Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction.
Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.
A key challenge with the wind energy utilization is that winds, and thus wind power, are highly variable on seasonal to interannual timescales because of atmospheric variability. There is a growing need of skillful seasonal wind energy prediction for energy system planning and operation. Here we demonstrate model’s capability in producing skillful seasonal wind energy prediction over the U.S. Great Plains during peak energy seasons (winter and spring), using seasonal prediction products from a climate model. The dominant source of that skillful prediction mainly comes from year-to-year variations of El Niño-Southern Oscillation in the tropical Pacific, which alters large-scale wind and storm track patterns over the United States. In the Southern Great Plains, the model can predict strong year-to-year wind energy changes with high skill multiple months in advance. Thus, this seasonal wind energy prediction capability offers potential benefits for optimizing wind energy utilization during peak energy production seasons.
The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.
Skillful prediction of wintertime cold extremes on seasonal time scales is beneficial for multiple sectors. This study demonstrates that North American cold extremes, measured by the frequency of cold days in winter, are predictable several months in advance in the Geophysical Fluid Dynamics Laboratory’s SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Three predictable components of cold extremes over the North American continent are found to be skillfully predicted on seasonal scales. One is a trend-like component, which shows a continent-wide decrease in the frequency of cold extremes and is primarily attributable to external radiative forcing. This trend-like component is predictable at least 9 months ahead. The second predictable component displays a dipole structure over North America, with negative signs in the northwest and positive signs in the southeast. This dipole component is predictable with significant correlation skill for 2 months and is a response to the central Pacific ENSO (El Niño-Southern Oscillation) as revealed from SPEAR AMIP-style simulations. The third component with the largest loadings over Canada and the northern US shows significant correlations with snow anomalies over mid-to-high latitudes of the North American continent. Predictions using only the three predictable components yield higher/comparable skill relative to the SPEAR raw forecasts.
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.
Using a state-of-the-art coupled general circulation model, physical processes underlying Antarctic sea ice multidecadal variability and predictability are investigated. Our model simulations constrained by atmospheric reanalysis and observed sea surface temperature broadly capture a multidecadal variability in the observed sea ice extent (SIE) with a low sea ice state (late 1970s–1990s) and a high sea ice state (2000s–early 2010s), although the model overestimates the SIE decrease in the Weddell Sea around the 1980s. The low sea ice state is largely due to the deepening of the mixed layer and the associated deep convection that brings subsurface warm water to the surface. During the high sea ice period (post-2000s), the deep convection substantially weakens, so surface wind variability plays a greater role in the SIE variability. Decadal retrospective forecasts started from the above model simulations demonstrate that the Antarctic sea ice multidecadal variability can be skillfully predicted 6–10 years in advance, showing a moderate correlation with the observation. Ensemble members with a deeper mixed layer and stronger deep convection tend to predict a larger sea ice decrease in the 1980s, whereas members with a larger surface wind variability tend to predict a larger sea ice increase after the 2000s. Therefore, skillful simulation and prediction of the Antarctic sea ice multidecadal variability require accurate simulation and prediction of the mixed layer, deep convection, and surface wind variability in the model.
Polkova, Iuliia, Didier Swingedouw, Leon Hermanson, Armin Köhl, Detlef Stammer, Doug Smith, Jürgen Kröger, Ingo Bethke, Xiaosong Yang, and Liping Zhang, et al., December 2023: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre. Frontiers in Climate, 5, DOI:10.3389/fclim.2023.1273770. Abstract
Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.
The Model-Analogs technique is used in the present study to assess the decadal sea surface temperature (SST) prediction skill over the Southern Ocean (SO). The Model-Analogs here is based on reanalysis products and model control simulations that have ∼1° ocean/ice (refined to 0.5° at high latitudes) components and 100 km atmosphere/land components. It is found that the model analog hindcasts show comparable skills with the initialized retrospective decadal hindcasts south of 50°S, with even higher skills over the Weddell Sea at longer lead years. The high SST skills primarily arise from the successful capture of SO deep convection states. This deep ocean memory and the associated decadal predictability are also clearly seen when we assess the Model-Analogs technique in a perfect model context. Within 30°S–50°S latitudinal band, the model analog hindcasts show low skills. When we include the externally forced signals estimated from the large ensemble simulations, the model analog hindcasts and initialized decadal hindcasts show identical skills. The Model-Analogs method therefore provides a great baseline for developing future decadal forecast systems. It is unclear whether such analog techniques would also be successful with models that explicitly resolve ocean mesoscale eddies or other small-scale processes. This area of research needs to be explored further.
Long-term sea-level rise and multiyear to decadal sea level variations pose substantial risks for flooding and erosion in coastal communities. Here we use observations and climate model predictions to show that sea level variations along the U.S. East Coast are skillfully predictable 3 to 10 years in advance. The most predictable component of sea level is a basin scale upward trend, predictable a decade in advance and primarily a response to increasing greenhouse gases. Significant additional predictability comes from multidecadal variations of the Atlantic Meridional Overturning Circulation (AMOC). While perfect model simulations show AMOC-related sea level predictability of 5-7 years, model biases and initialization uncertainties reduce the realized predictive skill to 3-5 years, depending on location. Overall, greenhouse gas warming and predictable AMOC variations lead to multiyear to decadal prediction skill for sea level along the U.S. East Coast. Such skill could have significant societal benefit for planning and adaptation.
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.
Hermanson, Leon, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco J Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav Kharin, William J Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicolí, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam A Scaife, Mark A Collier, Terence O'Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang A Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Thomas L Delworth, Xiaosong Yang, and Fanrong Zeng, et al., April 2022: WMO global annual to decadal climate update: A prediction for 2021–25. Bulletin of the American Meteorological Society, 103(4), DOI:10.1175/BAMS-D-20-0311.1E1117-E1129. Abstract
As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.
The extension of seasonal to interannual prediction of the physical climate system to include the marine ecosystem has a great potential to inform marine resource management strategies. Along the east coast of Africa, recent findings suggest that skillful Earth system model (ESM)-based chlorophyll predictions may enable anticipation of fisheries fluctuations. The mechanisms underlying skillful chlorophyll predictions, however, were not identified, eroding confidence in potential adaptive management steps. This study demonstrates that skillful chlorophyll predictions up to two years in advance arise from the successful simulation of westward-propagating off-equatorial Rossby waves in the Indian ocean. Upwelling associated with these waves supplies nutrients to the surface layer for the large coastal areas by generating north- and southward propagating waves at the east African coast. Further analysis shows that the off-equatorial Rossby wave is initially excited by wind stress forcing caused by El Niño/Southern Oscillation-Indian Ocean teleconnections.
