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Antarctic shelf ocean warming affects melt of ice shelf/sheets and sea ice but projected changes vary vastly across climate models. A projected increase in El Niño variability has been found to slow future mid-latitude Southern Ocean warming but how this impacts the Antarctic shelf ocean is unknown. Here we show that a projected increase in El Niño variability accelerates Antarctic shelf ocean warming, hastening ice shelf/sheet melt but slowing sea ice reduction.

Tropical cyclones (TCs) alter upper-ocean temperature and influence ocean heat content via enhanced turbulent mixing. A better understanding of the role of TCs within the climate system requires a fully coupled modeling framework, where TC-induced ocean responses feed back to the atmosphere and subsequently to the climate mean state and variability. Here, we investigate the impacts of TC wind forcing on the global ocean and the associated feedbacks within the climate system using the fully coupled Community Earth System Model version 1.3 (CESM1.3). Using the low-resolution version of CESM1.3 (1° atmosphere and ocean grid spacing) with no intrinsic TCs, we conduct a suite of sensitivity experiments by inserting TC winds extracted from a high-resolution (0.25° atmosphere grid spacing) TC-permitting simulation into the low-resolution model. Results from the low-resolution TC experiment are compared to a low-resolution control simulation to diagnose TCs’ impact. We found that the added TC winds can increase ocean heat content by affecting ocean vertical mixing, air–sea enthalpy fluxes, and cloud amount. The added TCs can influence mean SST, precipitation, ocean subsurface temperature, and ocean mixed layer depth. We found a strengthening of the wind-driven subtropical cells and a weakening of the Atlantic meridional overturning circulation due to the changes of surface buoyancy fluxes. TCs in the model cause anomalous equatorward ocean heat convergence in the deep tropics and an increase of poleward ocean heat transport out of the subtropics. Our modeling results provide new insights into the multiscale interactions between TCs and the coupled climate system.

The Energy Exascale Earth System Model version 2 (E3SMv2) was publicly released by the Department of Energy (DoE) in September 2021. An important component of the model validation were historical climate simulations that followed the protocols of the Coupled Model Intercomparison Project Phase 6 (CMIP6). In particular, five historical ensemble members were recently released, and 16 additional ensemble members will soon become available as part of an E3SMv2 Large Ensemble (LE). The paper sheds light on the characteristics of the E3SMv2 CMIP6 stratospheric circulation which has not yet been documented in the literature. Particular attention is paid to the tropical stratosphere which includes the so-called water vapor tape recorder and the Quasi-Biennial Oscillation. In addition, the general circulation and its variability are briefly described to reveal E3SMv2’s strengths and weaknesses. We compare E3SMv2’s circulation to observations and ERA5 reanalysis data. Furthermore, selected comparisons to the predecessor version E3SMv1 as well as other CMIP6 models are provided to put the results into context. The analysis informs the DoE Sandia National Laboratories project CLDERA which uses the Mt. Pinatubo volcanic eruption for climate attribution studies.

