
in density space, the anthropogenically forced AMOC changes over the 21st cen-
tury are intensified at northern high latitudes (nearly twice of those at lower lat-
itudes) due to changes in the North Atlantic Deep Water (NADW) formation.
In contrast, anthropogenically forced AMOC changes are much smaller in depth
space at the same northern high latitudes. Hence projecting AMOC changes in
depth space would lead to a significant underestimation of AMOC changes as-
sociated with changes in NADW formation. The result suggests that monitor-
ing AMOC change signal at northern high latitudes in density space might re-
veal a much stronger signal than that at lower latitudes. The simulated AMOC
changes in density space under anthropogenic forcing can not be distinguished
from that induced by natural AMOC variability for at least the first 20 years of
the 21st century, although the signal can be detected over a much longer period.
A “biased twin” experiment using two coupled general circulation models (CGCMs) that are biased with respect to each other is used to study the impact of deep ocean bias on ensemble ocean data assimilation. The “observations” drawn from one CGCM based on the Argo network are assimilated into the other. Traditional ensemble filtering can successfully recover the upper-ocean temperature and salinity of the target model but it usually fails to converge in the deep ocean where the model bias is large compared to the ocean’s intrinsic variability. The inconsistency between the well-constrained upper ocean and poorly constrained deep ocean generates spurious assimilation currents. An adaptively inflated ensemble filter is designed to enhance the consistency of upper- and deep-ocean adjustments, based on “climatological” standard deviations being adaptively updated by observations. The new algorithm reduces deep-ocean errors greatly, in particular, reducing current errors up to 70% and vertical motion errors up to 50%. Specifically, the tropical circulation is greatly improved with a better representation of the undercurrent, upwelling, and Western Boundary Current systems. The structure of the subtropical gyre is also substantially improved. Consequently, the new algorithm leads to better estimates of important global hydrographic features such as global overturning and pycnocline depth. Based on these improved estimates, decadal trends of basin-scale heat content and salinity as well as the seasonal–interannual variability of the tropical ocean are constructed coherently. Interestingly, the Indian Ocean (especially the north Indian Ocean), which is associated with stronger atmospheric feedbacks, is the most sensitive basin to the covariance formulation used in the assimilation. Also, while reconstruction of the local thermohaline structure plays a leading-order role in estimating the decadal trend of the Atlantic meridional overturning circulation (AMOC), more accurate estimates of the AMOC variability require coupled assimilation to produce coherently improved external forcings as well as internal heat and salt transport.
Retrospective predictions of seasonal hurricane activity in the Atlantic and
East Pacific are generated using an atmospheric model with 50km horizontal resolution and
simply persisting sea surface temperature (SST) anomalies from June through the hurricane
season. Using an ensemble of 5 realizations for each year between 1982 and 2008, the
correlations of the model mean with observations of basin-wide hurricane frequency are 0.69
in the North Atlantic and 0.58 in the East Pacific. In the North Atlantic, a significant part of the
degradation in skill as compared to a model forced with observed SSTs during the hurricane
season (correlation 0.78) can be explained by the change from June through the hurricane
season in one parameter, the difference between the SST in the main development region and
the tropical mean SST. In fact, simple linear regression models with this one predictor perform
nearly as well as the full dynamical model for basin-wide hurricane frequency in both the
East Pacific and the North Atlantic. The implication is that the quality of seasonal forecasts
based on a coupled atmosphere-ocean model will depend in large part on the model’s ability
to predict the evolution of this difference between main development region SST and tropical
mean SST.
Zheng, Xiao-Tong, Shang-Ping Xie, Gabriel A Vecchi, Q Liu, and J Hafner, March 2010: Indian Ocean dipole response to global warming: Analysis of ocean-atmospheric feedbacks in a coupled model. Journal of Climate, 23(5), doi:10.1175/2009JCLI3326.1. [ Abstract ]
Low-frequency modulation of and change under global warming in the Indian
Ocean Dipole (IOD) mode is investigated based on a pair of long simulations with a
coupled ocean-atmosphere general circulation model, one under constant climate forcing
and one forced by increasing greenhouse gas concentrations. In the unforced simulation,
IOD variance displays slow modulation significant in amplitude. It is found that the mean
thermocline depth in the eastern equatorial Indian Ocean (EEIO) is important for the slow
modulation, skewness and ENSO-correlation of IOD. IOD variance increases as the
EEIO thermocline shoals and thermocline feedback strengthens.
In the global warming simulation, the Walker circulation slows down with easterly
wind changes in the equatorial Indian Ocean. The thermocline shoals in the EEIO.
Thermocline feedback intensifies but surprisingly IOD variance does not. Zonal wind
anomalies associated with IOD are found to weaken, likely due to increased static
stability of the troposphere in global warming. Linear model experiments confirm this
stability effect to reduce circulation response to a sea surface temperature dipole. The
opposing changes in thermocline and atmospheric feedbacks result in little change in IOD
variance but the shoaling thermocline weakens IOD skewness. Little change under global
warming in IOD variance in the model suggests that the apparent intensification of IOD
activity for the recent decades is likely part of natural, chaotic modulation of the
ocean-atmosphere system.
The role of the penetration length scale of shortwave radiation into the surface ocean and its impact on tropical Pacific variability is investigated with a fully coupled ocean, atmosphere, land and ice model. Previous work has shown that removal of all ocean color results in a system that tends strongly towards an El Niño state. Results from a suite of surface chlorophyll perturbation experiments show that the mean state and variability of the tropical Pacific is highly sensitive to the concentration and distribution of ocean chlorophyll. Setting the near-oligotrophic regions to contain optically pure water warms the mean state and suppresses variability in the western tropical Pacific. Doing the same above the shadow zones of the tropical Pacific also warms the mean state but enhances the variability. It is shown that increasing penetration can both deepen the pycnocline (which tends to damp El Niño) while shifting the mean circulation so that the wind response to temperature changes is altered. Depending on what region is involved this change in the wind stress can either strengthen or weaken ENSO variability.
Chang, You-Soon, Anthony Rosati, Shaoqing Zhang, and Matthew J Harrison, February 2009: Objective analysis of monthly temperature and salinity for the world ocean in the 21st century: Comparison with World Ocean Atlas and application to assimilation validation. Journal of Geophysical Research, 114, C02014, doi:10.1029/2008JC004970. [ Abstract ]
A new World Ocean atlas of monthly temperature
and salinity, based on individual profiles for 2003–2007 (WOA21c), is
constructed and compared with the World Ocean Atlas 2001 (WOA01), the
World Ocean Atlas 2005 (WOA05), and the data assimilation analysis
from the Coupled Data Assimilation (CDA) system developed by the Geophysical
Fluid Dynamics Laboratory (GFDL). First, we established a global data
management system for quality control (QC) of oceanic observed data both in
real time and delayed mode. Delayed mode QC of Argo floats identified about
8.5% (3%) of the total floats (profiles) up to December 2007 as having a
significant salinity offset of more than 0.05. Second, all QCed data were
gridded at 1° by 1° horizontal resolution and 23 standard depth levels using
six spatial scales (large and small longitudinal, latitudinal, and cross-isobath)
and a temporal scale. Analyzed mean temperature in WOA21c is warm with
respect to WOA01 and WOA05, while salinity difference is less evident.
Consistent differences among WOA01, WOA05, and WOA21c are found both in the
fully and subsampled data set, which indicates a large impact of recent
observations on the existing climatologies. Root mean square temperature and
salinity differences and offsets of the GFDL's CDA results significantly
decrease in the order of WOA01, WOA05, and WOA21c in most oceans and depths
as well. This result suggests that the WOA21c is of use for the collocated
assessment approach especially for high-performance assimilation models on
the global scale.
230Th-dated oxygen isotope records of stalagmites from Sanbao Cave, China, characterize Asian Monsoon (AM) precipitation through the ends of the third- and fourthmost recent ice ages. As a result, AM records for the past four glacial terminations can now be precisely correlated with those from ice cores and marine sediments, establishing the timing and sequence of major events. In all four cases, observations are consistent with a classic Northern Hemisphere summer insolation intensity trigger for an initial retreat of northern ice sheets. Meltwater and icebergs entering the North Atlantic alter oceanic and atmospheric circulation and associated fluxes of heat and carbon, causing increases in atmospheric CO2 and Antarctic temperatures that drive the termination in the Southern Hemisphere. Increasing CO2 and summer insolation drive recession of northern ice sheets, with probable positive feedbacks between sea level and CO2.
The climate response of the equatorial Pacific to increased greenhouse gases is investigated using numerical experiments from 11 climate models participating in the Intergovernmental Panel on Climate Change’s Fourth Assessment Report. Multimodel mean climate responses to CO2 doubling are identified and related to changes in the heat budget of the surface layer. Weaker ocean surface currents driven by a slowing down of the Walker circulation reduce ocean dynamical cooling throughout the equatorial Pacific. The combined anomalous ocean dynamical plus radiative heating from CO2 is balanced by different processes in the western and eastern basins: Cloud cover feedbacks and evaporation balance the heating over the warm pool, while increased cooling by ocean vertical heat transport balances the warming over the cold tongue. This increased cooling by vertical ocean heat transport arises from increased near-surface thermal stratification, despite a reduction in vertical velocity. The stratification response is found to be a permanent feature of the equilibrium climate potentially linked to both thermodynamical and dynamical changes within the equatorial Pacific. Briefly stated, ocean dynamical changes act to reduce (enhance) the net heating in the east (west). This explains why the models simulate enhanced equatorial warming, rather than El Niño–like warming, in response to a weaker Walker circulation. To conclude, the implications for detecting these signals in the modern observational record are discussed.
Erukhimova, T, Rong Zhang, and K P Bowman, February 2009: The climatological mean atmospheric transport under weakened Atlantic thermohaline circulation climate scenario. Climate Dynamics, 32(2-3), doi:10.1007/s00382-008-0402-x. [ Abstract ]
Global atmospheric transport in a climate subject to a substantial weakening of the Atlantic thermohaline circulation (THC) is studied by using climatological Green’s functions of the mass conservation equation for a conserved, passive tracer. Two sets of Green’s functions for the perturbed climate and for the present climate are evaluated from 11-year atmospheric trajectory calculations, based on 3-D winds simulated by GFDL’s newly developed global coupled ocean–atmosphere model (CM2.1). The Green’s function analysis reveals pronounced effects of the climate change on the atmospheric transport, including seasonally modified Hadley circulation with a stronger Northern Hemisphere cell in DJF and a weaker Southern Hemisphere cell in JJA. A weakened THC is also found to enhance mass exchange rates through mixing barriers between the tropics and the two extratropical zones. The response in the tropics is not zonally symmetric. The 3-D Green’s function analysis of the effect of THC weakening on transport in the tropical Pacific shows a modified Hadley cell in the eastern Pacific, confirming the results of our previous studies, and a weakening (strengthening) of the upward and eastward motion to the south (north) of the Equator in the western Pacific in the perturbed climate as compared to the present climate.
