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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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/
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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, 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.
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.
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.
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.
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.
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.
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.
Wittenberg, Andrew T., December 2015: Low-frequency variations of ENSO. U.S. CLIVAR Variations, 13(1), 26-31.
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.
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.
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.
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.
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
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.
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.
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 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.
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.
Capotondi, Antonietta, and Andrew T Wittenberg, July 2013: ENSO diversity in climate models. U.S. CLIVAR Variations, 11(2), 10-14.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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, . Abstract PDF
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.
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.
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.
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.
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
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.
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.
Capotondi, Antonietta, Andrew T Wittenberg, and S Masina, 2006: Spatial and temporal structure of Tropical Pacific interannual variability in 20th century coupled simulations. Ocean Modelling, 15(3-4), DOI:10.1016/j.ocemod.2006.02.004. Abstract
Tropical Pacific interannual variability is examined in nine state-of-the-art coupled climate models, and compared with observations and ocean analyses data sets, the primary focus being on the spatial structure and spectral characteristics of El Niño-Southern Oscillation (ENSO). The spatial patterns of interannual sea surface temperature (SST) anomalies from the coupled models are characterized by maximum variations displaced from the coast of South America, and generally extending too far west with respect to observations. Thermocline variability is characterized by dominant modes that are qualitatively similar in all the models, and consistent with the “recharge oscillator” paradigm for ENSO. The meridional scale of the thermocline depth anomalies is generally narrower than observed, a result that can be related to the pattern of zonal wind stress perturbations in the central-western equatorial Pacific. The wind stress response to eastern equatorial Pacific SST anomalies in the models is narrower and displaced further west than observed. The meridional scale of the wind stress can affect the amount of warm water involved in the recharge/discharge of the equatorial thermocline, while the longitudinal location of the wind stress anomalies can influence the advection of the mean zonal temperature gradient by the anomalous zonal currents, a process that may favor the growth and longer duration of ENSO events when the wind stress perturbations are displaced eastwards. Thus, both discrepancies of the wind stress anomaly patterns in the coupled models with respect to observations (narrow meridional extent, and westward displacement along the equator) may be responsible for the ENSO timescale being shorter in the models than in observations. The examination of the leading advective processes in the SST tendency equation indicates that vertical advection of temperature anomalies tends to favor ENSO growth in all the CGCMs, but at a smaller rate than in observations. In some models it can also promote a phase transition. Longer periods tend to be associated with thermocline and advective feedbacks that are in phase with the SST anomalies, while advective tendencies that lead the SST anomalies by a quarter cycle favor ENSO transitions, thus leading to a shorter period.
The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.
Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.
The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.
Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).
Manuscript received 8 December 2004, in final form 18 March 2005
The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.
Since the mid-nineteenth century the Earth's surface has warmed1, 2, 3, and models indicate that human activities have caused part of the warming by altering the radiative balance of the atmosphere1, 3. Simple theories suggest that global warming will reduce the strength of the mean tropical atmospheric circulation4, 5. An important aspect of this tropical circulation is a large-scale zonal (east–west) overturning of air across the equatorial Pacific Ocean—driven by convection to the west and subsidence to the east—known as the Walker circulation6. Here we explore changes in tropical Pacific circulation since the mid-nineteenth century using observations and a suite of global climate model experiments. Observed Indo-Pacific sea level pressure reveals a weakening of the Walker circulation. The size of this trend is consistent with theoretical predictions, is accurately reproduced by climate model simulations and, within the climate models, is largely due to anthropogenic forcing. The climate model indicates that the weakened surface winds have altered the thermal structure and circulation of the tropical Pacific Ocean. These results support model projections of further weakening of tropical atmospheric circulation during the twenty-first century4, 5, 7.
We explore the extent to which stochastic atmospheric variability was fundamental to development of extreme sea surface temperature anomalies (SSTAs) during the 1997–8 El Niño. The observed western equatorial Pacific westerly zonal stress anomalies (τ a x ), which appeared between Nov. 1996 and May 1997 as a series of episodic bursts, were largely reproducible by an atmospheric general circulation model (AGCM) ensemble forced with observed SST. Retrospective forecasts using a hybrid coupled model (HCM) indicate that coupling only the part of τ a x linearly related to large-scale tropical Pacific SSTA is insufficient to capture the observed 1997 warming; but, accounting in the HCM for all the τ a x that was connected to SST, recovers most of the strong SSTA warming. The AGCM-estimated range of stochastic τ a x forcing induces substantial dispersion in the forecasts, but does not obscure the large-scale warming in most HCM ensemble members.