The Intergovernmental Panel on Climate Change (IPCC) consists of about 190 governments that commission assessments performed by the international climate science community to determine the current state of human knowledge of climate and climate change. As such, the IPCC does not perform scientific research, but, rather, assesses research in the form of published papers addressing topics in climate science related to climate variability and change. However, as the IPCC assessments have evolved (from the first in 1990 to the sixth in 2021, so far), the IPCC has formed a symbiotic relationship with climate science. Even though the goal of the IPCC is to assess the scientific research that is taking place, its high profile, prestige, and interest from governments that fund climate science research has stimulated and arguably accelerated climate science research. This is particularly relevant for Earth system modeling (including the physical climate system plus the biogeochemical components) that will be addressed here to illustrate the influence of IPCC on climate science. One outcome is that enhanced observations of the Earth system from a number of field campaigns have been funded by countries to gather targeted observations to improve the understanding of crucial processes that need to be represented with fidelity in Earth system models. Governments that fund Earth system modeling research want to have results from their model appear prominently in the IPCC assessments to partially justify the funds being spent on developing, running, and analyzing these models. And just as important as getting a model into the IPCC assessment process are the analyses of the model outputs done by the scientists in the modeling groups and other scientists around the world. The products of this process are the papers describing cutting-edge results that use the models to advance knowledge of climate variability and change. Therefore, model developers are competing with other modeling groups around the world to have the best possible models producing climate simulations that are analyzed to produce papers of the highest quality that are assessed in the IPCC reports. An important part of this process is the international scientific coordination provided by the World Climate Research Programme’s Coupled Model Intercomparison Project (CMIP). CMIP involves climate scientists from around the world who volunteer their time to organize CMIP while also developing climate models in their respective countries. CMIP started in the mid-1990s for modeling groups to run exactly the same experiments so the response across the models could be directly compared to quantify uncertainty in their simulations of historical and future climate. Because these climate experiments are, by construction, the current state-of-the-art in climate modeling with the best representation of human understanding of the workings of the climate system, the papers that are written based on those model integrations are of primary interest for the IPCC assessments. CMIP has since evolved to include numerous climate science communities that interface with the modeling groups to perform model intercomparison projects to address various compelling climate science problems. Thus, there is a symbiosis between climate science/modeling, the scientific framework provided by CMIP for coordinated climate change experiments, and the IPCC process that assesses papers that emerge from the scientific research done by scientists who desire their work to be featured in those prestigious IPCC assessments.

Volcanic aerosol forcing can affect global climate, but its role in climate prediction remains poorly understood. We isolate the impact of volcanic eruptions on multiyear-to-decadal climate prediction skill by comparing two suites of initialized decadal hindcasts conducted with and without historical volcanic forcing. Unexpectedly, the inclusion of volcanic forcing in the prediction system significantly degrades the forecast skill of detrended multiyear-to-decadal sea surface temperature (SST) variability in the central-eastern tropical Pacific. The ensemble mean hindcasts produce multiyear-to-decadal tropical Pacific SST cooling in response to large tropical volcanic eruptions through thermodynamic and El Niño–Southern Oscillation (ENSO)–like dynamic processes. However, in observations, these eruptions coincided with tropical Pacific warming, which is well predicted by the no-volcano hindcasts and, hence, is likely related to internal climate variability. Improved model representation of volcanic response and its interaction with internal climate variability is required to advance prediction of tropical Pacific decadal variability and associated global impacts.

This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k-means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific Decadal Varibility (PDV) and the Atlantic Multidecadal Variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic-Pacific interactions and investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV on PDV when analysing the complete 1900–2014 period, and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV on AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, Large Ensemble (LE) simulations showed better network similarity when PDV and AMV are out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistics metrics of climate variability to provide a powerful tool for climate model evaluation.

Consequences from a slowdown or collapse of the Atlantic meridional overturning circulation (AMOC) could include modulations to El Niño–Southern Oscillation (ENSO) and development of the Pacific meridional overturning circulation (PMOC). Despite potential ramifications to the global climate, our understanding of the influence of various AMOC and PMOC states on ENSO and global sea surface temperatures (SSTs) remains limited. Five multicentennial, fully coupled model simulations created with the Community Earth System Model were used to explore the influence of AMOC and PMOC on global SSTs and ENSO. We found that the amplitude of annual cycle SSTs across the tropical Pacific decreases and ENSO amplitude increases as a result of an AMOC shutdown, irrespective of PMOC development. However, active deep overturning circulations in both the Atlantic and Pacific basins reduce ENSO amplitude and variance of monthly SSTs globally. The underlying physical reasons for changes to global SSTs and ENSO are also discussed, with the atmospheric and oceanic mechanisms that drive changes to ENSO amplitude differing based on PMOC state. These results suggest that if climate simulations projecting AMOC weakening are realized, compounding climate impacts could occur given the far-reaching ENSO teleconnections to extreme weather and climate events. More broadly, these results provide us with insight into past geologic era climate states, when PMOC was active.