Findell, Kirsten L., A J Pitman, Matthew H England, and P J Pegion, June 2009: Regional and global impacts of land cover change and sea surface temperature anomalies. Journal of Climate, 22(12), doi:10.1175/2008JCLI2580.1. [ Abstract ]
The atmospheric and land components of the Geophysical Fluid Dynamics Laboratory’s climate model CM2.1 is used with climatological sea surface temperatures (SSTs) to investigate the relative climatic impacts of historical anthropogenic land cover change (LCC) and realistic SST anomalies. The SST forcing anomalies used are analogous to signals induced by an El Nino-Southern Oscillation (ENSO), a North Atlantic Oscillation (NAO), and the background trend. Coherent areas of LCC are represented throughout much of central and eastern Europe, northern India, southeastern China, and on either side of the ridge of the Appalachian Mountains in North America. Smaller areas of change are present in various tropical regions. The land cover changes in the model are almost exclusively a conversion of forests to grasslands.
Model results show that LCC has a negligible impact on the global scale, while the SST anomalies—particularly the ENSO-like signal—have a statistically significant global impact. However, in the regions where the land surface has been altered, the impact of LCC can be equally or more important than the SST forcing patterns in determining the seasonal cycle of the surface water and energy balance. LCC also perturbs the local air temperature and rainfall at a similar level of statistical significance as the SST anomalies. This suggests that proper representation of land cover conditions is essential in the design of climate model experiments, particularly if results are to be used for regional-scale assessments of climate change impacts.
Atlantic tropical cyclone activity has trended upward in recent decades. The increase coincides with favorable changes in local sea surface temperature and other environmental indices, principally associated with vertical shear and the thermodynamic profile. The relative importance of these environmental factors has not been firmly established. A recent study using a high-resolution dynamical downscaling model has captured both the trend and interannual variations in Atlantic storm frequency with considerable fidelity. In the present work, this downscaling framework is used to assess the importance of the large-scale thermodynamic environment relative to other factors influencing Atlantic tropical storms.
Separate assessments are done for the recent multidecadal trend (1980–2006) and a model-projected global warming environment for the late 21st century. For the multidecadal trend, changes in the seasonal-mean thermodynamic environment (sea surface temperature and atmospheric temperature profile at fixed relative humidity) account for more than half of the observed increase in tropical cyclone frequency, with other seasonal-mean changes (including vertical shear) having a somewhat smaller combined effect. In contrast, the model’s projected reduction in Atlantic tropical cyclone activity in the warm climate scenario appears to be driven mostly by increased seasonal-mean vertical shear in the western Atlantic and Caribbean rather than by changes in the SST and thermodynamic profile.
Gnanadesikan, Anand, and Whit G Anderson, February 2009: Ocean water clarity and the ocean general circulation in a coupled climate model. Journal of Physical Oceanography, 39(2), doi:10.1175/2008JPO3935.1. [ Abstract ]
Ocean water clarity affects the
distribution of shortwave heating in the water column. In a one-dimensional
time-mean sense, increased clarity would be expected to cool the surface and
heat subsurface depths as shortwave radiation penetrates deeper into the
water column. However, wind-driven upwelling, boundary currents, and the
seasonal cycle of mixing can bring water heated at depth back to the
surface. This warms the equator and cools the subtropics throughout the year
while reducing the amplitude of the seasonal cycle of temperature in polar
regions. This paper examines how these changes propagate through the climate
system in a coupled model with an isopycnal ocean component focusing on the
different impacts associated with removing shading from different regions.
Increasing shortwave penetration along the equator causes warming to the
south of the equator. Increasing it in the relatively clear gyres off the
equator causes the Hadley cells to strengthen and the subtropical gyres to
shift equatorward. Increasing shortwave penetration in the less clear
regions overlying the oxygen minimum zones causes the cold tongue to warm
and the Walker circulation to weaken. Increasing shortwave penetration in
the high-latitude Southern Ocean causes an increase in the formation of mode
water from subtropical water. The results suggest that more attention be
paid to the processes distributing heat below the mixed layer.
Coordinated Ocean-ice Reference Experiments (COREs) are presented as a tool to explore the behaviour of global ocean-ice models under forcing from a common atmospheric dataset. We highlight issues arising when designing coupled global ocean and sea ice experiments, such as difficulties formulating a consistent forcing methodology and experimental protocol. Particular focus is given to the hydrological forcing, the details of which are key to realizing simulations with stable meridional overturning circulations.
The atmospheric forcing from [Large, W., Yeager, S., 2004. Diurnal to decadal global forcing for ocean and sea-ice models: the data sets and flux climatologies. NCAR Technical Note: NCAR/TN-460+STR. CGD Division of the National Center for Atmospheric Research] was developed for coupled-ocean and sea ice models. We found it to be suitable for our purposes, even though its evaluation originally focussed more on the ocean than on the sea-ice. Simulations with this atmospheric forcing are presented from seven global ocean-ice models using the CORE-I design (repeating annual cycle of atmospheric forcing for 500 years). These simulations test the hypothesis that global ocean-ice models run under the same atmospheric state produce qualitatively similar simulations. The validity of this hypothesis is shown to depend on the chosen diagnostic. The CORE simulations provide feedback to the fidelity of the atmospheric forcing and model configuration, with identification of biases promoting avenues for forcing dataset and/or model development.
Guilyardi, Eric, Andrew T Wittenberg, Alexey Fedorov, Matthew Collins, C Wang, Antonietta Capotondi, G J van Oldenborgh, and T N Stockdale, March 2009: Understanding El Niño in ocean–atmosphere general circulation models: Progress and challenges. Bulletin of the American Meteorological Society, 90(3), doi:10.1175/2008BAMS2387.1. [ Abstract ]
Determining how El Niño and its impacts may change over the next 10 to 100 years remains a difficult scientific challenge. Ocean–atmosphere coupled general circulation models (CGCMs) are routinely used both to analyze El Niño mechanisms and teleconnections and to predict its evolution on a broad range of time scales, from seasonal to centennial. The ability to simulate El Niño as an emergent property of these models has largely improved over the last few years. Nevertheless, the diversity of model simulations of present-day El Niño indicates current limitations in our ability to model this climate phenomenon and to anticipate changes in its characteristics. A review of the several factors that contribute to this diversity, as well as potential means to improve the simulation of El Niño, is presented.
Harrison, D E., A M Chiodi, and Gabriel A Vecchi, November 2009: Effects of surface forcing on the seasonal cycle of the eastern equatorial Pacific. Journal of Marine Research, 67(6), 701-729. [ AbstractPDF ]
The roles of zonal and meridional wind stress and of surface heat flux in the seasonal cycle of sea surface temperature (SST) are examined with a primitive equation (PE) model of the tropical Pacific Ocean. While a variety of previous numerical and observational studies have examined the seasonal cycle of SST in the eastern tropical Pacific, it is noteworthy that different mechanisms have been invoked as primary in each case and different conclusions have been reached regarding the relative importance of the various components of surface forcing. Here, we perform a series of numerical experiments in which different components of the surface forcing are eliminated and the resulting upper ocean variability is compared with that of the climatological experiment. The model used for these experiments reproduces a realistic climatological seasonal cycle, in which SST emerges as an independent quantity. We find that the different cases all produce qualitatively reasonable seasonal cycles of SST, though only the most complete model is also able to reproduce the seasonal cycle of near surface currents, tropical instability waves (TIWs), and net surface heat fluxes consistent with historical observations. These results indicate that simply reproducing a qualitatively accurate seasonal cycle of SST does not necessarily allow meaningful conclusions to be made about the relative importance of the different components of surface forcing. The results described here also suggest that a model simulation must at least reproduce all the documented near surface kinematic features of the equatorial Pacific cold tongue region reasonably well, before accurate inferences can be made from model experiments. This provides useful guidelines to current efforts to develop and evaluate more complex fully coupled air-sea models and shows that results for simple or intermediate ocean models that do not have this level of fidelity to the observations will be difficult to interpret.
Hurrell, J W., Gerald A Meehl, David Bader, Thomas L Delworth, Ben P Kirtman, and B A Wielicki, December 2009: A unified modeling approach to climate system prediction. Bulletin of the American Meteorological Society, 90(12), doi:10.1175/2009BAMS2752.1. [ Abstract ]
There is a new perspective of a continuum of prediction problems, with a blurring of the distinction between short-term predictions and long-term climate projections. At the heart of this new perspective is the realization that all climate system predictions, regardless of time scale, share common processes and mechanisms; moreover, interactions across time and space scales are fundamental to the climate system itself. Further, just as seasonal-to-interannual predictions start from an estimate of the state of the climate system, there is a growing realization that decadal and longer-term climate predictions could be initialized with estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface. Even though the prediction problem itself is seamless, the best practical approach to it may be described as unified: models aimed at different time scales and phenomena may have large commonality but place emphasis on different aspects of the system. The potential benefits of this commonality are significant and include improved predictions on all time scales and stronger collaboration and shared knowledge, infrastructure, and technical capabilities among those in the weather and climate prediction communities.
Kim, D, and William F Stern, et al., December 2009: Application of MJO simulation diagnostics to climate models. Journal of Climate, 22(23), doi:10.1175/2009JCLI3063.1. [ Abstract ]
The ability of eight climate models to simulate the Madden–Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November–April) behavior. Initially, maps of the mean state and variance and equatorial space–time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space–time spectral peak in the zonal wind compared to that of precipitation. Using the phase–space representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (20–29 days) than observations (31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30–80-day band.
Several key variables associated with the model’s MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.
Changes in continental water stores, largely human-induced, affect sea level. Better hydrological models and observations could clarify the land's role in sea-level variations.
McPhaden, Michael J., and Gabriel A Vecchi, et al., February 2009: Ocean-atmosphere interactions during cyclone Nargis. EOS, 90(7), 53-60. [ PDF ]
A new field of study, “decadal prediction,” is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10–30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving three-dimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.
Schubert, S D., Thomas L Delworth, and Kirsten L Findell, et al., October 2009: A US CLIVAR project to assess and compare the responses of global climate models to drought-related SST forcing patterns: Overview and results. Journal of Climate, 22(19), doi:10.1175/2009JCLI3060.1. [ Abstract ]
The U.S. Climate Variability and Predictability (CLIVAR) working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land–atmosphere feedbacks on regional drought. The runs were carried out with five different atmospheric general circulation models (AGCMs) and one coupled atmosphere–ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Niño–Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic multidecadal oscillation (AMO), and a global trend pattern.
One of the key findings is that all of the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the United States tends to occur when the two oceans have anomalies of opposite signs. Further highlights of the response over the United States to the Pacific forcing include precipitation signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. The response to the positive SST trend forcing pattern is an overall surface warming over the world’s land areas, with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all of the models.
It is hoped that these early results, as well as those reported in the other contributions to this special issue on drought, will serve to stimulate further analysis of these simulations, as well as suggest new research on the physical mechanisms contributing to hydroclimatic variability and change throughout the world.