This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.
The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.
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.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
The rapid day-to-day temperature swings associated with extratropical storm tracks can cause cascading infrastructure failure and impact human outdoor activities, thus research on seasonal prediction and predictability of extreme temperature swings is of huge societal importance. To measure the extreme surface air temperature (SAT) variations associated with the winter extratropical storm tracks, a Temperature Swing Index (TSI) is formulated as the standard deviation of 24-h-difference-filtered data of the 6-hourly SAT. The dominant term governing the TSI variability is shown to be proportional to the product of eddy heat flux and mean temperature gradient. The seasonal prediction skill of the winter TSI over North America was assessed using Geophysical Fluid Dynamics Laboratory's new seasonal prediction system. The locations with skillful TSI prediction show a geographic pattern that is distinct from the pattern of skillful seasonal mean SAT prediction. The prediction of TSI provides additional predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of the El Niño-Southern Oscillation (ENSO), North Pacific Oscillation (NPO), and Pacific/North American (PNA) teleconnection. Over the central United States, the correlation skill of TSI prediction reaches 0.75 with strong links to observed ENSO, NPO, and PNA, while the skill of seasonal SAT prediction is relatively low with a correlation of 0.36. As a first attempt of diagnosing the combined predictability of the first-order (the seasonal mean) and second-order (TSI) statistics for SAT, this study highlights the importance of the eddy-mean flow interaction perspective for understanding the seasonal climate predictability in the extra tropics. These results point toward providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers.
One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.
The current GFDL seasonal prediction system, the Seamless System for Prediction and Earth System Research (SPEAR), has shown skillful prediction of Arctic sea ice extent with atmosphere and ocean constrained by observations. In this study we present improvements in subseasonal and seasonal predictions of Arctic sea ice by directly assimilating sea ice observations. The sea ice initial conditions from a data assimilation (DA) system that assimilates satellite sea ice concentration (SIC) observations are used to produce a set of reforecast experiments (IceDA) starting from the first day of each month from 1992 to 2017. Our evaluation of daily sea ice extent prediction skill concludes that the SPEAR system generally outperforms the anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on daily gridcell-level sea ice fields. SIC DA improves prediction skill of SIC forecasts prominently in the June-, July-, August-, and September-initialized reforecasts. We evaluate two additional user-oriented metrics: the ice-free probability (IFP) and ice-free date (IFD). IFP is the probability of a grid cell experiencing ice-free conditions in a given year, and IFD is the first date on which a grid cell is ice free. A combined analysis of IFP and IFD demonstrates that the SPEAR model can make skillful predictions of local ice melt as early as May, with modest improvements from SIC DA.
The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent.
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.
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.
Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi-decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR-A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR-A1 significantly outperforms CM2.1 in predicting multi-decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.
The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.
Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.
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.
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.
The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).
Smith, D M., Adam A Scaife, Rosie Eade, Panos Athanasiadis, A Bellucci, Ingo Bethke, Roberto Bilbao, L F Borchert, Louis-Philippe Caron, François Counillon, Gokhan Danabasoglu, Thomas L Delworth, Francisco J Doblas-Reyes, Nick Dunstone, V Estella-Perez, S Flavoni, Leon Hermanson, N Keenlyside, Viatcheslav Kharin, M Kimoto, William J Merryfield, J Mignot, T Mochizuki, K Modali, P-A Moneri, Wolfgang A Müller, Dario Nicolí, Pablo Ortega, Klaus Pankatz, Holger Pohlmann, J Robson, P Ruggieri, Reinel Sospedra-Alfonso, Didier Swingedouw, Yan Wang, S Wild, Stephen G Yeager, Xiaosong Yang, and Liping Zhang, July 2020: North Atlantic climate far more predictable than models imply. Nature, 583, DOI:10.1038/s41586-020-2525-0796-800. Abstract
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain. This leads to low confidence in regional projections, especially for precipitation, over the coming decades. The chaotic nature of the climate system may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.
Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea–ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system’s OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981–2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea–ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.
Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of Observing System Experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea-ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea-surface temperature (SST) satellite observations and subsurface conductivity-temperature-depth (CTD) measurements. The SST data is found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal timescales.
Climate variations have a profound impact on marine ecosystems and the communities that depend upon them. Anticipating ecosystem shifts using global Earth system models (ESMs) could enable communities to adapt to climate fluctuations and contribute to long-term ecosystem resilience. We show that newly developed ESM-based marine biogeochemical predictions can skillfully predict satellite-derived seasonal to multiannual chlorophyll fluctuations in many regions. Prediction skill arises primarily from successfully simulating the chlorophyll response to the El Niño–Southern Oscillation and capturing the winter reemergence of subsurface nutrient anomalies in the extratropics, which subsequently affect spring and summer chlorophyll concentrations. Further investigations suggest that interannual fish-catch variations in selected large marine ecosystems can be anticipated from predicted chlorophyll and sea surface temperature anomalies. This result, together with high predictability for other marine-resource–relevant biogeochemical properties (e.g., oxygen, primary production), suggests a role for ESM-based marine biogeochemical predictions in dynamic marine resource management efforts.
Most dust forecast models focus on a short, sub‐seasonal lead times, i.e., three to six days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the U.S. using an observation‐constrained regression model and key variables predicted by a seasonal prediction model developed at NOAA Geophysical Fluid Dynamics Laboratory, the Forecast‐Oriented Low Ocean Resolution (FLOR) Model.
Our method shows skillful predictions of spring dustiness three to six months in advance. It is found that the regression model explains about 71% of the variances of dust event frequency over the Great Plains and 63% over the southwestern U.S. in March‐May from 2004 to 2016 using predictors from FLOR initialized on December 1st. Variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors. Findings here will help development of a seasonal dust prediction system and hazard prevention.
Scaife, Adam A., L Ferranti, Oscar Alves, Panos Athanasiadis, J Baehr, M Dequé, T Dippe, Nick Dunstone, D Fereday, Richard G Gudgel, R J Greatbatch, Leon Hermanson, Yukiko Imada, S Jain, Arun Kumar, C MacLachlan, William J Merryfield, Wolfgang A Müller, Hong-Li Ren, Doug Smith, Yuhei Takaya, Gabriel A Vecchi, and Xiaosong Yang, February 2019: Tropical rainfall predictions from multiple seasonal forecast systems. International Journal of Climatology, 39(2), DOI:10.1002/joc.5855. Abstract
We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year‐to‐year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores.