The subtropical oceans between 35°-20°S in the Southern Hemisphere (SH) have exhibited prevailingly rapid sea-level rise (SLR) rates since the mid-20th century, amplifying damages of coastal hazards and exerting increasing threats to South America, Africa, and Australia. Yet, mechanisms of the observed SLR have not been firmly established, and its representation in climate models has not been examined. By analyzing observational sea-level estimates, ocean reanalysis products, and ocean model hindcasts, we show that the steric SLR of the SH subtropical oceans between 35°-20°S is faster than the global mean rate by 18.2%±9.9% during 1958-2014. However, present climate models—the fundamental bases for future climate projections—generally fail to reproduce this feature. Further analysis suggests that the rapid SLR in the SH subtropical oceans is primarily attributable to the persistent upward trend of the Southern Annular Mode (SAM). Physically, this trend in SAM leads to the strengthening of the SH subtropical highs, with the strongest signatures observed in the southern Indian Ocean. These changes in atmospheric circulation promote regional SLR in the SH subtropics by driving upper-ocean convergence. Climate models show systematic biases in the simulated structure and trend magnitude of SAM and significantly underestimate the enhancement of subtropical highs. These biases lead to the inability of models to correctly simulate the observed subtropical SLR. This work highlights the paramount necessity of reducing model biases to provide reliable regional sea-level projections.

Author(s): Li, X; Cai, W; Meehl, GA; Chen, D; Yuan, X; Raphael, M; Holland, DM; Ding, Q; Fogt, RL; Markle, BR; Wang, G; Bromwich, DH; Turner, J; Xie, SP; Steig, EJ; Gille, ST; Xiao, C; Wu, B; Lazzara, MA; Chen, X; Stammerjohn, S; Holland, PR; Holland, MM; Cheng, X; Price, SF; Wang, Z; Bitz, CM; Shi, J; Gerber, EP; Liang, X; Goosse, H; Yoo, C; Ding, M; Geng, L; Xin, M; Li, C; Dou, T; Liu, C; Sun, W; Wang, X; Song, C | Abstract: Over the modern satellite era, substantial climatic changes have been observed in the Antarctic, including atmospheric and oceanic warming, ice sheet thinning and a general Antarctic-wide expansion of sea ice, followed by a more recent rapid loss. Although these changes, featuring strong zonal asymmetry, are partially influenced by increasing greenhouse gas emissions and stratospheric ozone depletion, tropical–polar teleconnections are believed to have a role through Rossby wave dynamics. In this Review, we synthesize understanding of tropical teleconnections to the Southern Hemisphere extratropics arising from the El Nino–Southern Oscillation, Interdecadal Pacific Oscillation and Atlantic Multidecadal Oscillation, focusing on the mechanisms and long-term climatic impacts. These teleconnections have contributed to observed Antarctic and Southern Ocean changes, including regional rapid surface warming, pre-2015 sea ice expansion and its sudden reduction thereafter, changes in ocean heat content and accelerated thinning of most of the Antarctic ice sheet. However, due to limited observations and inherent model biases, uncertainties remain in understanding and assessing the importance of these teleconnections versus those arising from greenhouse gases, ozone recovery and internal variability. Sustained pan-Antarctic efforts towards long-term observations, and more realistic dynamics and parameterizations in high-resolution climate models, offer opportunities to reduce these uncertainties.