We have developed a dynamic land model (LM3V) able to simulate ecosystem dynamics and exchanges of water, energy, and CO2 between land and atmosphere. LM3V is specifically designed to address the consequences of land use and land management changes including cropland and pasture dynamics, shifting cultivation, logging, fire, and resulting patterns of secondary regrowth. Here we analyze the behavior of LM3V, forced with the output from the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model AM2, observed precipitation data, and four historic scenarios of land use change for 1700–2000. Our analysis suggests a net terrestrial carbon source due to land use activities from 1.1 to 1.3 GtC/a during the 1990s, where the range is due to the difference in the historic cropland distribution. This magnitude is substantially smaller than previous estimates from other models, largely due to our estimates of a secondary vegetation sink of 0.35 to 0.6 GtC/a in the 1990s and decelerating agricultural land clearing since the 1960s. For the 1990s, our estimates for the pastures' carbon flux vary from a source of 0.37 to a sink of 0.15 GtC/a, and for the croplands our model shows a carbon source of 0.6 to 0.9 GtC/a. Our process-based model suggests a smaller net deforestation source than earlier bookkeeping models because it accounts for decelerated net conversion of primary forest to agriculture and for stronger secondary vegetation regrowth in tropical regions. The overall uncertainty is likely to be higher than the range reported here because of uncertainty in the biomass recovery under changing ambient conditions, including atmospheric CO2 concentration, nutrients availability, and climate.
Sulfate aerosols resulting from strong volcanic explosions last for 2–3 years in the lower stratosphere. Therefore it was traditionally believed that volcanic impacts produce mainly short-term, transient climate perturbations. However, the ocean integrates volcanic radiative cooling and responds over a wide range of time scales. The associated processes, especially ocean heat uptake, play a key role in ongoing climate change. However, they are not well constrained by observations, and attempts to simulate them in current climate models used for climate predictions yield a range of uncertainty. Volcanic impacts on the ocean provide an independent means of assessing these processes. This study focuses on quantification of the seasonal to multidecadal time scale response of the ocean to explosive volcanism. It employs the coupled climate model CM2.1, developed recently at the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory, to simulate the response to the 1991 Pinatubo and the 1815 Tambora eruptions, which were the largest in the 20th and 19th centuries, respectively. The simulated climate perturbations compare well with available observations for the Pinatubo period. The stronger Tambora forcing produces responses with higher signal-to-noise ratio. Volcanic cooling tends to strengthen the Atlantic meridional overturning circulation. Sea ice extent appears to be sensitive to volcanic forcing, especially during the warm season. Because of the extremely long relaxation time of ocean subsurface temperature and sea level, the perturbations caused by the Tambora eruption could have lasted well into the 20th century.nd sea level, the perturbations caused by the Tambora eruption could last well into the 20th century.
Sukharev, J, C Wang, K-L Ma, and Andrew T Wittenberg, April 2009: Correlation study of time-varying multivariate climate data sets In Proc. of IEEE VGTC Pacific Visualization Symposium 2009, Beijing, China, IEEE, . [ AbstractPDF ]
We present a correlation study of time-varying multivariate volumetric data sets. In most scientific disciplines, to test hypotheses and discover insights, scientists are interested in looking for connections among different variables, or among different spatial locations within a data field. In response, we propose a suite of techniques to analyze the correlations in time-varying multivariate data. Various temporal curves are utilized to organize the data and capture the temporal behaviors. To reveal patterns and find connections, we perform data clustering and segmentation using the kmeans clustering and graph partitioning algorithms. We study the correlation structure of a single or a pair of variables using pointwise correlation coefficients and canonical correlation analysis. We demonstrate our approach using results on time-varying multivariate climate data sets.
The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate
phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and
as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the
MJO in our climate and weather systems, current global circulation models (GCMs) exhibit considerable
shortcomings in representing this phenomenon. These shortcomings have been documented in a number of
multimodel comparison studies over the last decade. However, diagnosis of model performance has been
challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized
set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability
(CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively
evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is
reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their
calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of
variance and correlation, to more sophisticated space–time spectral and empirical orthogonal function
analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to
identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.
Wan, X, P Chang, R Saravanan, Rong Zhang, and M Schmidt, January 2009: On the interpretation of Caribbean paleo-temperature reconstructions during the Younger Dryas. Geophysical Research Letters, 36, L02701, doi:10.1029/2008GL035805. [ Abstract ]
A conundrum exists regarding whether the sea-surface temperatures decreased or increased over the southern Caribbean and the western Tropical Atlantic region during the Younger Dryas when the North Atlantic cooled substantially and the Atlantic thermohaline circulation was weakened significantly. Despite the proximity of core locations, some proxy reconstructions record a surface cooling, while others indicate a warming. We suggest that this seemingly paradoxical finding may, at least partially, be attributed to the competing physical processes that result in opposing signs of temperature change in the region in response to weakened North Atlantic meridional overturning circulation. Our coupled ocean-atmosphere model experiments indicate that the temperature response over the southern Caribbean and Western Tropical Atlantic regions is complex and can vary considerably in small spatial scales, depending on the nature of physical processes that dominate.
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 onetier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Ni ńo 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Nińo 3.4 SST variation. The forecasts for 2 m air temperature are better in El Nińo years than in La Nińa years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the onetier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.
A control simulation of the GFDL CM2.1 global coupled GCM, run for 2000 years with its atmospheric composition, solar irradiance, and land cover held fixed at 1860 values, exhibits strong interdecadal and intercentennial modulation of its ENSO behavior. To the extent that such modulation is realistic, it could attach large uncertainties to ENSO metrics diagnosed from centennial and shorter records – with important implications for historical and paleo records, climate projections, and model assessment and intercomparison. Analysis of the wait times between ENSO warm events suggests that such slow modulation need not require multidecadal memory; it can arise simply from Poisson statistics applied to ENSO's interannual time scale and seasonal phase-locking.
The impact of oceanic observing systems, external radiative forcings due to greenhouse gas and natural aerosol (GHGNA), and oceanic initial conditions on long time variability of oceanic heat content and salinity is assessed by the assimilation of oceanic “observations” in the context of a “perfect” Intergovernmental Panel on Climate Change Fourth Assessment Report model. According to times and locations at which observations are available, the 20th century expendable bathythermograph (XBT) temperature and 21st century Argo temperature and salinity observations are drawn from a model simulation (set as the “truth”) with historical GHGNA radiative forcings. These model observations are assimilated into another coupled model simulation based on temporally varying or fixed year GHGNA values and different oceanic initial conditions. The degree to which the assimilation recovers the truth variability of oceanic heat content and salinity is an assessment of the impact of each factor on the detection of the oceanic “climate.” Results show that both the 20th century XBT and 21st century Argo observations adequately capture the basin-scale variability of heat content. The Argo salinity observations appear to be necessary to reproduce the North Atlantic thermohaline structure and variability. The addition of historical radiative forcings does not make a significant contribution to the detection skill. The initial conditions spun up by historical GHGNA produce better detection skill than the initial conditions spun up by preindustrial fixed year GHGNA due to reduced assimilation shocks. While the 20th century XBT temperature observations alone capture some basic features of salinity variations of the tropical ocean due to the strong T-S relationship from tropical air-sea interactions, the Argo salinity observations are important for global state estimation, particularly in high latitudes where haline effects on ocean density are greater.
Zhang, Rong, and Thomas L Delworth, March 2009: A new method for attributing climate variations over the Atlantic Hurricane Basin's main development region. Geophysical Research Letters, 36, L06701, doi:10.1029/2009GL037260. [ Abstract ]
We propose a new approach to
decompose observed climate variations over the Atlantic Hurricane Basin's
main development region (MDR) into components attributable to radiative
forcing changes and to internal oceanic variability. Our attribution
suggests that the observed multidecadal anomalies of vertical shear (Uz) and
a simple index of maximum potential intensity (SIMPI) for tropical cyclones
are both dominated by internal variability, consistent with multidecadal
variations of Atlantic Hurricane activity; changes in radiative forcing led
to increasing Uz and decreasing SIMPI since the late 50's, unfavorable for
Atlantic Hurricane activity. Physically, at least for the GFDL model, sea
surface temperature (SST) anomalies induced by ocean heat transport
variations are more efficient in producing negative Uz anomalies than that
induced by altered radiative forcing.
A global atmospheric model with roughly 50 km horizontal grid spacing is used to simulate the interannual variability of tropical cyclones using observed sea surface temperatures (SSTs) as the lower boundary condition. The model's convective parameterization is based on a closure for shallow convection, with much of the deep convection allowed to occur on resolved scales. Four realizations of the period 1981–2005 are generated. The correlation of yearly Atlantic hurricane counts with observations is greater than 0.8 when the model is averaged over the four realizations, supporting the view that the random part of this annual Atlantic hurricane frequency (the part not predictable given the SSTs) is relatively small (< 2 hurricanes/yr). Correlations with observations are lower in the East, West and South Pacific (roughly 0.6, 0.5 and 0.3) and insignificant in the Indian ocean. The model trends in Northern Hemisphere basin-wide frequency are consistent with the observed trends in the IBTrACS database. The model generates an upward trend of hurricane frequency in the Atlantic and downward trends in the East and West Pacific over this time frame. The model produces a negative trend in the Southern Hemisphere that is larger than that in the IBTrACS.
The same model is used to simulate the response to the SST anomalies generated by coupled models in the CMIP3 archive, using the late 21st century in the A1B scenario. Results are presented for SST anomalies computed by averaging over 18 CMIP3 models and from individual realizations from three models. A modest reduction of global and Southern Hemisphere hurricane frequency is obtained in each case, but the results in individual Northern Hemisphere basins differ among the models. The vertical shear in the Atlantic Main Development Region (MDR) and the difference between the MDR SST and the tropical mean SST are well correlated with the model's Atlantic storm frequency, both for interannual variability and for the intermodel spread in global warming projections.
Barreiro, M, Alexey Fedorov, Ronald C Pacanowski, and S G H Philander, 2008: Abrupt climate changes: How freshening of the northern Atlantic affects the thermohaline & wind-driven oceanic circulations. Annual Review of Earth and Planetary Sciences, 36, 33-58. [ Abstract ]
Leading hypotheses for abrupt climate changes are focused on the ocean response to a freshening of surface waters in the north Atlantic. The degree to which such a freshening affects the deep, slow thermohaline, rather than the shallow, swift, wind-driven circulations of the ocean, and hence the degree to which that freshening affects climate in high rather than low latitudes, differ from model to model, depending on factors such as the treatment of diffusive processes in the oceans. Many comprehensive climate models are biased and confine the influence mainly to the thermohaline circulation and northern climates. Simulations of paleoclimates can provide valuable tests for the models, but only some of those climates provide sufficiently stringent tests to determine which models are realistic.