We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter‐basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi‐model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter‐annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins.
These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter‐basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real‐time forecasts.
Smith, D M., Rosie Eade, Adam A Scaife, Louis-Philippe Caron, Gokhan Danabasoglu, T DelSole, Thomas L Delworth, Francisco J Doblas-Reyes, Nick Dunstone, Leon Hermanson, Viatcheslav Kharin, M Kimoto, William J Merryfield, T Mochizuki, Wolfgang A Müller, Holger Pohlmann, Stephen G Yeager, and Xiaosong Yang, May 2019: Robust skill of decadal climate predictions. npj Climate and Atmospheric Science, 2, 13, DOI:10.1038/s41612-019-0071-y. Abstract
There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.
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.
With a GFDL coupled model, the subseasonal prediction of wintertime (December‐February) surface air temperature (SAT) is investigated through the analysis of 11‐year hindcasts. Significant subseasonal week 3‐5 correlation skill exists over a large portion of the global land domain, and the predictability originates primarily from the eight most predictable SAT modes. The first three modes, identified as the El Niño‐Southern Oscillation mode, the North Atlantic Oscillation (NAO) mode, and the Eurasia Meridional Dipole (EMD) mode, can be skillfully predicted more than 5 weeks in advance. The NAO and EMD modes are strongly correlated with the initial stratospheric polar vortex strength, highlighting the role of stratosphere in subseasonal prediction. Interestingly, the Madden‐Julian Oscillation is not essential for the subseasonal land SAT prediction in the Northern Hemisphere extratropics. The spatial correlation skill exhibits considerable intraseasonal and interannual fluctuations, indicative of the importance to identify the time window of opportunity for subseasonal prediction.
Observed Southern Ocean surface cooling and sea-ice expansion over the past several decades are inconsistent with many historical simulations from climate models. Here we show that natural multidecadal variability involving Southern Ocean convection may have contributed strongly to the observed temperature and sea-ice trends. These observed trends are consistent with a particular phase of natural variability of the Southern Ocean as derived from climate model simulations. Ensembles of simulations are conducted starting from differing phases of this variability. The observed spatial pattern of trends is reproduced in simulations that start from an active phase of Southern Ocean convection. Simulations starting from a neutral phase do not reproduce the observed changes, similarly to the multimodel mean results of CMIP5 models. The long timescales associated with this natural variability show potential for skilful decadal prediction.
Improving the seasonal prediction of tropical cyclone (TC) activity demands a robust analysis of the prediction skill and the inherent predictability of TC activity. Using the resampling technique, this study analyzes a state‐of‐the‐art prediction system and offers a robust assessment of when and where the seasonal prediction of TC activity is skillful. We found that uncertainties of initial conditions affect the predictions and the skill evaluation significantly. The sensitivity of predictions to initial conditions also suggests that landfall and high‐latitude activity are inherently harder to predict. The lower predictability is consistent with the relatively low prediction skill in these regions. Additionally, the lower predictability is largely related to the atmospheric environment rather than the sea surface temperature, at least for the predictions initialized shortly before the hurricane season. These findings suggest the potential for improving the seasonal TC prediction and will help the development of the next‐generation prediction systems.
An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, “observations” drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the “identical” twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.
Mountain snowpack in the western United States provides a natural reservoir for cold season precipitation; variations in snowpack influence warm season water supply, wildfire risk, ecology, and industries like agriculture dependent on snow and downstream water availability. Efforts to understand snowpack variability have predominantly been focused on either weekly (weather) or decadal to centennial (climate variability and change) timescales. We focus on a timescale between these ranges by demonstrating that a global climate model suite can provide snowpack predictions 8 months in advance. The predictions from climate models outperform statistical methods from observations alone. Our results show that seasonal hydroclimate predictions are possible and highlight areas for future prediction system improvements.
Unprecedented high intensity flooding induced by extreme precipitation was reported over Chennai in India during November-December of 2015, which led to extensive damage to human life and property. It is of utmost importance to determine the odds of occurrence of such extreme floods in future and the related climate phenomena, for planning and mitigation purposes. Here, we make use of a suite of simulations from GFDL high-resolution coupled climate models to investigate the odds of occurrence of extreme floods induced by extreme precipitation over Chennai and the role of radiative forcing and/or large-scale SST forcing in enhancing the probability of such events in future. Climate of 20th century experiments with large ensembles suggest that the radiative forcing may not enhance the probability of extreme floods over Chennai. Doubling of CO2 experiments also fail to show evidence for increase of such events in a global warming scenario. Further, this study explores the role of SST forcing from the Indian and Pacific Oceans on the odds of occurrence of Chennai-like floods. Neither an El Niño nor La Niña enhances the probability of extreme floods over Chennai. However, warm Bay of Bengal tends to increase the odds of occurrence of extreme Chennai-like floods. The atmospheric condition such as a tropical depression over Bay of Bengal favoring the transport of moisture from warm Bay of Bengal is conducive for intense precipitation.
Extratropical transition can extend the threat of tropical cyclones into the mid‐latitudes, and modify it through expansion of rainfall and wind fields. Despite the scientific and socioeconomic interest, the seasonal forecast of extratropical transition has received little attention. The GFDL High‐Resolution Forecast‐Oriented Low Ocean Resolution (FLOR) model (HiFLOR) shows skill in seasonal forecasts of tropical cyclone frequency as well as major hurricanes. A July‐initialized twelve‐member ensemble retrospective seasonal forecast experiment with HiFLOR in the North Atlantic is conducted, representing one of the very first attempts to predict the extratropical transition activity months in advance. HiFLOR exhibits retrospective skill in seasonal forecasts of basin‐wide and regional ET activity relative to best track and reanalysis data. In contrast, the skill of HiFLOR in predictions of non‐ET activity is limited. Future work targeted at improved predictions of non‐ET storms provides a path for enhanced TC activity forecasting.
Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled-climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model-data weights that removed spurious biogeochemical signals while benefitting from off-equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.