The build-up of decadal timescale upper ocean heat content in the off-equatorial western tropical Pacific can provide necessary conditions for interannual El Nino/Southern Oscillation (ENSO) events to contribute to decadal timescale transitions of tropical Pacific SSTs to the opposite phase of the Interdecadal Pacific Oscillation (IPO). This can be viewed as a corollary to subseasonal westerly wind burst events contributing to El Nino interannual timescale transitions. A long pre-industrial control run with CESM1 is analyzed to show that there is a greater chance of ENSO activity to contribute to an IPO transition when off-equatorial western Pacific Ocean heat content reaches either a maximum (for El Nino to contribute to a transition to positive IPO) or minimum (for La Nina to contribute to a transition to negative IPO) as seen in observations. If above a necessary ocean heat content threshold, the convergence associated with westerly anomaly near-equatorial surface winds associated with El Nino activity can draw that heat content equatorward to sustain anomalously warm western and central Pacific SSTs. These are associated with positive precipitation and convective heating anomalies, a Gill-type response and wind stress curl anomalies that continue to feed warm water into the near-equatorial western Pacific. These conditions then sustain the decadal-timescale transition to positive IPO (with the opposite sign for transition to negative IPO). Associated central equatorial Pacific convective heating anomalies produce SLP and wind stress anomalies in the North and South Pacific that can excite westward-propagating off-equatorial oceanic Rossby waves to contribute to the western Pacific thermocline depth and consequent heat content anomalies.

Continuous emissions of anthropogenic greenhouse gases (GHGs) and aerosols in the last 160 years have resulted in an increasing trend of global mean surface temperatures (GMSTs). Due to interactions with natural variability, rates of the combined anthropogenically and naturally induced warming trends are characterized by significant slowdowns and speedups on decadal timescales. Here, by analyzing observed and model-simulated data, we investigate how the duration of these episodes will change with different strengths of GHG and aerosol forcing. We found that the duration of warming slowdowns can be more than 30 yr with a slower rate of anthropogenic emissions but would shorten to about 5 yr with a higher one. This duration reduction depends on both the magnitude of the climate response to anthropogenic forcing and the strength of the internal variability. Moreover, the warming slowdowns can still occur even towards the end of this century under high emissions scenarios but with significantly shortened duration.

Decadal climate prediction presumes there are decadal-timescale processes and mechanisms that, if initialized properly in models, potentially provide predictive skill more than one or two years into the future. Candidate mechanisms involve Pacific decadal variability and Atlantic multidecadal variability, elements of which involve slow fluctuations of tropical Pacific and Atlantic sea surface temperatures (SSTs) from positive anomalies (positive phase) to negative anomalies (negative phase). Here we use model experiments to show that there tends to be a weak opposite-sign SST response in the tropical Pacific when observed SSTs are specified in the Atlantic, while there is a weak same-sign SST response in the tropical Atlantic when observed SSTs are specified in the tropical Pacific. Net surface heat flux in the Atlantic and ocean dynamics in the Pacific play contrasting roles in the ocean response to specified SSTs in the respective basins. We propose that processes in the Pacific and Atlantic are sequentially interactive through the atmospheric Walker circulation along with contributions from midlatitude teleconnections for the Atlantic response to the Pacific. Atmospheric Walker circulation results in a two-way interaction between decadal-scale sea surface temperature variability in the Atlantic and Pacific, according to pacemaker climate modelling experiments.

The Southern Hemisphere summertime eddy-driven jet and storm tracks have shifted poleward over the recent few decades. In previous studies, explanations have mainly stressed the influence of external forcing in driving this trend. Here we examine the role of internal tropical SST variability in controlling the austral summer jet’s poleward migration, with a focus on interdecadal time scales. The role of external forcing and internal variability are isolated by using a hierarchy of Community Earth System Model version 1 (CESM1) simulations, including the pre-industrial control, large ensemble, and pacemaker runs. Model simulations suggest that in the early twenty-first century, both external forcing and internal tropical Pacific SST variability are important in driving a positive southern annular mode (SAM) phase and a poleward migration of the eddy-driven jet. Tropical Pacific SST variability, associated with the negative phase of the interdecadal Pacific oscillation (IPO), acts to shift the jet poleward over the southern Indian and southwestern Pacific Oceans and intensify the jet in the southeastern Pacific basin, while external forcing drives a significant poleward jet shift in the South Atlantic basin. In response to both external forcing and decadal Pacific SST variability, the transient eddy momentum flux convergence belt in the middle latitudes experiences a poleward migration due to the enhanced meridional temperature gradient, leading to a zonally symmetric southward migration of the eddy-driven jet. This mechanism distinguishes the influence of the IPO on the midlatitude circulation from the dynamical impact of ENSO, with the latter mainly promoting the subtropical wave-breaking critical latitude poleward and pushing the midlatitude jet to higher latitudes.