Chang, P, Rong Zhang, W Hazeleger, C. Wen, X Wan, L Ji, R J Haarsma, W-P Breugem, and H. Seidel, 2008: Oceanic link between abrupt changes in the North Atlantic Ocean and the African monsoon. Nature Geoscience, 1(7), doi:10.1038/ngeo218. [ Abstract ]
Abrupt changes in the African monsoon can have pronounced socioeconomic impacts on many West African countries. Evidence for both prolonged humid periods and monsoon failures have been identified throughout the late Pleistocene and early Holocene epochs1, 2. In particular, drought conditions in West Africa have occurred during periods of reduced North Atlantic thermohaline circulation, such as the Younger Dryas cold event1. Here, we use an ocean–atmosphere general circulation model to examine the link between oceanographic changes in the North Atlantic Ocean and changes in the strength of the African monsoon. Our simulations show that when North Atlantic thermohaline circulation is substantially weakened, the flow of the subsurface North Brazil Current reverses. This leads to decreased upper tropical ocean stratification and warmer sea surface temperatures in the equatorial South Atlantic Ocean, and consequently reduces African summer monsoonal winds and rainfall over West Africa. This mechanism is in agreement with reconstructions of past climate. We therefore suggest that the interaction between thermohaline circulation in the North Atlantic Ocean and wind-driven currents in the tropical Atlantic Ocean contributes to the rapidity of African monsoon transitions during abrupt climate change events.
Chen, C-T, and Thomas R Knutson, 2008: On the verification and comparison of extreme rainfall indices from climate models. Journal of Climate, 21(7), doi:10.1175/2007JCLI1494.1. [ Abstract ]
The
interpretation of model precipitation output (e.g., as a gridpoint estimate
versus as an areal mean) has a large impact on the evaluation and comparison
of simulated daily extreme rainfall indices from climate models. It is first
argued that interpretation as a gridpoint estimate (i.e., corresponding to
station data) is incorrect. The impacts of this interpretation versus the
areal mean interpretation in the context of rainfall extremes are then
illustrated. A high-resolution (0.25° × 0.25° grid) daily observed
precipitation dataset for the United States [from Climate Prediction Center
(CPC)] is used as idealized perfect model gridded data. Both 30-yr return
levels of daily precipitation (P30) and a simple daily
intensity index are substantially reduced in these data when estimated at
coarser resolution compared to the estimation at finer resolution. The
reduction of P30 averaged over the conterminous United
States is about 9%, 15%, 28%, 33%, and 43% when the data were first
interpolated to 0.5° × 0.5°, 1° × 1°, 2° × 2°, 3° × 3°, and 4° × 4° grid
boxes, respectively, before the calculation of extremes. The differences
resulting from the point estimate versus areal mean interpretation are
sensitive to both the data grid size and to the particular extreme rainfall
index analyzed. The differences are not as sensitive to the magnitude and
regional distribution of the indices. Almost all Intergovernmental Panel on
Climate Change (IPCC) Fourth Assessment Report (AR4) models underestimate
U.S. mean P30 if it is compared directly with P30
estimated from the high-resolution CPC daily rainfall observation. On the
other hand, if CPC daily data are first interpolated to various model
resolutions before calculating the P30 (a more correct
procedure in our view), about half of the models show good agreement with
observations while most of the remaining models tend to overestimate the
mean intensity of heavy rainfall events. A further implication of
interpreting model precipitation output as an areal mean is that use of
either simple multimodel ensemble averages of extreme rainfall or of
intermodel variability measures of extreme rainfall to assess the common
characteristics and range of uncertainties in current climate models is not
appropriate if simulated extreme rainfall is analyzed at a model’s native
resolution. Owing to the large sensitivity to the assumption used, the
authors recommend that for analysis of precipitation extremes, investigators
interpret model precipitation output as an area average as opposed to a
point estimate and then ensure that various analysis steps remain consistent
with that interpretation.
Clark, P U., Thomas L Delworth, and A J Weaver, April 2008: Freshwater Forcing:Will History Repeat Itself?Science, 320(5874), 316.
Previous work has suggested that the strength and latitudinal position of the Southern Hemisphere (SH) mid-latitude westerly winds has an important impact on climate and the Atlantic Meridional Overturning Circulation (AMOC). We probe this hypothesis by conducting ensembles of experiments using the GFDL CM2.1 coupled ocean-atmosphere model with altered SH wind stress. We find, consistent with previous work, that enhanced (reduced) and poleward (equatorward) displaced SH westerly winds lead to an AMOC intensification (weakening). While the AMOC takes more than a century to respond fully to the altered SH winds, initial effects in the North Atlantic can occur within a few decades. The AMOC changes generate SST and surface air temperature responses in the North Atlantic and adjacent continental regions. In the Southern Hemisphere, the atmosphere responds to the altered ocean circulation with a further strengthening and poleward movement of the SH winds, thereby constituting a modest positive feedback.
Delworth, Thomas L., and Rong Zhang, et al., December 2008: The potential for abrupt change in the Atlantic Meridional Overturning Circulation In Abrupt Climate Change: Final Report, Synthesis & Assessment Product 3.4, CSSP, Reston, VA, U.S. Geological Survey, 258-359. [ PDF ]
Gutowski, W J., Thomas R Knutson, and Ronald J Stouffer, et al., 2008: Causes of observed changes in extremes and projections of future changes In Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands. T.R. Karl, G.A. Meehl, C.D. Miller, S.J. Hassol, A.M. Waple, and W.L. Murray (eds.), Washington, DC, Department of Commerce/NCDC, 81-116. [ PDF ]
Kim, D, Jong-Seong Kug, I-S Kang, Fei-Fei Jin, and Andrew T Wittenberg, 2008: Tropical Pacific impacts of convective momentum transport in the SNU coupled GCM. Climate Dynamics, 31(2-3), doi:10.1007/s00382-007-0348-4. [ Abstract ]
Impacts of convective momentum transport
(CMT) on tropical Pacific climate are examined, using an
atmospheric (AGCM) and coupled GCM (CGCM) from
Seoul National University. The CMT scheme affects the
surface mainly via a convection-compensating atmospheric
subsidence which conveys momentum downward through
most of the troposphere. AGCM simulations—with SSTs
prescribed from climatological and El Nino Southern
Oscillation (ENSO) conditions—show substantial changes
in circulation when CMT is added, such as an eastward
shift of the climatological trade winds and west Pacific
convection. The CMT also alters the ENSO wind anomalies
by shifting them eastward and widening them
meridionally, despite only subtle changes in the precipitation anomaly patterns. During ENSO, CMT affects the low-level winds mainly via the anomalous convection
acting on the climatological westerly wind shear over the
central Pacific—so that an eastward shift of convection
transfers more westerly momentum toward the surface than
would occur without CMT. By altering the low-level
circulation, the CMT further alters the precipitation, which
in turn feeds back on the CMT. In the CGCM, CMT affects
the simulated climatology by shifting the mean convection
and trade winds eastward and warming the equatorial SST;
the ENSO period and amplitude also increase. In contrast
to the AGCM simulations, CMT substantially alters the El
Nino precipitation anomaly patterns in the CGCM. Also
discussed are possible impacts of the CMT-induced changes in climatology on the simulated ENSO.
Knutson, Thomas R., and Robert E Tuleya, May 2008: Tropical cyclones and climate change: Revisiting recent studies at GFDL In Climate Extremes and Society, Diaz, H.F. and R.J. Murnane, Eds., New York, NY, Cambridge University Press, 120-144. [ Abstract ]
In this chapter, we revisit two recent studies performed at the Geophysical Fluid Dynamics Laboratory (GFDL), with a focus on issues relevant to tropical cyclones and climate change. The first study was a model-based assessment of twentieth-century regional surface temperature trends. The tropical Atlantic Main Development Region (MDR) for hurricane activity was found to have warmed by several tenths of a degree Celsius over the twentieth century. Coupled model historical simulations using current best estimates of radiative forcing suggest that the century-scale warming trend in the MDR may contain a significant contribution from anthropogenic forcing, including increases in atmospheric greenhouse gas concentrations. The results further suggest that the low-frequency variability in the MDR, apart from the trend, may contain substantial contributions from both radiative forcing (natural and anthropogenic) and internally generated climate variability. The second study used the GFDL huyrricane model, in an idealized setting, to simulate the impact of a pronounced CO2-induced warming on hurricane intensities and precipitation. A 1.75°C warming increases the intensities of hurricanes in the model by 5.8% in terms of surface wind speeds, 14% in terms of central pressure fall, or about one half category on the Saffir-Simpson Hurricane Scale. A revised storm-core accumulated (six-hour) rainfall measure shows a 21.6% increase under high CO2 conditions. Our simulated storm intensities are substantially less sensitive to sea surface temperature (SST) changes than recently reported historical observational trends are - a difference we are not able to completely reconcile at this time.
Increasing sea surface temperatures in the tropical Atlantic Ocean and measures of Atlantic hurricane activity have been reported to be strongly correlated since at least 1950 (refs 1, 2, 3, 4, 5), raising concerns that future greenhouse-gas-induced warming6 could lead to pronounced increases in hurricane activity. Models that explicitly simulate hurricanes are needed to study the influence of warming ocean temperatures on Atlantic hurricane activity, complementing empirical approaches. Our regional climate model of the Atlantic basin reproduces the observed rise in hurricane counts between 1980 and 2006, along with much of the interannual variability, when forced with observed sea surface temperatures and atmospheric conditions7. Here we assess, in our model system7, the changes in large-scale climate that are projected to occur by the end of the twenty-first century by an ensemble of global climate models8, and find that Atlantic hurricane and tropical storm frequencies are reduced. At the same time, near-storm rainfall rates increase substantially. Our results do not support the notion of large increasing trends in either tropical storm or hurricane frequency driven by increases in atmospheric greenhouse-gas concentrations.
Kunkel, Kenneth E., and Thomas R Knutson, et al., 2008: Observed changes in weather and climate extremes In Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands. T.R. Karl, G.A. Meehl, C.D. Miller, S.J. Hassol, A.M. Waple, and W.L. Murray (eds.), Washington, DC, Department of Commerce/NCDC, 35-80. [ PDF ]
This study examines the impact of
projected changes (A1B “marker” scenario) in emissions of four short-lived
air pollutants (ozone, black carbon, organic carbon, and sulfate) on future
climate. Through year 2030, simulated climate is only weakly dependent on
the projected levels of short-lived air pollutants, primarily the result of
a near cancellation of their global net radiative forcing. However, by year
2100, the projected decrease in sulfate aerosol (driven by a 65% reduction
in global sulfur dioxide emissions) and the projected increase in black
carbon aerosol (driven by a 100% increase in its global emissions)
contribute a significant portion of the simulated A1B surface air warming
relative to the year 2000: 0.2°C (Southern Hemisphere), 0.4°C globally,
0.6°C (Northern Hemisphere), 1.5–3°C (wintertime Arctic), and 1.5–2°C (∼40%
of the total) in the summertime United States. These projected changes are
also responsible for a significant decrease in central United States late
summer root zone soil water and precipitation. By year 2100, changes in
short-lived air pollutants produce a global average increase in radiative
forcing of ∼1 W/m2; over east Asia it exceeds 5 W/m2.