Smith, D M., Adam A Scaife, E Hawkins, Roberto Bilbao, G J Boer, M Caian, Louis-Philippe Caron, Gokhan Danabasoglu, Thomas L Delworth, Francisco J Doblas-Reyes, R Doescher, Nick Dunstone, Rosie Eade, Leon Hermanson, Masao Ishii, Viatcheslav Kharin, M Kimoto, Torben Koenigk, Y Kushnir, D Matei, Gerald A Meehl, Martin Ménégoz, William J Merryfield, T Mochizuki, Wolfgang A Müller, Holger Pohlmann, Scott B Power, M Rixen, Reinel Sospedra-Alfonso, M Tuma, K Wyser, Xiaosong Yang, and Stephen G Yeager, November 2018: Predicted chance that global warming will temporarily exceed 1.5°C. Geophysical Research Letters, 45(21), DOI:10.1029/2018GL079362. Abstract
The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5oC above pre‐industrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5oC. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5oC above pre‐industrial levels in the coming five‐years. For the period 2017 to 2021 we predict a 38% and 10% chance respectively of monthly or yearly temperatures exceeding 1.5oC, with virtually no chance of the five‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events.
A “typical” El Niño leads to wet (dry) wintertime anomalies over the southern (northern) half of the Western United States (WUS). However, during the strong El Niño of 2015/16, the WUS winter precipitation pattern was roughly opposite to this canonical (average of the record) anomaly pattern. To understand why this happened, and whether it was predictable, we use a suite of high-resolution seasonal prediction experiments with coupled climate models. We find that the unusual 2015/16 precipitation pattern was predictable at zero-lead time horizon when the ocean/atmosphere/land components were initialized with observations. However, when the ocean alone is initialized the coupled model fails to predict the 2015/16 pattern, although ocean initial conditions alone can reproduce the observed WUS precipitation during the 1997/98 strong El Niño. Further observational analysis shows that the amplitudes of the El Niño induced tropical circulation anomalies during 2015/16 were weakened by about 50% relative to those of 1997/98. This was caused by relative cold (warm) anomalies in the eastern (western) tropical Pacific suppressing (enhancing) deep convection anomalies in the eastern (western) tropical Pacific during 2015/16. The reduced El Niño teleconnection led to a weakening of the subtropical westerly jet over the southeast North Pacific and southern WUS, resulting in the unusual 2015/16 winter precipitation pattern over the WUS. This study highlights the importance of initial conditions not only in the ocean, but in the land and atmosphere as well, for predicting the unusual El Niño teleconnection and its influence on the winter WUS precipitation anomalies during 2015/16.
Over the 1997-2014 period, the mean frequency of western North Pacific (WNP) tropical cyclones (TCs) was markedly lower (~18%) than the period 1980-1996. Here we show that these changes were driven by an intensification of the vertical wind shear in the southeastern/eastern WNP tied to the changes in the Walker circulation, which arose primarily in response to the enhanced sea surface temperature (SST) warming in the North Atlantic, while the SST anomalies associated with the negative phase of the Pacific Decadal Oscillation (PDO) in the tropical Pacific and the anthropogenic forcing play only secondary roles. These results are based on observations and experiments using the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented Low-ocean Resolution Coupled Climate Model (FLOR) coupled climate model. The present study suggests a crucial role of the North Atlantic SST in causing decadal changes to WNP TC frequency.
Due to its persistence on seasonal timescales, Arctic sea-ice thickness (SIT) is a potential source of predictability for summer sea-ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea-ice volume anomalies is studied using a 700-year control integration and a suite of initialized ensemble forecasts from a fully-coupled global climate model. Our analysis is focused on the September sea-ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea-ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea-ice concentration field, melt-onset date, snow coverage, and ice-thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice-albedo feedback processes in seasonal forecast systems.
Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea-ice extent (SIE). In this work, we move towards stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981–2015 made with a coupled atmosphere-ocean-sea ice-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent, and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea-ice thickness initial conditions provide a crucial source of skill for regional summer SIE.
The relationship between the North Atlantic Oscillation (NAO) and Atlantic sea surface temperature (SST) variability is investigated using models and observations. Coupled climate models are used in which the ocean component is either a fully dynamic ocean, or a slab ocean with no resolved ocean heat transport. On time scales less than ten years NAO variations drive a tripole pattern of SST anomalies in both observations and models. This SST pattern is a direct response of the ocean mixed layer to turbulent surface heat flux anomalies associated with the NAO.
On time scales longer than ten years a similar relationship exists between the NAO and the tripole pattern of SST anomalies in models with a slab ocean. A different relationship exists both for the observations and for models with a dynamic ocean. In these models a positive (negative) NAO anomaly leads, after a decadal-scale lag, to a monopole pattern of warming (cooling) that resembles the Atlantic Multidecadal Oscillation (AMO), although with smaller than observed amplitudes of tropical SST anomalies. Ocean dynamics are critical to this decadal scale response in the models. The simulated Atlantic Meridional Overturning Circulation (AMOC) strengthens (weakens) in response to a prolonged positive (negative) phase of the NAO, thereby enhancing (decreasing) poleward heat transport, leading to broad scale warming (cooling).
We use additional simulations in which heat flux anomalies derived from observed NAO variations from 1901 to 2014 are applied to the ocean component of coupled models. We show that ocean dynamics allow models to reproduce important aspects of the observed AMO, mainly in the subpolar gyre.
The wintertime Arctic temperature (T, surface-400 hPa) decreased from 1979-1997 and increased rapidly from 1998-2012, in contrast to the global mean surface air temperature. Here we examine aspects of circulation variability that are associated with these temperature changes using the NCEP/NCAR and ERA-Interim reanalysis products. It is found that the Nordic-Siberia seesaw of meridional winds near 70°N is associated with 2/3 of the variance of the Arctic winter mean T, possibly contributing to the cooling and warming trends. We suggest that the seesaw accounts for much of the difference in Arctic amplification between observations and climate models. Growth of sea ice in winter is hindered by southerly winds over the Nordic region (0°-60°E). Through modulation of the wind seesaw, the Eastern Atlantic (EA) pattern is found to be significantly associated with Arctic and East Asia winter climate variations. In one phase of the EA pattern, a mid-latitude North Atlantic ridge anomaly is associated with a poleward shift of the mean storm track, a weakened eddy-driven jet over Eurasia, and above normal sea-level pressures (SLP) over Siberia, most significantly in the region to the northwest of Lake Baikal. The EA pattern is associated with 2/3 of the variance of winter-average SLP over Siberia.
This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extra-tropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extra-tropical near surface land temperature. We show that most of the lead-0 month spring predictive skill of land temperature over extra-tropics, particularly over northern Eurasia, stems from stratospheric initialization. We further reveal that this predictive skill of extra-tropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.