Despite the observed monotonic increase in greenhouse-gas concentrations, global mean temperature displays important decadal fluctuations typically attributed to both external forcing and internal variability. Here, we provide a robust quantification of the relative contributions of anthropogenic, natural, and internally-driven decadal variability of global mean sea surface temperature (GMSST) by using a unique dataset consisting of 30-member large initial-condition ensembles with five Earth System Models (ESM-LE). We present evidence that a large fraction (~29–53%) of the simulated decadal-scale variance in individual timeseries of GMSST over 1950–2010 is externally forced and largely linked to the representation of volcanic aerosols. Comparison with the future (2010–2070) period suggests that external forcing provides a source of additional decadal-scale variability in the historical period. Given the unpredictable nature of future volcanic aerosol forcing, it is suggested that a large portion of decadal GMSST variability might not be predictable., Global mean sea surface surface temperature shows decadal fluctuations superimposed to the warming trend whose causes are still debated. Here, the authors provide a quantification of relative contributions of different drivers and conclude that both internal and externally-forced variability play a comparable role.

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction. Flash droughts, which develop over the course of weeks, are difficult to forecast given the current state of subseasonal-to-seasonal prediction. This Perspective offers operational and research definitions, places them in the broader context of climate and suggests avenues for future research.

In initialized seasonal to decadal (S2D) predictions, model hindcasts rapidly drift away from the initial observed state and converge toward a preferred state characterized by systematic error, or bias. Bias and drift are among the greatest challenges facing initialized prediction today. Differences in trends between initial states and drifted states, combined with bias and drift, introduce complexities in calculating anomalies to assess skill of initialized predictions. We examine several methods of calculating anomalies using the Decadal Prediction Large Ensemble (DPLE) using the Community Earth System Model (CESM) initialized hindcasts and focus on Pacific and Atlantic SSTs to illustrate issues with anomaly calculations. Three methods of computing anomalies, one as differences from a long term model climatology, another as bias-adjusted differences from the previous 15 year average from observations, and a third as differences from the previous 15 year average from the model, are contrasted and each is shown to have limitations. For the first, trends in bias and drift introduce higher skill estimates earlier and later in the hindcast period due to the trends that contribute to skill. For the second, higher skill can be introduced in situations where low frequency variability in the observations is large compared to the hindcasts on timescales greater than 15 years, while lower skill can result if the predicted signal is small and the bias-correction itself produces a transition of SST anomalies to the opposite sign of those that are observed. The third method has somewhat lower skill compared to each of the others, but has less difficulties with not only the long term trends in the model climatology, but also with the unrealistic situational skill from using observations as a reference. However, the first 15 years of the hindcast period cannot be evaluated due to having to wait to accumulate the previous 15 year model climatology before the method can be applied. The IPO transition in the 2014–2016 time frame from negative to positive (predicted by Meehl et al. in in Nat Commun, 10.1038/NCOMMS11718, 2016) did indeed verify using all three methods, though each provides somewhat different skill values as a result of the respective limitations. There is no clear best method, as all are roughly comparable, and each has its own set of limitations and caveats. However, all three methods show generally higher overall skill in the AMO region compared to the IPO region. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S.Department of Energy's Office of Biological & Environmental Research (BER) via National Science Foundation IA 1947282, and under Award Number DE-SC0022070. This work also was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement No.1852977