However, the resulting regional patterns of surface temperature warming do
not follow the regional patterns of changes in short-lived species
emissions, tropospheric loadings, or radiative forcing (global pattern
correlation coefficient of −0.172). Rather, the regional patterns of warming
from short-lived species are similar to the patterns for well-mixed
greenhouse gases (global pattern correlation coefficient of 0.8) with the
strongest warming occurring over the summer continental United States,
Mediterranean Sea, and southern Europe and over the winter Arctic.
Milly, P C., J Betancourt, M Falkenmark, R M Hirsch, Z W Kundzewicz, D Lettenmaier, and Ronald J Stouffer, 2008: Stationarity is dead: Whither water management?Science, 319(5863), doi:10.1126/science.1151915.
Song, Qian, Gabriel A Vecchi, and Anthony Rosati, 2008: Predictability of the Indian Ocean sea surface temperature anomalies in the GFDL coupled model. Geophysical Research Letters, 35, L02701, doi:10.1029/2007GL031966. [ Abstract ]
We explore the predictability of the sea surface temperature anomalies associated with the Indian Ocean Dipole/Zonal Mode (IODZM) at a three-season lead, within the Geophysical Fluid Dynamics Laboratory (GFDL) coupled general circulation model (CGCM). In both control simulations and retrospective forecasts of the 1990's in the CGCM, we find that the occurrence of some IODZM events is preconditioned by oceanic conditions and potentially predictable three seasons in advance, while other IODZM events appear to be triggered by weather noise and have low predictability. The results highlight the necessity for future studies to distinguish periods when the IODZM is more or less predictable and search for its precursory pattern in the ocean.
In this study, an estimate of the expected
number of Atlantic tropical cyclones (TCs) that were missed by the observing
system in the presatellite era (between 1878 and 1965) is developed. The
significance of trends in both number and duration since 1878 is assessed
and these results are related to estimated changes in sea surface
temperature (SST) over the “main development region” (“MDR”). The
sensitivity of the estimate of missed TCs to underlying assumptions is
examined. According to the base case adjustment used in this study, the
annual number of TCs has exhibited multidecadal variability that has
strongly covaried with multidecadal variations in MDR SST, as has been noted
previously. However, the linear trend in TC counts (1878–2006) is notably
smaller than the linear trend in MDR SST, when both time series are
normalized to have the same variance in their 5-yr running mean series.
Using the base case adjustment for missed TCs leads to an 1878–2006 trend in
the number of TCs that is weakly positive, though not statistically
significant, with p ~ 0.2. The estimated trend for 1900–2006 is
highly significant (+~ 4.2 storms century−1) according to the
results of this study. The 1900–2006 trend is strongly influenced by a
minimum in 1910–30, perhaps artificially enhancing significance, whereas the
1878–2006 trend depends critically on high values in the late 1800s, where
uncertainties are larger than during the 1900s. The trend in average TC
duration (1878–2006) is negative and highly significant. Thus, the evidence
for a significant increase in Atlantic storm activity over the most recent
125 yr is mixed, even though MDR SST has warmed significantly. The
decreasing duration result is unexpected and merits additional exploration;
duration statistics are more uncertain than those of storm counts. As TC
formation, development, and track depend on a number of environmental
factors, of which regional SST is only one, much work remains to be done to
clarify the relationship between anthropogenic climate warming, the
large-scale tropical environment, and Atlantic TC activity.
Vecchi, Gabriel A., A C Clement, and Brian J Soden, February 2008: Examining the tropical Pacific's response to global warming. EOS, 89(9), 81, 83. [ PDF ]
Alternative interpretations of the relationship between sea surface temperature and hurricane activity imply vastly different future Atlantic hurricane activity.
Winton, Michael, December 2008: Sea ice - Albedo feedback and nonlinear Arctic climate change In Arctic Sea Ice Decline, Washington, DC, American Geophysical Union, 111-131. [ AbstractPDF ]
The potential for sea ice-albedo feedback to give rise to nonlinear climate change in the Arctic Ocean defined as a nonlinear relationship between polar and global temperature change or, equivalently, a time-varying polar amplification is explored in IPCC AR4 climate models. Five models supplying SRES A1B ensembles for the 21st century are examined and very linear relationships are found between polar and global temperatures (indicating linear Arctic Ocean climate change), and between polar temperature and albedo (the potential source of nonlinearity). Two of the climate models have Arctic Ocean simulations that become annually sea ice-free under the stronger CO2 increase to quadrupling forcing. Both of these runs show increases in polar amplification at polar temperatures above -5°C and one exhibits heat budget changes that are consisten with the small ice cap instability of simple energy balance models. Both models show linear warming up to a polar temperature of -5° well above the disappearance of their September ice covers at about -9°C. Below -5°C, surface albedo decreases smoothly as reductions move, progressively, to earlier parts of the sunlit period. Atmospheric heat transport exerts a strong cooling effect during the transition to annually ice-free conditions. Specialized experiments with atmosphere and coupled models show that the main damping mechanism for sea ice region surface temperature is reduced upward heat flux through the adjacent ice-free oceans resulting in reduced atmospheric heat transport into the region.
A common practice in the design of forecast models for ENSO is to couple ocean general circulation models to simple atmospheric models. Therefore, by construction these models (known as hybrid ENSO models) do not resolve various kinds of atmospheric variability [e.g., the Madden–Julian oscillation (MJO) and westerly wind bursts] that are often regarded as “unwanted noise.” In this work the sensitivity of three hybrid ENSO models to this unresolved atmospheric variability is studied. The hybrid coupled models were tuned to be asymptotically stable and the magnitude, and spatial and temporal structure of the unresolved variability was extracted from observations. The results suggest that this neglected variability can add an important piece of realism and forecast skill to the hybrid models. The models were found to respond linearly to the low-frequency part of the neglected atmospheric variability, in agreement with previous findings with intermediate models. While the wind anomalies associated with the MJO typically explain a small fraction of the unresolved variability, a large fraction of the interannual variability can be excited by this forcing. A large correlation was found between interannual anomalies of Kelvin waves forced by the intraseasonal MJO and the Kelvin waves forced by the low-frequency part of the MJO. That is, in years when the MJO tends to be more active it also produces a larger low-frequency contribution, which can then resonate with the large-scale coupled system. Other kinds of atmospheric variability not related to the MJO can also produce interannual anomalies in the hybrid models. However, when projected on the characteristics of Kelvin waves, no clear correlation between its low-frequency content and its intraseasonal activity was found. This suggests that understanding the mechanisms by which the intraseasonal MJO interacts with the ocean to modulate its low-frequency content may help to better to predict ENSO variability.
Zhang, Rong, October 2008: Coherent surface-subsurface fingerprint of the Atlantic meridional overturning circulation. Geophysical Research Letters, 35, L20705, doi:10.1029/2008GL035463. [ Abstract ]
Satellite altimeter data shows a weakening of the North Atlantic subpolar gyre during the 1990s, which is thought as an indicator of a slowdown of the Atlantic meridional overturning circulation (AMOC). However, whether the recent slowing subpolar gyre is a decadal variation or a long-term trend remains unclear. Here I show that altimeter data is highly correlated with instrumental subsurface ocean temperature data in the North Atlantic, and both show opposite signs between the subpolar gyre and the Gulf Stream path. Such a dipole pattern is a distinctive fingerprint of AMOC variability, as shown for the first time by a 1000-year coupled ocean-atmosphere model simulation. The results suggest that, contrary to previous interpretations, the recent slowdown of the subpolar gyre is a part of a multidecadal variation and suggests a strengthening of the AMOC. The ongoing satellite and subsurface temperature measurements could be used to monitor future AMOC variations sensitively.
The impact of the penetration length scale of shortwave radiation into the surface ocean is investigated with a fully coupled ocean, atmosphere, land and ice model. Oceanic shortwave radiation penetration is assumed to depend on the chlorophyll concentration. As chlorophyll concentrations increase the distribution of shortwave heating becomes shallower. This change in heat distribution impacts mixed-layer depth. This study shows that removing all chlorophyll from the ocean results in a system that tends strongly towards an El Niño state—suggesting that chlorophyll is implicated in maintenance of the Pacific cold tongue. The regions most responsible for this response are located off-equator and correspond to the oligotrophic gyres. Results from a suite of surface chlorophyll perturbation experiments suggest a potential positive feedback between chlorophyll concentration and a non-local coupled response in the fully coupled ocean-atmosphere system.
Delworth, Thomas L., Rong Zhang, and M E Mann, 2007: Decadal to centennial variability of the Atlantic from observations and models In Ocean Circulation: Mechanisms and Impacts, Geophysical Monograph Series 173, Washington, DC, American Geophysical Union, 131-148. [ AbstractPDF ]
Some aspects of multidecadal Atlantic climate variability, and its impact on regional and hemispheric scale climate, are reviewed. Observational analyses have documented distinct patterns of Atlantic variability with decadal (8-12 years) and multidecadal (30-80 years) time scales. Numerical models have succeeded in capturing some aspects of this observed variability, but much work remains to understand the mechanisms of the observed variability. The impacts of the variability — particularly on the multidecadal time scale — are striking, including modulation of African and Indian summer monsoon rainfall, summer climate over North America and Europe, and a potential influence on Atlantic hurricane activity. Some of the observed variability, particularly in recent decades, is likely influenced by changing radiative forcings, of both anthropogenic and natural origin. This poses an important challenge for the detection, attribution and prediction of climate change.
Delworth, Thomas L., and Kirsten L Findell, 2007: Decadal to centennial scale changes in summer continental hydrology In Climate Variability and Change: Past, Present, and Future, John E. Kutzbach Symposium, Gisela Kutzbach, Ed., Madison, WI, Ctr. of Climatic Research, U. Wisconsin-Madison, 49-56. [ Abstract ]
Past
studies have suggested that increasing atmospheric CO2 will lead
to a substantial reduction of soil moisture during summer in the
extratropics. We revisit this topic using a new climate model developed at
NOAA's Geophysical Fluid Dynamics Laboratory. The new model has a horizontal
resolution of 2.5° longitude by 2.0° latitude, with 24 vertical levels, and
has both a diurnal and seasonal cycle of insolation. The model incorporates
substantially updated physics relative to previous versions.
Results from
earlier studies showed, among other things, an increase in wintertime
rainfall over most mid-latitude continental regions when CO2 is
doubled, an earlier snowmelt season and onset of springtime evaporation, and
a higher ratio of evaporation to precipitation in summer. These factors led
to large-scale increases in soil moisture in winter and decreases in summer
in mid-latitude in doubled-CO2 experiments. The new model shows
similar results, and the processes discussed above are important in this
model as well. In addition, we find that changes in atmospheric circulation
play an important role in regional hydrologic changes. Additional
experiments have been run to probe the causes of the circulation changes.