This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple timescales. To illustrate the approach, three sets of 32-year-long simulations are analyzed for northeastern North America and for the March-May season using the Geophysical Fluid Dynamics Laboratory’s Low Ocean-Atmosphere Resolution (LOAR) and Forecast-oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach, and are associated with a misrepresentation of troughs centered north of the Great Lakes, and deep coastal troughs. Moreover, the intra-seasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, inter-experiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple timescales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.
Given knowledge at the time, the recent 2015–2016 zika virus (ZIKV) epidemic probably could not have been predicted. Without the prior knowledge of ZIKV being already present in South America, and given the lack of understanding of key epidemiologic processes and long-term records of ZIKV cases in the continent, the best related prediction could be carried out for the potential risk of a generic Aedes-borne disease epidemic. Here we use a recently published two-vector basic reproduction number model to assess the predictability of the conditions conducive to epidemics of diseases like zika, chikungunya, or dengue, transmitted by the independent or concurrent presence of Aedes aegypti and Aedes albopictus. We compare the potential risk of transmission forcing the model with the observed climate and with state-of-the-art operational forecasts from the North American Multi Model Ensemble (NMME), finding that the predictive skill of this new seasonal forecast system is highest for multiple countries in Latin America and the Caribbean during the December-February and March-May seasons, and slightly lower—but still of potential use to decision-makers—for the rest of the year. In particular, we find that above-normal suitable conditions for the occurrence of the zika epidemic at the beginning of 2015 could have been successfully predicted at least 1 month in advance for several zika hotspots, and in particular for Northeast Brazil: the heart of the epidemic. Nonetheless, the initiation and spread of an epidemic depends on the effect of multiple factors beyond climate conditions, and thus this type of approach must be considered as a guide and not as a formal predictive tool of vector-borne epidemics.
The 2015 hurricane season in the Eastern and Central Pacific Oceans (EPO and CPO), particularly around Hawaii, was extremely active – including a record number of tropical cyclones (TCs) and the first instance of three simultaneous Category 4 hurricanes in the EPO and CPO. A strong El Niño developed during the 2015 boreal summer season, and was attributed by some to be the cause of the extreme number of TCs. However, according to a suite of targeted high-resolution model experiments, the extreme 2015 EPO and CPO hurricane season was not primarily induced by the 2015 El Niño’s tropical Pacific warming, but by warming in the subtropical Pacific Ocean. This warming is not typical of El Niño, but rather the “Pacific Meridional Mode (PMM)” superimposed on long-term anthropogenic warming. Although the likelihood of such an extreme year depends on the phase of natural variability, the coupled GCM projects an increase in the frequency of such extremely active TC years over the next few decades for the EPO, CPO, and Hawaii due enhanced subtropical Pacific warming from anthropogenic greenhouse forcing.
Decisions made by fishers and fisheries managers are informed by climate and fisheries observations that now often span more than 50 years. Multi-annual climate forecasts could further inform such decisions if they were skillful in predicting future conditions relative to the 50-year scope of past variability. We demonstrate that an existing multi-annual prediction system skillfully forecasts the probability of next year, the next 1–3 years, and the next 1–10 years being warmer or cooler than the 50-year average at the surface in coastal ecosystems. Probabilistic forecasts of upper and lower seas surface temperature (SST) terciles over the next 3 or 10 years from the GFDL CM 2.1 10-member ensemble global prediction system showed significant improvements in skill over the use of a 50-year climatology for most Large Marine Ecosystems (LMEs) in the North Atlantic, the western Pacific, and Indian oceans. Through a comparison of the forecast skill of initialized and uninitialized hindcasts, we demonstrate that this skill is largely due to the predictable signature of radiative forcing changes over the 50-year timescale rather than prediction of evolving modes of climate variability. North Atlantic LMEs stood out as the only coastal regions where initialization significantly contributed to SST prediction skill at the 1 to 10 year scale.
The TAO/TRITON array is the cornerstone of the tropical Pacific and ENSO observing system. Motivated by the recent rapid decline of the TAO/TRITON array, the potential utility of TAO/TRITON was assessed for ENSO monitoring and prediction. The analysis focused on the period when observations from Argo floats were also available. We coordinated observing system experiments (OSEs) using the global ocean data assimilation system (GODAS) from the National Centers for Environmental Prediction and the ensemble coupled data assimilation (ECDA) from the Geophysical Fluid Dynamics Laboratory for the period 2004–2011. Four OSE simulations were conducted with inclusion of different subsets of in situ profiles: all profiles (XBT, moorings, Argo), all except the moorings, all except the Argo and no profiles. For evaluation of the OSE simulations, we examined the mean bias, standard deviation difference, root-mean-square difference (RMSD) and anomaly correlation against observations and objective analyses. Without assimilation of in situ observations, both GODAS and ECDA had large mean biases and RMSD in all variables. Assimilation of all in situ data significantly reduced mean biases and RMSD in all variables except zonal current at the equator. For GODAS, the mooring data is critical in constraining temperature in the eastern and northwestern tropical Pacific, while for ECDA both the mooring and Argo data is needed in constraining temperature in the western tropical Pacific. The Argo data is critical in constraining temperature in off-equatorial regions for both GODAS and ECDA. For constraining salinity, sea surface height and surface current analysis, the influence of Argo data was more pronounced. In addition, the salinity data from the TRITON buoys played an important role in constraining salinity in the western Pacific. GODAS was more sensitive to withholding Argo data in off-equatorial regions than ECDA because it relied on local observations to correct model biases and there were few XBT profiles in those regions. The results suggest that multiple ocean data assimilation systems should be used to assess sensitivity of ocean analyses to changes in the distribution of ocean observations to get more robust results that can guide the design of future tropical Pacific observing systems.