Multicentury integrations from two global coupled ocean–atmosphere–land–ice models [Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), developed at the Geophysical Fluid Dynamics Laboratory] are described in terms of their tropical Pacific climate and El Niño–Southern Oscillation (ENSO). The integrations are run without flux adjustments and provide generally realistic simulations of tropical Pacific climate. The observed annual-mean trade winds and precipitation, sea surface temperature, surface heat fluxes, surface currents, Equatorial Undercurrent, and subsurface thermal structure are well captured by the models. Some biases are evident, including a cold SST bias along the equator, a warm bias along the coast of South America, and a westward extension of the trade winds relative to observations. Along the equator, the models exhibit a robust, westward-propagating annual cycle of SST and zonal winds. During boreal spring, excessive rainfall south of the equator is linked to an unrealistic reversal of the simulated meridional winds in the east, and a stronger-than-observed semiannual signal is evident in the zonal winds and Equatorial Undercurrent.
Both CM2.0 and CM2.1 have a robust ENSO with multidecadal fluctuations in amplitude, an irregular period between 2 and 5 yr, and a distribution of SST anomalies that is skewed toward warm events as observed. The evolution of subsurface temperature and current anomalies is also quite realistic. However, the simulated SST anomalies are too strong, too weakly damped by surface heat fluxes, and not as clearly phase locked to the end of the calendar year as in observations. The simulated patterns of tropical Pacific SST, wind stress, and precipitation variability are displaced 20°–30° west of the observed patterns, as are the simulated ENSO teleconnections to wintertime 200-hPa heights over Canada and the northeastern Pacific Ocean. Despite this, the impacts of ENSO on summertime and wintertime precipitation outside the tropical Pacific appear to be well simulated. Impacts of the annual-mean biases on the simulated variability are discussed.
As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing.
A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980-2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF's utilization of anisotropic background error covariances that may vary in time.
for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2–LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to E1 Niño– Southern Oscillation (ENSO).
One AM2–LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2–LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2– LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden–Julian oscillation.
An ensemble of 10 AM2–LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2–LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.
Surface wind stresses are fundamental to understanding El Niño, yet most observational stress products are too short to permit multidecadal ENSO studies. Two exceptions are the Florida State University subjective analysis (FSU1) and the NCEP–NCAR reanalysis (NCEP1), which are widely used in climate research. Here, the focus is on the aspects of the stress most relevant to ENSO—namely, the climatological background, anomaly spectrum, response to SST changes, subannual "noise" forcing, and seasonal phase locking—and how these differ between FSU1 and NCEP1 over the tropical Pacific for 1961–99.
The NCEP1 stress climatology is distinguished from FSU1 by weaker equatorial easterlies, stronger off-equatorial cyclonic curl, stronger southerlies along the Peruvian coast, and weaker convergence zones with weaker seasonality. Compared to FSU1, the NCEP1 zonal stress anomalies (The NCEP1 stress climatology is distinguished from FSU1 by weaker equatorial easterlies, stronger off-equatorial cyclonic curl, stronger southerlies along the Peruvian coast, and weaker convergence zones with weaker seasonality. Compared to FSU1, the NCEP1 zonal stress anomalies (t'x) are weaker, less noisy, and show less persistent westerly peaks during El Niño events. NCEP1 also shows a more stationary spectrum that more closely resembles that of equatorial east Pacific SST anomalies. After the 1970s, the equatorial trade winds and stress variability shift east and strengthen in FSU1, while the opposite occurs in NCEP1. Both products show increased mean convergence in the equatorial far west Pacific in recent decades, with weaker mean easterlies near the date line, an increased stress response to SST anomalies, and stronger interannual and subannual t'x in the central equatorial Pacific (Niño-4; 5°N–5°S, 160°E–150°W). The variance of Niño-4 t'x is highly seasonal in both datasets, with an interannual peak in October–November and a subannual peak in November–February; yet apart from interannual Niño-4 t'x after 1980, stress anomalies are not well correlated between the products. Newer and more reliable stress estimates generally fall between NCEP1 and FSU1, with most closer to FSU1.