These simulations show that global scale sea surface temperature increases
caused by the CO2 doubling explain the majority of the
atmospheric circulation changes, while positive feedbacks from the land
surface have a secondary impact. These results highlight the importance of
global scale sea surface temperature changes for future regional hydrology
changes.
Donner, Simon D., Thomas R Knutson, and M Oppenheimer, March 2007: Model-based assessment of the role of human-induced climate change in the 2005 Caribbean coral bleaching event. Proceedings of the National Academy of Sciences, 104(13), doi:10.1073/pnas.0610122104. [ Abstract ]
Episodes of mass coral bleaching around the world in recent
decades have been attributed to periods of anomalously warm ocean
temperatures. In 2005, the sea surface temperature (SST) anomaly
in the tropical North Atlantic that may have contributed to the
strong hurricane season caused widespread coral bleaching in the
Eastern Caribbean. Here, we use two global climate models to
evaluate the contribution of natural climate variability and
anthropogenic forcing to the thermal stress that caused the 2005
coral bleaching event. Historical temperature data and
simulations for the 1870–2000 period show that the observed
warming in the region is unlikely to be due to unforced climate
variability alone. Simulation of background climate variability
suggests that anthropogenic warming may have increased the
probability of occurrence of significant thermal stress events
for corals in this region by an order of magnitude. Under
scenarios of future greenhouse gas emissions, mass coral bleaching
in the Eastern Caribbean may become a biannual event in 20–30
years. However, if corals and their symbionts can adapt by 1–1.5°C,
such mass bleaching events may not begin to recur at potentially
harmful intervals until the latter half of the century. The
delay could enable more time to alter the path of greenhouse gas
emissions, although long-term "committed warming" even after
stabilization of atmospheric CO2 levels may still represent
an additional long-term threat to corals.
Eng, K, and P C D Milly, 2007: Relating low-flow characteristics to the base flow recession time constant at partial record stream gauges. Water Resources Research, 43, W01201, doi:10.1029/2006WR005293. [ AbstractPDF ]
Base flow recession information is helpful for regional estimation of low-flow characteristics. However, analyses that exploit such information generally require a continuous record of streamf low at the estimation site to characterize base flow recession. Here we propose a simple method for characterizing base flow recession at low-flow partial record stream gauges (i.e., sites with very few streamflow measurements under low-streamflow conditions), and we use that characterization as the basis for a practical new approach to low-flow regression. In a case study the introduction of a base flow recession time constant, estimated from a single pair of strategically timed streamflow measurements, approximately halves the root-mean-square estimation error relative to that of a conventional drainage area regression. Additional streamflow measurements can be used to reduce the error further.
Eng, K, P C D Milly, and G D Tasker, November 2007: Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method. Journal of Hydrologic Engineering, 12(6), 585-591. [ Abstract ]
To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow (Q50). The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation) is important, but incomplete, and that the consideration of geographic proximity of stations provides a useful surrogate for characteristics that are not included in the analysis.
http://dx.doi.org/10.1061/(ASCE)1084-0699(2007)12:6(585)
Fedorov, Alexey, M Barreiro, G Boccaletti, Ronald C Pacanowski, and S G H Philander, April 2007: The freshening of surface waters in high latitudes: Effects on the thermohaline and wind-driven circulations. Journal of Physical Oceanography, 37(4), doi:10.1175/JPO3033.1. [ Abstract ]
The impacts of a freshening of surface waters in high latitudes on the deep, slow, thermohaline circulation have received enormous attention, especially the possibility of a shutdown in the meridional overturning that involves sinking of surface waters in the northern Atlantic Ocean. A recent study by Federov,et al. has drawn attention to the effects of a freshening on the other main component of the oceanic circulation—the swift, shallow, wind-driven circulation that varies on decadal time scales and is closely associated with the ventilated thermocline. That circulation, too, involves meridional overturning, but its variations and critical transitions affect mainly the Tropics. A surface freshening in mid- to high latitudes can deepen the equatorial thermocline to such a degree that temperatures along the equator become as warm in the eastern part of the basin as they are in the west, the tropical zonal sea surface temperature gradient virtually disappears, and permanently warm conditions prevail in the Tropics. In a model that has both the wind driven and thermohaline components of the circulation, which factors determine the relative effects of a freshening on the two components and its impact on climate? Studies with an idealized ocean general circulation model find that vertical diffusivity is one of the critical parameters that affect the relative strength of the two circulation components and hence their response to a freshening. The spatial structure of the freshening and imposed meridional temperature gradients are other important factors.
Equilibrium experiments with the Geophysical Fluid Dynamics Laboratory’s climate model are used to investigate the impact of anthropogenic land cover change on climate. Regions of altered land cover include large portions of Europe, India, eastern China, and the eastern United States. Smaller areas of change are present in various tropical regions. This study focuses on the impacts of biophysical changes associated with the land cover change (albedo, root and stomatal properties, roughness length), which is almost exclusively a conversion from forest to grassland in the model; the effects of irrigation or other water management practices and the effects of atmospheric carbon dioxide changes associated with land cover conversion are not included in these experiments.
The model suggests that observed land cover changes have little or no impact on globally averaged climatic variables (e.g., 2-m air temperature is 0.008 K warmer in a simulation with 1990 land cover compared to a simulation with potential natural vegetation cover). Differences in the annual mean climatic fields analyzed did not exhibit global field significance. Within some of the regions of land cover change, however, there are relatively large changes of many surface climatic variables. These changes are highly significant locally in the annual mean and in most months of the year in eastern Europe and northern India. They can be explained mainly as direct and indirect consequences of model-prescribed increases in surface albedo, decreases in rooting depth, and changes of stomatal control that accompany deforestation.
Frappier, A, Thomas R Knutson, K-B Liu, and Kerry A Emanuel, 2007: Perspective: coordinating paleoclimate research on tropical cyclones with hurricane-climate theory and modelling. Tellus A, 59(4), 529-537. [ AbstractPDF ]
Extending the meteorological record back in time can offer critical data for assessing tropical cyclone-climate links. While paleotempestology, the study of ancient storms, can provide a more realistic view of past ‘worst case scenarios’, future environmental conditions may have no analogues in the paleoclimate record. The primary value in paleotempestology proxy records arises from their ability to quantify climate–tropical cyclone interactions by sampling tropical cyclone activity during pre-historic periods with a wider range of different climates. New paleotempestology proxies are just beginning to be applied, encouraging new collaboration between the paleo and tropical cyclone dynamics communities. The aim of this paper is to point out some paths toward closer coordination by outlining target needs of the tropical cyclone theory and modelling community and potential contributions of the paleotempestology community. We review recent advances in paleotempestology, summarize the range of types and quality of paleodata generation, and identify future research opportunities for paleotempestology, tropical cyclone dynamics and climate change impacts and attribution communities.
Gebbie, G, I Eisenman, Andrew T Wittenberg, and E Tziperman, 2007: Modulation of Westerly Wind Bursts by Sea Surface Temperature: A Semistochastic Feedback for ENSO. Journal of the Atmospheric Sciences, 64(9), doi:10.1175/JAS4029.1. [ Abstract ]
Westerly wind bursts (WWBs) in the equatorial Pacific are known to play a significant role in the development of El Niño events. They have typically been treated as a purely stochastic external forcing of ENSO. Recent observations, however, show that WWB characteristics depend upon the large-scale SST field. The consequences of such a WWB modulation by SST are examined using an ocean general circulation model coupled to a statistical atmosphere model (i.e., a hybrid coupled model). An explicit WWB component is added to the model with guidance from a 23-yr observational record. The WWB parameterization scheme is constructed such that the likelihood of WWB occurrence increases as the western Pacific warm pool extends: a “semistochastic” formulation, which has both deterministic and stochastic elements. The location of the WWBs is parameterized to migrate with the edge of the warm pool. It is found that modulation of WWBs by SST strongly affects the characteristics of ENSO. In particular, coupled feedbacks between SST and WWBs may be sufficient to transfer the system from a damped regime to one with self-sustained oscillations. Modulated WWBs also play a role in the irregular timing of warm episodes and the asymmetry in the size of warm and cold events in this ENSO model. Parameterizing the modulation of WWBs by an increase of the linear air–sea coupling coefficient seems to miss important dynamical processes, and a purely stochastic representation of WWBs elicits only a weak ocean response. Based upon this evidence, it is proposed that WWBs may need to be treated as an internal part of the coupled ENSO system, and that the detailed knowledge of wind burst dynamics may be necessary to explain the characteristics of ENSO.
Gebbie, G, I Eisenman, Andrew T Wittenberg, and E Tziperman, September 2007: Could ocean-modulated wind bursts lead to better El Niño forecasts?Bulletin of the American Meteorological Society, 88(9), 1356-1357. [ PDF ]
Gnanadesikan, Anand, Joellen L Russell, and Fanrong Zeng, 2007: How does ocean ventilation change under global warming?Ocean Science, 3(1), 43-53. [ AbstractPDF ]
Since the upper ocean takes up much of the heat added to the earth system by anthropogenic global warming, one would expect that global warming would lead to an increase in stratification and a decrease in the ventilation of the ocean interior. However, multiple simulations in global coupled climate models using an ideal age tracer which is set to zero in the mixed layer and ages at 1 yr/yr outside this layer show that the intermediate depths in the low latitudes, Northwest Atlantic, and parts of the Arctic Ocean become younger under global warming. This paper reconciles these apparently contradictory trends, showing that the decreases result from changes in the relative contributions of old deep waters and younger surface waters. Implications for the tropical oxygen minimum zones, which play a critical role in global biogeochemical cycling are considered in detail.
In
this study, a new modeling framework for simulating Atlantic hurricane
activity is introduced. The model is an 18-km-grid nonhydrostatic regional
model, run over observed specified SSTs and nudged toward observed
time-varying large-scale atmospheric conditions (Atlantic domain wavenumbers
0–2) derived from the National Centers for Environmental Prediction (NCEP)
reanalyses. Using this “perfect large-scale model” approach for 27 recent
August–October seasons (1980–2006), it is found that the model successfully
reproduces the observed multidecadal increase in numbers of Atlantic
hurricanes and several other tropical cyclone (TC) indices over this period.
The correlation of simulated versus observed hurricane activity by year
varies from 0.87 for basin-wide hurricane counts to 0.41 for U.S.
landfalling hurricanes. For tropical storm count, accumulated cyclone
energy, and TC power dissipation indices the correlation is 0.75, for major
hurricanes the correlation is 0.69, and for U.S. landfalling tropical
storms, the correlation is 0.57. The model occasionally simulates hurricanes
intensities of up to category 4 (942 mb) in terms of central pressure,
although the surface winds (< 47 m s-1 ) do not exceed category-2
intensity. On interannual time scales, the model reproduces the observed
ENSO-Atlantic hurricane covariation reasonably well. Some notable aspects of
the highly contrasting 2005 and 2006 seasons are well reproduced, although
the simulated activity during the 2006 core season was excessive. The
authors conclude that the model appears to be a useful tool for exploring
mechanisms of hurricane variability in the Atlantic (e.g., shear versus
potential intensity contributions). The model may be capable of making
useful simulations/projections of pre-1980 or twentieth-century Atlantic
hurricane activity. However, the reliability of these projections will
depend on obtaining reliable large-scale atmospheric and SST conditions from
sources external to the model.