Xue, Y, C. Wen, Arun Kumar, Magdalena Alonso Balmaseda, Yosuke Fujii, Oscar Alves, M Martin, Xiaosong Yang, G Vernieres, C Desportes, Tong Lee, I Ascione, Richard G Gudgel, and I Ishikawa, December 2017: A real-time ocean reanalyses intercomparison project in the context of tropical pacific observing system and ENSO monitoring. Climate Dynamics, 49(11-12), DOI:10.1007/s00382-017-3535-y. Abstract
An ensemble of nine operational ocean reanalyses (ORAs) is now routinely collected, and is used to monitor the consistency across the tropical Pacific temperature analyses in real-time in support of ENSO monitoring, diagnostics, and prediction. The ensemble approach allows a more reliable estimate of the signal as well as an estimation of the noise among analyses. The real-time estimation of signal-to-noise ratio assists the prediction of ENSO. The ensemble approach also enables us to estimate the impact of the Tropical Pacific Observing System (TPOS) on the estimation of ENSO-related oceanic indicators. The ensemble mean is shown to have a better accuracy than individual ORAs, suggesting the ensemble approach is an effective tool to reduce uncertainties in temperature analysis for ENSO. The ensemble spread, as a measure of uncertainties in ORAs, is shown to be partially linked to the data counts of in situ observations. Despite the constraints by TPOS data, uncertainties in ORAs are still large in the northwestern tropical Pacific, in the SPCZ region, as well as in the central and northeastern tropical Pacific. The uncertainties in total temperature reduced significantly in 2015 due to the recovery of the TAO/TRITON array to approach the value before the TAO crisis in 2012. However, the uncertainties in anomalous temperature remained much higher than the pre-2012 value, probably due to uncertainties in the reference climatology. This highlights the importance of the long-term stability of the observing system for anomaly monitoring. The current data assimilation systems tend to constrain the solution very locally near the buoy sites, potentially damaging the larger-scale dynamical consistency. So there is an urgent need to improve data assimilation systems so that they can optimize the observation information from TPOS and contribute to improved ENSO prediction.
This study examines the year-to-year modulation of the western North Pacific (WNP) tropical cyclones (TC) activity by the Atlantic Meridional Mode (AMM) using both observations and the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution Version of CM2.5 (FLOR) global coupled model. 1. The positive (negative) AMM phase suppresses (enhances) WNP TC activity in observations. The anomalous occurrence of WNP TCs results mainly from changes in TC genesis in the southeastern part of the WNP. 2. The observed responses of WNP TC activity to the AMM are connected to the anomalous zonal vertical wind shear (ZVWS) caused by AMM-induced changes to the Walker circulation. During the positive AMM phase, the warming in the North Atlantic induces strong descending flow in the tropical eastern and central Pacific, which intensifies the Walker cell in the WNP. The intensified Walker cell is responsible for the suppressed (enhanced) TC genesis in the eastern (western) part of the WNP by strengthening (weakening) ZVWS. 3. The observed WNPTC–AMM linkage is examined by the long-term control and idealized perturbations experiment with FLOR-FA. A suite of sensitivity experiments strongly corroborate the observed WNPTC–AMM linkage and underlying physical mechanisms.
This study attempts to improve the prediction of western North Pacific (WNP) and East Asia (EA) landfalling tropical cyclones (TCs) using modes of large-scale climate variability [e.g., the Pacific meridional mode (PMM), the Atlantic meridional mode (AMM), and North Atlantic sea surface temperature anomalies (NASST)] as predictors in a hybrid statistical–dynamical scheme, based on dynamical model forecasts with the GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 with flux adjustments (FLOR-FA). Overall, the predictive skill of the hybrid model for the WNP TC frequency increases from lead month 5 (initialized in January) to lead month 0 (initialized in June) in terms of correlation coefficient and root-mean-square error (RMSE). The hybrid model outperforms FLOR-FA in predicting WNP TC frequency for all lead months. The predictive skill of the hybrid model improves as the forecast lead time decreases, with values of the correlation coefficient increasing from 0.56 for forecasts initialized in January to 0.69 in June. The hybrid models for landfalling TCs over the entire East Asian (EEA) coast and its three subregions [i.e., southern EA (SEA), middle EA (MEA), and northern EA (NEA)] dramatically outperform FLOR-FA. The correlation coefficient between predicted and observed TC landfall over SEA increases from 0.52 for forecasts initialized in January to 0.64 in June. The hybrid models substantially reduce the RMSE of landfalling TCs over SEA and EEA compared with FLOR-FA. This study suggests that the PMM and NASST/AMM can be used to improve statistical/hybrid forecast models for the frequencies of WNP or East Asia landfalling TCs.
This study explores the potential predictability of the Southern Ocean (SO) climate on decadal timescales as represented in the GFDL CM2.1 model using prognostic methods. We conduct perfect model predictability experiments starting from ten different initial states, and show potentially predictable variations of Antarctic bottom water formation (AABW) rates on time scales as long as twenty years. The associated Weddell Sea (WS) subsurface temperatures and Antarctic sea ice have comparable potential predictability as the AABW cell. The predictability of sea surface temperature (SST) variations over the WS and the SO is somewhat smaller, with predictable scales out to a decade. This reduced predictability is likely associated with stronger damping from air-sea interaction. As a complement to our perfect predictability study, we also make hindcasts of SO decadal variability using the GFDL CM2.1 decadal prediction system. Significant predictive skill for SO SST on multi-year time scales is found in the hindcast system. The success of the hindcasts, especially in reproducing observed surface cooling trends, is largely due to initializing the state of the AABW cell. A weak state of the AABW cell leads to cooler surface conditions and more extensive sea ice. Although there are considerable uncertainties regarding the observational data used to initialize the hindcasts, the consistency between the perfect model experiments and the decadal hindcasts at least gives us some indication as to where and to what extent skillful decadal SO forecasts might be possible.
Pronounced climate changes have occurred since the 1970s, including rapid loss of Arctic sea ice1, large-scale warming2 and increased tropical storm activity3 in the Atlantic. Anthropogenic radiative forcing is likely to have played a major role in these changes4, but the relative influence of anthropogenic forcing and natural variability is not well established. The above changes have also occurred during a period in which the North Atlantic Oscillation has shown marked multidecadal variations5. Here we investigate the role of the North Atlantic Oscillation in these rapid changes through its influence on the Atlantic meridional overturning circulation and ocean heat transport. We use climate models to show that observed multidecadal variations of the North Atlantic Oscillation can induce multidecadal variations in the Atlantic meridional overturning circulation and poleward ocean heat transport in the Atlantic, extending to the Arctic. Our results suggest that these variations have contributed to the rapid loss of Arctic sea ice, Northern Hemisphere warming, and changing Atlantic tropical storm activity, especially in the late 1990s and early 2000s. These multidecadal variations are superimposed on long-term anthropogenic forcing trends that are the dominant factor in long-term Arctic sea ice loss and hemispheric warming.