International audience; Climate variability in the tropical Pacific affects global climate on a wide range of time scales. On interannual time scales, the tropical Pacific is home to the El Niño–Southern Oscillation (ENSO). Decadal variations and changes in the tropical Pacific, referred to here collectively as tropical Pacific decadal variability (TPDV), also profoundly affect the climate system. Here, we use TPDV to refer to any form of decadal climate variability or change that occurs in the atmosphere, the ocean, and over land within the tropical Pacific. “Decadal,” which we use in a broad sense to encompass multiyear through multidecadal time scales, includes variability about the mean state on decadal time scales, externally forced mean-state changes that unfold on decadal time scales, and decadal variations in the behavior of higher-frequency modes like ENSO.

Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts, and biases, this paper presents a novel data set developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This data set has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology, that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions. The authors would like to thank the two anonymous reviewers, whose suggestions helped improve and clarify several aspects of the manuscript, as well as Editor Dr. Stephen Griffies. In addition, Hai Lin and Yuhei Takaya provided helpful comments about the behavior of the ECCC-S2S and JMA-S2S models, respectively. The assistance of Marina Trubina in constructing S2S hindcast climatologies for the LRFTIP data set is also kindly acknowledged.

Low-lying island nations like Indonesia are vulnerable to sea level Height EXtremes (HEXs). When compounded by marine heatwaves, HEXs have larger ecological and societal impact. Here we combine observations with model simulations, to investigate the HEXs and Compound Height-Heat Extremes (CHHEXs) along the Indian Ocean coast of Indonesia in recent decades. We find that anthropogenic sea level rise combined with decadal climate variability causes increased occurrence of HEXs during 2010–2017. Both HEXs and CHHEXs are driven by equatorial westerly and longshore northwesterly wind anomalies. For most HEXs, which occur during December-March, downwelling favorable northwest monsoon winds are enhanced but enhanced vertical mixing limits surface warming. For most CHHEXs, wind anomalies associated with a negative Indian Ocean Dipole (IOD) and co-occurring La Niña weaken the southeasterlies and cooling from coastal upwelling during May-June and November-December. Our findings emphasize the important interplay between anthropogenic warming and climate variability in affecting regional extremes.

Because of natural decadal climate variability—Atlantic multi-decadal variability (AMV) and Pacific decadal variability (PDV) —the increase of global mean surface air temperature (GMSAT) has not been monotonic although atmospheric greenhouse-gas (GHG) concentrations have been increasing continuously. It has always been a challenge regarding how to separate the effects of these two factors on GMSAT. Here, we find a physically based quasi-linear relationship between transient GMSAT and well-mixed GHG changes for both observations and model simulations. With AMV and PDV defined as the combination of variability over both the Atlantic and Pacific basins after the GHG-related trend is removed, we show that the observed GMSAT changes from 1880 to 2017 on multi-decadal or longer timescales receive contributions of about 70% from GHGs, while AMV and PDV together account for roughly 30%. Moreover, AMV contributes more to time-evolving GMSAT on multi-decadal and longer timescales, but PDV leads AMV on decadal timescales with comparable contributions to GMSAT trends. Climate forecasts are challenged by the fact that climate is driven simultaneously by a combination of external forcing and natural variability. Separating these effects remains an important issue for understanding the past and future evolution of climate on global and regional scales. Tongwen Wu of the Beijing Climate Center and colleagues analyze both observational data and model simulations to determine the relative contributions of Atlantic multi-decadal variability (AMV), Pacific decadal variability (PDV), and a combination of well-mixed greenhouse gases (GHG) to global mean surface air temperature (GMSAT) changes between 1880 and 2017. They show that there is a reliable linear relationship between GHG concentrations and changes in GMSAT. Further, 70% of the observed change in GMSAT over this period is contributed by GHGs and a combined 30% by AMV and PDV. The influence of natural variability relative to GHGs is expected to decrease in the future.