Lu, Jian, Gabriel A Vecchi, and T Reichler, 2007: Expansion of the Hadley cell under global warming. Geophysical Research Letters, 34, L06805, doi:10.1029/2006GL028443. [ Abstract ]
A consistent weakening and poleward expansion of the Hadley circulation is diagnosed in the climate change simulations of the IPCC AR4 project. Associated with this widening is a poleward expansion of the subtropical dry zone. Simple scaling analysis supports the notion that the poleward extent of the Hadley cell is set by the location where the thermally driven jet first becomes baroclinically unstable. The expansion of the Hadley cell is caused by an increase in the subtropical static stability, which pushes poleward the baroclinic instability zone and hence the outer boundary of the Hadley cell.
Meehl, Gerald A., C Covey, Thomas L Delworth, M Latif, B McAveney, J F B Mitchell, Ronald J Stouffer, and Karl E Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88(9), doi:10.1175/BAMS-88-9-1383. [ Abstract ]
A coordinated set of global coupled climate model [atmosphere–ocean general circulation model (AOGCM)] experiments for twentieth- and twenty-first-century climate, as well as several climate change commitment and other experiments, was run by 16 modeling groups from 11 countries with 23 models for assessment in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Since the assessment was completed, output from another model has been added to the dataset, so the participation is now 17 groups from 12 countries with 24 models. This effort, as well as the subsequent analysis phase, was organized by the World Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on Coupled Models (WGCM) Climate Simulation Panel, and constitutes the third phase of the Coupled Model Intercomparison Project (CMIP3). The dataset is called the WCRP CMIP3 multimodel dataset, and represents the largest and most comprehensive international global coupled climate model experiment and multimodel analysis effort ever attempted. As of March 2007, the Program for Climate Model Diagnostics and Intercomparison (PCMDI) has collected, archived, and served roughly 32 TB of model data. With oversight from the panel, the multimodel data were made openly available from PCMDI for analysis and academic applications. Over 171 TB of data had been downloaded among the more than 1000 registered users to date. Over 200 journal articles, based in part on the dataset, have been published so far. Though initially aimed at the IPCC AR4, this unique and valuable resource will continue to be maintained for at least the next several years. Never before has such an extensive set of climate model simulations been made available to the international climate science community for study. The ready access to the multimodel dataset opens up these types of model analyses to researchers, including students, who previously could not obtain state-of-the-art climate model output, and thus represents a new era in climate change research. As a direct consequence, these ongoing studies are increasing the body of knowledge regarding our understanding of how the climate system currently works, and how it may change in the future.
Milly, P C., June 2007: Stationarity is dead. Ground Water News and Views, 4(1), 6, 8. [ PDF ]
Schmittner, A, Eric D Galbraith, S W Hostetler, T F Pedersen, and Rong Zhang, 2007: Large fluctuations of dissolved oxygen in the Indian and Pacific oceans during Dansgaard-Oeschger oscillations caused by variations of North Atlantic Deep Water subduction. Paleoceanography, 22, PA3207, doi:10.1029/2006PA001384. [ Abstract ]
Paleoclimate records from glacial Indian and Pacific oceans sediments document millennial-scale fluctuations of subsurface dissolved oxygen levels and denitrification coherent with North Atlantic temperature oscillations. Yet the mechanism of this teleconnection between the remote ocean basins remains elusive. Here we present model simulations of the oxygen and nitrogen cycles that explain how changes in deepwater subduction in the North Atlantic can cause large and synchronous variations of oxygen minimum zones throughout the Northern Hemisphere of the Indian and Pacific oceans, consistent with the paleoclimate records. Cold periods in the North Atlantic are associated with reduced nutrient delivery to the upper Indo-Pacific oceans, thereby decreasing productivity. Reduced export production diminishes subsurface respiration of organic matter leading to higher oxygen concentrations and less denitrification. This effect of reduced oxygen consumption dominates at low latitudes. At high latitudes in the Southern Ocean and North Pacific, increased mixed layer depths and steepening of isopycnals improve ocean ventilation and oxygen supply to the subsurface. Atmospheric teleconnections through changes in wind-driven ocean circulation modify this basin-scale pattern regionally. These results suggest that changes in the Atlantic Ocean circulation, similar to those projected by climate models to possibly occur in the centuries to come because of anthropogenic climate warming, can have large effects on marine ecosystems and biogeochemical cycles even in remote areas.
How anthropogenic climate change will affect hydroclimate in the arid regions of southwestern North America has implications for the allocation of water resources and the course of regional development. Here we show that there is a broad consensus among climate models that this region will dry in the 21st century and that the transition to a more arid climate should already be under way. If these models are correct, the levels of aridity of the recent multiyear drought or the Dust Bowl and the 1950s droughts will become the new climatology of the American Southwest within a time frame of years to decades.
Following Hurricane Katrina and the parade of storms that affected the conterminous United States in 2004–2005, the apparent recent increase in intense hurricane activity in the Atlantic basin, and the reported increases in recent decades in some hurricane intensity and duration measures in several basins have received considerable attention. An important ongoing avenue of investigation in the climate and meteorology research communities is to determine the relative roles of anthropogenic forcing (i.e., global warming) and natural variability in producing the observed recent increases in hurricane frequency in the Atlantic, as well as the reported increases of tropical cyclone activity measures in several other ocean basins. A survey of the existing literature shows that many types of data have been used to describe hurricane intensity, and not all records are of sufficient length to reliably identify historical trends. Additionally, there are concerns among researchers about possible effects of data inhomogeneities on the reported trends. Much of the current debate has focused on the relative roles of sea-surface temperatures or large-scale potential intensity versus the role of other environmental factors such as vertical wind shear in causing observed changes in hurricane statistics. Significantly more research – from observations, theory, and modeling – is needed to resolve the current debate around global warming and hurricanes.
Song, Qian, Gabriel A Vecchi, and Anthony Rosati, June 2007: The role of Indonesian throughflow in the Indo-Pacific climate variability in the GFDL coupled climate model. Journal of Climate, 20(11), doi:10.1175/JCLI4133.1. [ Abstract ]
The impacts of the Indonesian Throughflow (ITF) on the tropical Indo–Pacific climate, particularly on the character of interannual variability, are explored using a coupled general circulation model (CGCM). A pair of CGCM experiments—a control experiment with an open ITF and a perturbation experiment in which the ITF is artificially closed—is integrated for 200 model years, with the 1990 values of trace gases. The closure of the ITF results in changes to the mean oceanic and atmospheric conditions throughout the tropical Indo–Pacific domain as follows: surface temperatures in the eastern tropical Pacific (Indian) Ocean warm (cool), the near-equatorial Pacific (Indian) thermocline flattens (shoals), Indo–Pacific warm-pool precipitation shifts eastward, and there are relaxed trade winds over the tropical Pacific and anomalous surface easterlies over the equatorial Indian Ocean. The character of the oceanic changes is similar to that described by ocean-only model experiments, though the amplitude of many features in the tropical Indo–Pacific is amplified in the CGCM experiments.
In addition to the mean-state changes, the character of tropical Indo–Pacific interannual variability is substantially modified. Interannual variability in the equatorial Pacific and the eastern tropical Indian Ocean is substantially intensified by the closure of the ITF. In addition to becoming more energetic, El Niño–Southern Oscillation (ENSO) exhibits a shorter time scale of variability and becomes more skewed toward its warm phase (stronger and more frequent warm events). The structure of warm ENSO events changes; the anomalies of sea surface temperature (SST), precipitation, and surface westerly winds are shifted to the east and the meridional extent of surface westerly anomalies is larger.
In the eastern tropical Indian Ocean, the interannual SST variability off the coast of Java–Sumatra is noticeably amplified by the occurrence of much stronger cooling events. Closing the ITF shoals the eastern tropical Indian Ocean thermocline, which results in stronger cooling events through enhanced atmosphere–thermocline coupled feedbacks. Changes to the interannual variability caused by the ITF closure rectify into mean-state changes in tropical Indo–Pacific conditions. The modified Indo–Pacific interannual variability projects onto the mean-state differences between the ITF open and closed scenarios, rectifying into mean-state differences. These results suggest that CGCMs need to reasonably simulate the ITF in order to successfully represent not just the mean climate, but its variations as well.
The interannual variability of the Indian Ocean, with particular focus on the Indian Ocean dipole/zonal mode (IODZM), is investigated in a 250-yr simulation of the GFDL coupled global general circulation model (CGCM). The CGCM successfully reproduces many fundamental characteristics of the climate system of the Indian Ocean. The character of the IODZM is explored, as are relationships between positive IODZM and El Niño events, through a composite analysis. The IODZM events in the CGCM grow through feedbacks between heat-content anomalies and SST-related atmospheric anomalies, particularly in the eastern tropical Indian Ocean. The composite IODZM events that co-occur with El Niño have stronger anomalies and a sharper east–west SSTA contrast than those that occur without El Niño. IODZM events, whether or not they occur with El Niño, are preceded by distinctive Indo-Pacific warm pool anomaly patterns in boreal spring: in the central Indian Ocean easterly surface winds, and in the western equatorial Pacific an eastward shift of deep convection, westerly surface winds, and warm sea surface temperature. However, delayed onsets of the anomaly patterns (e.g., boreal summer) are often not followed by IODZM events. The same anomaly patterns often precede El Niño, suggesting that the warm pool conditions favorable for both IODZM and El Niño are similar. Given that IODZM events can occur without El Niño, it is proposed that the observed IODZM–El Niño relation arises because the IODZM and El Niño are both large-scale phenomena in which variations of the Indo-Pacific warm pool deep convection plays a central role. Yet each phenomenon has its own dynamics and life cycle, allowing each to develop without the other.
The CGCM integration also shows substantial decadal modulation of the occurrence of IODZM events, which is found to be not in phase with that of El Niño events. There is a weak, though significant, negative correlation between the two. Moreover, the statistical relationship between the IODZM and El Niño displays strong decadal variability.