This study investigates the roles of radiative forcing, sea surface temperatures (SSTs), and atmospheric and land initial conditions in the summer warming episodes of the United States. The summer warming episodes are defined as the significantly above normal (1983-2012) June-August 2-m temperature anomalies, and are referred to as heat waves in this study. Two contrasting cases, the summers of 2006 and 2012, are explored in detail to illustrate the distinct roles of SSTs, direct radiative forcing, and atmospheric and land initial conditions in driving U.S. summer heat waves. For 2012, simulations with the GFDL atmospheric general circulation model reveal that SSTs play a critical role. Further sensitivity experiments reveal the contributions of uniform global SST warming, SSTs in individual ocean basins and direct radiative forcing to the geographic distribution and magnitudes of warm temperature anomalies. In contrast, for 2006, the atmospheric and land initial conditions are key drivers. The atmospheric (land) initial conditions play a major (minor) role in the central and northwestern (eastern) U.S.. Due to changes in radiative forcing, the probability of areal-averaged summer temperature anomalies over U.S. exceeding the observed 2012 anomaly increases with time over the early 21st century. La Niña (El Niño) events tend to increase (reduce) the occurrence rate of heat waves. The temperatures over the central U.S. are mostly influenced by El Niño/La Niña, with the central tropical Pacific playing a more important role than the eastern tropical Pacific. Thus, atmospheric and land initial conditions, SSTs and radiative forcing are all important drivers of, and sources of predictability for U.S. summer heat waves.
Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that has empirically-set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model which includes large-scale feedbacks for cumulus convection, and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.
Skillful seasonal forecasting of tropical cyclone (TC; wind speed ≥17.5 m s−1) activity is challenging, even more so when the focus is on major hurricanes (wind speed ≥49.4 m s−1), the most intense hurricanes (Category 4–5; wind speed ≥58.1 m s−1), and landfalling TCs. Here we show that a 25-km resolution global coupled model (HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL) has improved skill in predicting the frequencies of major hurricanes and Category 4–5 hurricanes in the North Atlantic, and landfalling TCs over the United States and Caribbean Islands a few months in advance, relative to its 50-km resolution predecessor climate model (FLOR). HiFLOR also shows significant skill in predicting Category 4–5 hurricanes in the western North Pacific and eastern North Pacific, while both models show comparable skills in predicting basin-total and landfalling TC frequency in the basins. The improved skillful forecasts of basin-total TCs, major hurricanes, and Category 4–5 hurricane activity in the North Atlantic by HiFLOR are obtained mainly by improved representation of the TCs and their response to climate from the increased horizontal resolution, rather than improvements in large-scale parameters.
Fujii, Yosuke, J Cummings, Y Xue, A Schiller, Tong Lee, Magdalena Alonso Balmaseda, E Remy, S Masuda, G Brassington, Oscar Alves, B Cornuelle, M Martin, P Oke, G Smith, and Xiaosong Yang, October 2015: Evaluation of the Tropical Pacific Observing System from the Ocean Data Assimilation Perspective. Quarterly Journal of the Royal Meteorological Society, 141(692), DOI:10.1002/qj.2579. Abstract
The dramatic reduction in the number of observation data from the Tropical Atmospheric Ocean (TAO)/ Triangle Trans-Ocean Buoy Network (TRITON) array since 2012 has given rise to a need to assess the impact of those data in ocean Data Assimilation (DA) systems. This paper provides a review of existing studies evaluating the impacts of data from the TAO/TRITON array and other components of the Tropical Pacific Observing System (TPOS) on current ocean DA systems used for a variety of operational and research applications. It can be considered as background information that can guide the evaluation exercise of TPOS. Temperature data from TAO/TRITON array are assimilated in most ocean DA systems which cover the tropical Pacific in order to constrain the ocean heat content, stratification and circulation. It is shown that the impacts of observation data depend considerably on the system and application. The presence of model error often makes the results difficult to interpret. Nevertheless there is consensus that the data from TAO/TRITON generally have positive impacts complementary to Argo floats. In the equatorial Pacific, the impacts are generally around the same level or larger than those of Argo. We therefore conclude that with the current configuration of TPOS, the loss of the TAO/TRITON data is having a significant detrimental impact on many applications based on ocean DA systems. This conclusion needs to be kept under review because the equatorial coverage by Argo is expected to improve in the future.
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.
Rosati, Anthony, Oscar Alves, Magdalena Alonso Balmaseda, Xiaosong Yang, and Yan Xue, 2015: Ocean data assimilation for ENSO prediction. U.S. CLIVAR Variations, 13(1), .
Sea surface temperature (SST) anomalies are often both leading indicators and important drivers of marine resource fluctuations. Assessment of the skill of SST anomaly forecasts within coastal ecosystems accounting for the majority of global fish yields, however, has been minimal. This reflects coarse global forecast system resolution and past emphasis on the predictability of ocean basin-scale SST variations. This paper assesses monthly to inter-annual SST anomaly predictions in coastal “Large Marine Ecosystems” (LMEs). We begin with an analysis of 7 well-observed LMEs adjacent to the United States and then examine how mechanisms responsible for prediction skill in these systems are reflected in predictions for LMEs globally. Historical SST anomaly estimates from the 1/4o daily Optimal Interpolation Sea Surface Temperature reanalysis (OISST.v2) were first found to be highly consistent with in-situ measurements for 6 of the 7 U.S. LMEs. Thirty years of retrospective forecasts from climate forecast systems developed at NOAA’s Geophysical Fluid Dynamics Laboratory (CM2.5-FLOR) and the National Center for Environmental Prediction (CFSv2) were then assessed against OISST.v2. Forecast skill varied widely by LME, initialization month, and lead but there were many cases of high skill that also exceeded that of a persistence forecast, some at leads greater than 6 months. Mechanisms underlying skill above persistence included accurate simulation of a) seasonal transitions between less predictable locally generated and more predictable basin-scale SST variability; b) seasonal transitions between different basin-scale influences; c) propagation of SST anomalies across seasons through sea ice; and d) re-emergence of previous anomalies upon the breakdown of summer stratification. Globally, significant skill above persistence across many tropical systems arises via mechanisms a) and b). Combinations of all four mechanisms contribute to less prevalent but nonetheless significant skill in extratropical systems. While continued refinement of global climate forecast systems and observations are needed to improve coastal SST anomaly prediction and extend predictions to other ecosystem relevant variables (e.g., salinity), present skill warrants close examination of forecasts for marine resource applications.
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.