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers. ISSN:1758-6798

The low-lying coastal and island regions are vulnerable to sea level rise and extreme events. Compounded by marine heatwaves, sea level extremes have devastating impacts on coastal community and marine ecosystems. As long tide gauge records are sparse, sea level extremes around Indonesia are poorly understood, and the Compound Height-Heat EXtreme (C-HHEX) events remain unexplored. Here we combine in situ and satellite observations with model simulations, to investigate the long-lasting (>1 month) sea level extremes and C-HHEXs along Indonesian coasts of the Indian Ocean since the 1960s. We find that 90% (80%) of the extreme sea level (C-HHEX) events, with a maximum monthly sea level anomaly of 0.45m, are clustered in an 8yr period of 2010-2017, due to anthropogenic global sea level rise and decadal enhancement driven by changing surface winds associated with a combined invigoration of the Indian and Pacific Walker Cells, atmospheric overturning circulations in east-west direction. Remote and local surface wind anomalies associated with negative phases of the Indian Ocean Dipole (IOD) - enhanced by La Niña – drive individual C-HHEX events under a precondition of shallow thermocline (a region of subsurface ocean with temperature decreases rapidly downward). By contrast, winds associated with monsoon and its intraseasonal oscillations force the sea level alone events under a deep thermocline condition. We conclude that the shoaling thermocline in eastern Indian Ocean under anthropogenic warming and global sea level rise favorably precondition the ocean for stronger and more frequent sea level extremes and C-HHEXs, increasing the environmental stress on Indonesia.

Extreme heat occurrence worldwide has increased in the past decades. Greenhouse gas emissions, if not abated aggressively, will lead to large increases in frequency and intensity of heat extremes. At the same time, many cities are facing severe air pollution problems featuring high‐PM episodes that last from days to weeks. Based on a high‐resolution decadal‐long model simulation using a state‐of‐the‐science regional chemistry‐climate model that is bias corrected against reanalysis, here we show that when daily average wet‐bulb temperature of 25 °C is taken as the threshold for severe health impacts, heat extremes frequency averaged over South Asia increases from 45 ± 5 days/year in 1997–2004 to 78 ± 3 days/year in 2046–2054 under RCP8.5 scenario. With daily averaged PM2.5 surface concentration of 60 μg/m3 defined as the threshold for such “unhealthy” extremes, high‐PM extremes would occur 132 ± 8 days/year in the Decade 2050 under RCP8.5. Even more concerning, due to the potential health impacts of two stressors acting in tandem, is the joint occurrence of the heatwave and high‐PM hazard (HHH), which would have substantial increases of 175% in frequency and 79% in duration. This is in contrast to the 73–76% increase for heatwave or high PM when assessed individually. The fraction of land exposed to prolonged HHH increases by more than tenfold in 2050. The alarming increases in just a few decades pose great challenges to adaptation and call for more aggressive mitigation. For example, under a lower emission pathway, the frequency of HHH will only increase by 58% with a lower frequency of high‐PM extremes.

Recent work has suggested that tropical Pacific decadal variability and external forcings have had a comparable influence on the observed changes in the Southern Hemisphere (SH) summertime eddy-dri...