Two global ocean analyses from 1993 to 2001 have been generated by the Global Modeling and Assimilation Office (GMAO) and Geophysical Fluid Dynamics Laboratory (GFDL), as part of the Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI) consortium efforts. The ocean general circulation models (OGCM) and assimilation methods in the analyses are different, but the forcing and observations are the same as designed for ODASI experiments. Global expendable bathythermograph and Tropical Atmosphere Ocean (TAO) temperature profile observations are assimilated. The GMAO analysis also assimilates synthetic salinity profiles based on climatological T–S relationships from observations (denoted "TS scheme"). The quality of the two ocean analyses in the tropical Pacific is examined here. Questions such as the following are addressed: How do different assimilation methods impact the analyses, including ancillary fields such as salinity and currents? Is there a significant difference in interpretation of the variability from different analyses? How does the treatment of salinity impact the analyses? Both GMAO and GFDL analyses reproduce the time mean and variability of the temperature field compared with assimilated TAO temperature data, taking into account the natural variability and representation errors of the assimilated temperature observations. Surface zonal currents at 15 m from the two analyses generally agree with observed climatology. Zonal current profiles from the analyses capture the intensity and variability of the Equatorial Undercurrent (EUC) displayed in the independent acoustic Doppler current profiler data at three TAO moorings across the equatorial Pacific basin. Compared with independent data from TAO servicing cruises, the results show that 1) temperature errors are reduced below the thermocline in both analyses; 2) salinity errors are considerably reduced below the thermocline in the GMAO analysis; and 3) errors in zonal currents from both analyses are comparable. To discern the impact of the forcing and salinity treatment, a sensitivity study is undertaken with the GMAO assimilation system. Additional analyses are produced with a different forcing dataset, and another scheme to modify the salinity field is tested. This second scheme updates salinity at the time of temperature assimilation based on model T–S relationships (denoted "T scheme"). The results show that both assimilated field (i.e., temperature) and fields that are not directly observed (i.e., salinity and currents) are impacted. Forcing appears to have more impact near the surface (above the core of the EUC), while the salinity treatment is more important below the surface that is directly influenced by forcing. Overall, the TS scheme is more effective than the T scheme in correcting model bias in salinity and improving the current structure. Zonal currents from the GMAO control run where no data are assimilated are as good as the best analysis.
To help understand possible impacts of anthropogenic greenhouse warming on hurricane activity, we assess model-projected changes in large-scale environmental factors tied to variations in hurricane statistics. This study focuses on vertical wind shear (Vs) over the tropical Atlantic during hurricane season, the increase of which has been historically associated with diminished hurricane activity and intensity. A suite of state-of-the-art global climate model experiments is used to project changes in Vs over the 21st century. Substantial increases in tropical Atlantic and East Pacific shear are robust features of these experiments, and are shown to be connected to the model-projected decrease in the Pacific Walker circulation. The relative changes in shear are found to be comparable to those of other large-scale environmental parameters associated with Atlantic hurricane activity. The influence of these Vs changes should be incorporated into projections of long-term hurricane activity.
An integrated in situ Indian Ocean observing system (IndOOS) is simulated using a high-resolution ocean general circulation model (OGCM) with daily mean forcing, including an estimate of subdaily oceanic variability derived from observations. The inclusion of subdaily noise is fundamental to the results; in the mixed layer it is parameterized as Gaussian noise with an rms of 0.1°C; below the mixed layer a Gaussian interface displacement with an rms of 7 m is used. The focus of this assessment is on the ability of an IndOOS—comprising a 3° × 3° Argo profiling float array, a series of frequently repeated XBT lines, and an array of moored buoys—to observe the interannual and subseasonal variability of subsurface Indian Ocean temperature. The simulated IndOOS captures much of the OGCM interannual subsurface temperature variability.
A fully deployed Argo array with 10-day sampling interval is able to capture a significant part of the Indian Ocean interannual temperature variability; a 5-day sampling interval degrades its ability to capture variability. The proposed moored buoy array and frequently repeated XBT lines provide complementary information in key regions, particularly the Java/Sumatra and Somali upwelling and equatorial regions. Since the subdaily noise is of the same order as the subseasonal signal and since much of the variability is submonthly, a 5-day sampling interval does not drastically enhance the ability of Argo to capture the OGCM subseasonal variability. However, as sampling intervals are decreased, there is enhanced divergence of the Argo floats, diminished ability to quality control data, and a decreased lifetime of the floats; these factors argue against attempting to resolve subseasonal variability with Argo by shortening the sampling interval. A moored array is essential to capturing the subseasonal and near-equatorial variability in the model, and the proposed moored buoy locations span the region of strong subseasonal variability. On the whole, the proposed IndOOS significantly enhances the ability to capture both interannual and subseasonal variability in the Indian Ocean.
This study examines the response of the tropical atmospheric and oceanic circulation to increasing
greenhouse gases using a coordinated set of twenty-first-century climate model experiments performed for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The strength
of the atmospheric overturning circulation decreases as the climate warms in all IPCC AR4 models, in a
manner consistent with the thermodynamic scaling arguments of Held and Soden. The weakening occurs
preferentially in the zonally asymmetric (i.e., Walker) rather than zonal-mean (i.e., Hadley) component of
the tropical circulation and is shown to induce substantial changes to the thermal structure and circulation
of the tropical oceans. Evidence suggests that the overall circulation weakens by decreasing the frequency
of strong updrafts and increasing the frequency of weak updrafts, although the robustness of this behavior
across all models cannot be confirmed because of the lack of data. As the climate warms, changes in both
the atmospheric and ocean circulation over the tropical Pacific Ocean resemble “El Niño–like” conditions;
however, the mechanisms are shown to be distinct from those of El Niño and are reproduced in both mixed
layer and full ocean dynamics coupled climate models. The character of the Indian Ocean response to global
warming resembles that of Indian Ocean dipole mode events. The consensus of model results presented
here is also consistent with recently detected changes in sea level pressure since the mid–nineteenth century.
The
response of tropical cyclone activity to global warming is widely debated.
It is often assumed that warmer sea surface temperatures provide a more
favourable environment for the development and intensification of tropical
cyclones, but cyclone genesis and intensity are also affected by the
vertical thermodynamic properties of the atmosphere. Here we use climate
models and observational reconstructions to explore the relationship between
changes in sea surface temperature and tropical cyclone 'potential
intensity'—a measure that provides an upper bound on cyclone intensity and
can also reflect the likelihood of cyclone development. We find that changes
in local sea surface temperature are inadequate for characterizing even the
sign of changes in potential intensity, but that long-term changes in
potential intensity are closely related to the regional structure of
warming; regions that warm more than the tropical average are characterized
by increased potential intensity, and vice versa. We use this relationship
to reconstruct changes in potential intensity over the twentieth century
from observational reconstructions of sea surface temperature. We find that,
even though tropical Atlantic sea surface temperatures are currently at a
historical high, Atlantic potential intensity probably peaked in the 1930s
and 1950s, and recent values are near the historical average. Our results
indicate that—per unit local sea surface temperature change—the response of
tropical cyclone activity to natural climate variations, which tend to
involve localized changes in sea surface temperature, may be larger than the
response to the more uniform patterns of greenhouse-gas-induced warming.
While the Northern Hemisphere mean surface temperature has clearly warmed over the 20th century due in large part to increasing greenhouse gases, this warming has not been monotonic. The departures from steady warming on multidecadal timescales might be associated in part with radiative forcing, especially solar irradiance, volcanoes, and anthropogenic aerosols. It is also possible that internal oceanic variability explains a part of this variation. We report here on simulations with a climate model in which the Atlantic Ocean is constrained to produce multidecadal fluctuations similar to observations by redistributing heat within the Atlantic, with other oceans left free to adjust to these Atlantic perturbations. The model generates multidecadal variability in Northern Hemisphere mean temperatures similar in phase and magnitude to detrended observations. The results suggest that variability in the Atlantic is a viable explanation for a portion of the multidecadal variability in the Northern Hemisphere mean temperature record.
Zhang, Rong, 2007: Anticorrelated multidecadal variations between surface and subsurface tropical North Atlantic. Geophysical Research Letters, 34, L12713, doi:10.1029/2007GL030225. [ AbstractPDF ]
In this paper for the first time I show that the multidecadal variations of observed tropical North Atlantic (TNA) sea surface temperature (SST) are strongly anticorrelated with those of the observed TNA subsurface ocean temperature, with long-term trends removed. I further show that the anticorrelated change between the TNA surface and subsurface temperature is a distinctive signature of the Atlantic meridional overturning circulation (AMOC) variations, using water-hosing experiments with the GFDL state-of-art coupled climate model (CM2.1). External radiative forced simulations with the same model do not provide a significant relationship between the TNA surface and subsurface temperature variations. The observed detrended multidecadal TNA subsurface temperature anomaly may be taken as a proxy for the AMOC variability. Various mechanisms proposed for the multidecadal TNA SST variations, which are crucial for multidecadal variations of Atlantic hurricane activities, should take into account the observed anticorrelation between the TNA surface and subsurface temperature variations.
Zhang, Rong, and Geoffrey K Vallis, August 2007: The role of bottom vortex stretching on the path of the North Atlantic Western Boundary Current and on the Northern Recirculation Gyre. Journal of Physical Oceanography, 37(8), doi:10.1175/JPO3102.1. [ Abstract ]
The mechanisms affecting the path of the depth-integrated North Atlantic western boundary current and the formation of the northern recirculation gyre are investigated using a hierarchy of models, namely, a robust diagnostic model, a prognostic model using a global 1° ocean general circulation model coupled to a two-dimensional atmospheric energy balance model with a hydrological cycle, a simple numerical barotropic model, and an analytic model. The results herein suggest that the path of this boundary current and the formation of the northern recirculation gyre are sensitive to both the magnitude of lateral viscosity and the strength of the deep western boundary current (DWBC). In particular, it is shown that bottom vortex stretching induced by a downslope DWBC near the south of the Grand Banks leads to the formation of a cyclonic northern recirulation gyre and keeps the path of the depth-integrated western boundary current downstream of Cape Hatteras separated from the North American coast. Both south of the Grand Banks and at the crossover region of the DWBC and Gulf Stream, the downslope DWBC induces strong bottom downwelling over the steep continental slope, and the magnitude of the bottom downwelling is locally stronger than surface Ekman pumping velocity, providing strong positive vorticity through bottom vortex stretching effects. The bottom vortex-stretching effect is also present in an extensive area in the North Atlantic, and the contribution to the North Atlantic subpolar and subtropical gyres is on the same order as the local surface wind stress curl. Analytic solutions show that the bottom vortex stretching is important near the western boundary only when the continental slope is wider than the Munk frictional layer scale.
A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation—with temporally varying greenhouse gas and natural aerosol radiative forcings—serves as a “truth” from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the “truth,” the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature–salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations—including those from the newly deployed Argo profiles—may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.
In this paper, we found that the Atlantic Multidecadal Oscillation (AMO) can contribute to the Pacific Decadal Oscillation (PDO), especially the component of the PDO that is linearly independent of El Niño and the Southern Oscillation (ENSO), i.e. the North Pacific Multidecadal Oscillation (NPMO), and the associated Pacific/North America (PNA) pattern. Using a hybrid version of the GFDL CM2.1 climate model, we show that the AMO provides a source of multidecadal variability to the North Pacific, and needs to be considered along with other forcings for North Pacific climate change. The lagged North Pacific response to the North Atlantic forcing is through atmospheric teleconnections and reinforced by oceanic dynamics and positive air-sea feedback over the North Pacific. The results indicate that a North Pacific regime shift, opposite to the 1976–77 shift, might occur now a decade after the switch of the observed AMO to a positive phase around 1995.