This study examines two sets of high-resolution coupled model forecasts starting from no-tropical cyclone (TC) and correct-TC-statistics initial conditions to understand the role of TC events on climate prediction. While the model with no-TC initial conditions can quickly spin up TCs within a week, the initial conditions with a corrected TC distribution can produce more accurate forecast of sea surface temperature up to one and half months and maintain larger ocean heat content up to 6 months due to enhanced mixing from continuous interactions between initialized and forecasted TCs and the evolving ocean states. The TC-enhanced tropical ocean mixing strengthens the meridional heat transport in the Southern Hemisphere driven primarily by Southern Ocean surface Ekman fluxes but weakens the Northern Hemisphere poleward transport in this model. This study suggests a future plausible initialization procedure for seamless weather-climate prediction when individual convection-permitting cyclone initialization is incorporated into this TC-statistics-permitting framework.
There have been few attempts to quantify errors in various objective analyzed (OA) fields, even though they have potential uncertainties associated with data handling and mapping methods. Here, we compare five different OA fields (EN3, GFDL, IPRC, JAMSTEC, and SIO) for 2008–2011. The variability and linear trends of the upper ocean temperature are very similar in every ocean basin, but the mean values are different from each other. This discrepancy is evident, especially around the southern ocean (± 0.07 °C in the Antarctic Ocean) where Argo observations are still sparse, which is related to different first-guess climatologies and decorrelation length scales applied to individual OA products. In the subpolar North Atlantic, detailed spatial anomalous patterns are also different. Along the boundary current areas, substantial warming (salting) anomalies with respect to WOA09 climatology are depicted by GFDL, IPRC, and SIO. By comparing with statistical bin-averaged fields and data assimilation products, we confirm that this anomalous pattern is robust, but it could be exaggerated when we calculate the anomalies with WOA09 climatology or other OA fields showing a relatively weak horizontal gradient across the boundary current regions.
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.
Given a biased coupled model and the atmospheric and oceanic observing system, how to maintain balanced and coherent climate estimation is of critical importance for producing accurate climate analysis and prediction initialization. However, due to limitation of the observing system (most of the oceanic measurements are only available for the upper ocean, for instance), directly evaluating climate estimation with real observations is difficult. With two coupled models which are biased with respect to each other, a “biased” twin experiment is designed to simulate the problem. To do that, the atmospheric and oceanic “observations” drawn from one model based on the modern climate observing system are assimilated into the other. The model that produces “observations” serves as the “truth” and the degree by which an assimilation recovers the “truth” steadily and coherently is an assessment of the impact of the data constraint scheme on climate estimation. Given the assimilation model bias of warmer atmosphere and colder ocean, while the atmospheric-only (oceanic-only) data constraint produces an over-cooling (over-warming) ocean through the atmosphere-ocean interaction, the constraints with both atmospheric and oceanic data create a balanced and coherent ocean estimate as the observational model. Moreover, the consistent atmosphere-ocean constraint produces the most accurate estimate for North Atlantic Deep Water (NADW), while NADW is too strong (weak) as the system is only constrained by atmospheric (oceanic) data. These twin experiment results provide insights that consistent data constraints of multiple components are very important when a coupled model is combined with the climate observing system for climate estimation and prediction initialization.
When observations are assimilated into a high-resolution coupled model, a traditional scheme that preferably projects observations to correct large scale background tends to filter out small scale cyclones. Here we separately process the large scale background and small scale perturbations with low-resolution observations for reconstructing historical cyclone statistics in a cyclone-permitting model. We show that by maintaining the interactions between small scale perturbations and successively-corrected large scale background, a model can successfully retrieve the observed cyclone statistics that in return improve estimated ocean states. The improved ocean initial conditions together with the continuous interactions of cyclones and background flows are expected to reduce model forecast errors. Combined with convection-permitting cyclone initialization, the new high-resolution model initialization along with the progressively-advanced coupled models should contribute significantly to the ongoing research on seamless weather-climate predictions.
DelSole, T, Xiaosong Yang, and Michael K Tippett, January 2013: Is unequal weighting significantly better than equal weighting for multi-model forecasting?Quarterly Journal of the Royal Meteorological Society, 139(670), DOI:10.1002/qj.1961. Abstract
This article proposes a statistical test for whether a multi-model combination with
unequal weights has significantly smaller errors than a combination with equal
weights. A combination with equal weights includes the case of a no-skill model,
in which all weights equal zero, and the multi-model mean, in which all weights
equal 1/M, where M is the number of models. The test is applied to seasonal
hindcasts of 2m temperature and precipitation generated by five state-of-the-art
coupled atmosphere–ocean models. The hypothesis of equal weights could not be
rejected over 75% the globe for temperature and 90% of the land for precipitation,
implying that strategies for unequal weighting of forecasts may be of value only
over a relatively small fraction of the globe. The fact that the test does not require
pre-specifying a specific strategy for weighting forecasts suggests that it should be
useful for exploring a wide range of multi-model strategies.
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.
Yang, Xiaosong, and T DelSole, January 2012: Systematic Comparison of ENSO Teleconnection Patterns between Models and Observations. Journal of Climate, 25(2), DOI:10.1175/JCLI-D-11-00175.1. Abstract
This paper applies a new field significance test to establish the existence and consistency of ENSO teleconnection patterns across models and observations. An ENSO teleconnection pattern is defined as a field of regression coefficients between an index of the tropical Pacific sea surface temperature and a field of variables such as surface air temperature or precipitation. The test is applied to boreal winter and summer in six continents using observations and hindcasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. This comparison represents one of the most comprehensive and up-to-date assessments of the extent to which ENSO teleconnection patterns exist and can be reproduced by coupled models.
Statistically significant ENSO teleconnection patterns are detected in both observations and models and in all continents and in both winter and summer seasons, except in two cases: 1) Europe (both seasons and variables), and 2) North America (both variables in boreal summer). Despite many ENSO teleconnection patterns being significant, however, the patterns do not necessarily agree between observations and models. The degree of agreement between models and observations is characterized as “robust,” “moderate,” or “low.” Only Australia and South America are found to have robust agreement between ENSO teleconnection patterns, and then only for limited seasons and variables. Although many of our conclusions regarding teleconnection patterns conform to previous studies, there are exceptions, including the fact that the teleconnection for boreal winter precipitation is generally accepted to exist in Africa but in fact has only low agreement with model simulations, while that in Asia is not widely recognized to exist but is found to be significant and in moderate agreement with model teleconnections.