Time-stepping schemes in ocean-atmosphere models can involve multiple time levels. Traditional data assimilation implementation considers only the adjustment of the current state using observations available, i.e. the one time level adjustment. However, one time level adjustment introduces an inconsistency between the adjusted and unadjusted states into the model time integration, which can produce extra assimilation errors. For time-dependent assimilation approaches such as ensemble-based filtering algorithms, the persistent introduction of this inconsistency can give rise to computational instability and requires extra time filtering to maintain the assimilation.
A multiple time level adjustment assimilation scheme is thus proposed, in which the states at times t and t- 1, t- 2, ... , if applicable, are adjusted using observations at time t. Given a leap frog time-stepping scheme, a low-order (Lorenz-63) model and a simple atmospheric (global barotropic) model are used to demonstrate the impact of the two time level adjustment on assimilation results in a perfect model framework with observing/assimilation simulation experiments. The assimilation algorithms include an ensemble-based filter (the ensemble adjustment Kalman filter, EAKF) and a strong constraint four-dimensional variational (4D-Var) assimilation method. Results show that the two time level adjustment always reduces the assimilation errors for both filtering and variational algorithms due to the consistency of the adjusted states at times t and t- 1 that are used to produce the future state in the leap frog time-stepping. The magnitude of the error reduction made by the two time level adjustment varies according to the availability of observations, the nonlinearity of the assimilation model and the strength of the time filter used in the model. Generally the sparser the observations in time, the larger the error reduction. In particular, for the EAKF when the model uses a weak time filter and for the 4D-Var method when the model is strongly nonlinear, two time level adjustment can significantly improve the performance of these assimilation algorithms.
Nobody anticipated that El Niño would be weak and prolonged in 1992, but brief and intense in 1997/98. Why are various El Niño episodes so different, and so difficult to predict? The answer involves the important role played by random atmospheric disturbances (such as westerly wind bursts) in sustaining the weakly damped Southern Oscillation, whose complementary warm and cold phases are, respectively, El Niño and La Niña. As in the case of a damped pendulum sustained by modest blows at random times, so the predictability of El Niño is limited, not by the amplification of errors in initial conditions as in the case of weather, but mainly by atmospheric disturbances interacting with the Southern Oscillation. Given the statistics of the wind fluctuations, the probability distribution function of future sea surface temperature fluctuations in the eastern equatorial Pacific can be determined by means of an ensemble of calculations with a coupled ocean–atmosphere model. Each member of the ensemble starts from the same initial conditions and has, superimposed, a different realization of the noise. Such a prediction, made at the end of 1996, would have assigned a higher likelihood to a moderate event than to the extremely strong event that actually occurred in 1997. (The rapid succession of several westerly wind bursts in early 1997 was a relatively rare phenomenon.) In late 2001, conditions were similar to those in 1996, which suggested a relatively high probability of El Niño appearing in 2002. Whether the event will be weak or intense depends on the random disturbances that materialize during the year.
Wittenberg, Andrew T., 2002: ENSO response to altered climates, Ph.D. thesis, Princeton, NJ: Princeton University, 475pp. Abstract
Available from
http://www.gfdl.noaa.gov/~atw/research/thesis
Wittenberg, Andrew T., and Jeffrey L Anderson, 1998: Dynamical implications of prescribing part of a coupled system: Results from a low-order model. Nonlinear Processes in Geophysics, 5(3), 167-179. Abstract PDF
It is a common procedure in climate modeling to specify dynamical system components from an external source; a prominent example is the forcing of an atmospheric model with observed sea surface temperatures. In this paper, we examine the dynamics of such forced models using a simple prototype climate system. A particular fully-coupled run of the model is designated the "true" solution, and an ensemble of perturbed initial states is generated by adding small errors to the "true" initial state. The perturbed ensemble is then integrated for the same period as the true solution, using both the fully-coupled model and a model in which the ocean is prescribed exactly from the true solution at every time step. Although the prescribed forcing is error-free, the forced-atmosphere ensemble is shown to converge to spurious solutions. Statistical tests show that neither the time-mean state nor the variability of the forced ensemble is consistent with the fully-coupled system. A stability analysis reveals the source of the inconsistency, and suggests that such behavior may be a more general feature of models with prescribed subsystems. Possible implications for model validation and predictability are discussed.