Initialized Earth System predictions are made by starting a numerical prediction model in a state as consistent as possible to observations and running it forward in time for up to 10 years. Skilful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, ranging from agriculture to water resource management to human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skilful predictions can be made: on S2S timescales, there has been some skill in predicting the Madden–Julian Oscillation and North Atlantic Oscillation; on S2I, in predicting the El Niño–Southern Oscillation; and on S2D, in predicting ocean and atmosphere variability in the North Atlantic region. However, challenges remain, and future work must prioritize reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could enhance predictive skill and, in turn, confidence. As numerical models progress towards Earth System models, initialized predictions are expanding to include prediction of sea ice, air pollution, and terrestrial and ocean biochemistry that can bring clear benefit to society and various stakeholders. The foundations of this Review emerged from a workshop held by the National Academies of Sciences, Engineering and Medicine in 2015 at Woods Hole, MA, USA, and the authors gratefully acknowledge support from A. Purcell and N. Huddleston. G.A.M., J.H.R. and N.R. were supported in part by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1844590, and by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement No. 1852977. M.H.E. acknowledges support from the Australian Research Council (Grant CE170100023). H.T. was partly supported by DOE/BER RGMA HiLAT-RASM. M.E.M. was supported by a grant from the NSF Paleoclimate Program #1748097. F.D.-R. and M.G.D. were supported by the H2020 EUCP project under Grant agreement no. 776613, and M.G.D also by the Ramón y Cajal 2017 grant reference RYC-2017-22964. A.C. acknowledges support from the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office’s Modeling Analysis, Prediction and Projections (MAPP) Program and from the NOAA Climate Program Office’s Climate Variability and Predictability (CVP) Program. A.C.S. acknowledges support from the NOAA Climate Variability and Predictability Program (Award NA18OAR4310405) and NOAA-MAPP (NA17OAR4310106). N.S.L. is grateful for support from the NSF (OCE-1752724). D.T. acknowledges support from the NCAR Advanced Study Program and NSF (OCE-1931242). S.C.S was supported by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) Postdoctoral Fellowship. A.A.S. and D.S were supported by the Met Office Hadley Centre Climate Programme funded by the Department for Business, Energy & Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (Defra) and by the European Commission Horizon 2020 EUCP project (GA 776613). Peer Reviewed "Article signat per 26 autors/es: Gerald A. Meehl, Jadwiga H. Richter, Haiyan Teng, Antonietta Capotondi, Kim Cobb, Francisco Doblas-Reyes, Markus G. Donat, Matthew H. England, John C. Fyfe, Weiqing Han, Hyemi Kim, Ben P. Kirtman, Yochanan Kushnir, Nicole S. Lovenduski, Michael E. Mann, William J. Merryfield, Veronica Nieves, Kathy Pegion, Nan Rosenbloom, Sara C. Sanchez, Adam A. Scaife, Doug Smith, Aneesh C. Subramanian, Lantao Sun, Diane Thompson, Caroline C. Ummenhofer & Shang-Ping Xie"

Simulations of 21st century climate with Community Earth System Model version 2 (CESM2) using the standard atmosphere (CAM6), denoted CESM2(CAM6), and the latest generation of the Whole Atmosphere Community Climate Model (WACCM6), denoted CESM2(WACCM6), are presented, and a survey of general results is described. The equilibrium climate sensitivity (ECS) of CESM2(CAM6) is 5.3°C, and CESM2(WACCM6) is 4.8°C, while the transient climate response (TCR) is 2.1°C in CESM2(CAM6) and 2.0°C in CESM2(WACCM6). Thus, these two CESM2 model versions have higher values of ECS than the previous generation of model, the CESM (CAM5) (hereafter CESM1), that had an ECS of 4.1°C, though the CESM2 versions have lower values of TCR compared to the CESM1 with a somewhat higher value of 2.3°C. All model versions produce credible simulations of the time evolution of historical global surface temperature. The higher ECS values for the CESM2 versions are reflected in higher values of global surface temperature increase by 2,100 in CESM2(CAM6) and CESM2(WACCM6) compared to CESM1 between comparable emission scenarios for the high forcing scenario. Future warming among CESM2 model versions and scenarios diverges around 2050. The larger values of TCR and ECS in CESM2(CAM6) compared to CESM1 are manifested by greater warming in the tropics. Associated with a higher climate sensitivity, for CESM2(CAM6) the first instance of an ice‐free Arctic in September occurs for all scenarios and ensemble members in the 2030–2050 time frame, but about a decade later in CESM2(WACCM6), occurring around 2040–2060.