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This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2xCO2 is slightly higher than previously at 2.7‐‐3.1°C (depending on version), and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity. Plain Language Summary Many articles analyze and compare global climate sensitivity in climate models, that is, how their global warming differs at a given level of CO2 concentrations. However, global warming is only one quantity affecting impacts. To assess human‐ and ecosystem‐relevant impacts, it is essential to evaluate the regional climate sensitivity of climate models, that is, how their regional climate features differ at a given level of global warming. We analyze here regional climate sensitivity in the new multimodel ensemble that will underlie the conclusions of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This ensemble of model projections is called the “Sixth Coupled Model Intercomparison Project” or CMIP6. We find that differences in regional climate sensitivity between models in CMIP6 often contribute more to the uncertainty of regional extremes projections than the uncertainty in global mean warming between models. Overall, the regional climate sensitivity features in the CMIP6 models' projections ensemble are very similar to those of the prior ensemble (CMIP5), although the model ensembles have been highlighted to differ in their global climate sensitivity over the 21st century. Key Points Changes in climate extremes as a function of global warming are quasilinear and determine a “regional climate sensitivity” in CMIP5 and CMIP6 The regional climate sensitivity of climate extremes is found to be very similar in CMIP5 and CMIP6, unlike global climate sensitivity Model spread in regional climate sensitivity in CMIP6 contributes more to uncertainty of projected extremes than global climate sensitivity

Extremely high temperatures pose an immediate threat to humans and ecosystems. In recent years, many regions on land and in the ocean experienced heat waves with devastating impacts that would have been highly unlikely without human‐induced climate change. Impacts are particularly severe when heat waves occur in regions with high exposure of people or crops. The recent 2018 spring‐to‐summer season was characterized by several major heat and dry extremes. On daily average between May and July 2018 about 22% of the populated and agricultural areas north of 30° latitude experienced concurrent hot temperature extremes. Events of this type were unprecedented prior to 2010, while similar conditions were experienced in the 2010 and 2012 boreal summers. Earth System Model simulations of present‐day climate, that is, at around +1 °C global warming, also display an increase of concurrent heat extremes. Based on Earth System Model simulations, we show that it is virtually certain (using Intergovernmental Panel on Climate Change calibrated uncertainty language) that the 2018 north hemispheric concurrent heat events would not have occurred without human‐induced climate change. Our results further reveal that the average high‐exposure area projected to experience concurrent warm and hot spells in the Northern Hemisphere increases by about 16% per additional +1 °C of global warming. A strong reduction in fossil fuel emissions is paramount to reduce the risks of unprecedented global‐scale heat wave impacts. Key Points Twenty‐two percent of populated and agricultural areas of the Northern Hemisphere concurrently experienced hot extremes between May and July 2018 It is virtually certain that these 2018 northhemispheric concurrent heat events could not have occurred without human‐induced climate change We would experience a GCWH18‐like event nearly 2 out of 3 years at +1.5 °C and every year at +2 °C global warming

Saltwater intrusion is a critical concern for coastal communities due to its impacts on fresh ecosystems and civil infrastructure. Declining recharge and rising sea level are the two dominant drivers of saltwater intrusion along the land‐ocean continuum, but there are currently no global estimates of future saltwater intrusion that synthesize these two spatially variable processes. Here, for the first time, we provide a novel assessment of global saltwater intrusion risk by integrating future recharge and sea level rise while considering the unique geology and topography of coastal regions. We show that nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100, with different dominant drivers. Climate‐driven changes in subsurface water replenishment (recharge) is responsible for the high‐magnitude cases of saltwater intrusion, whereas sea level rise and coastline migration are responsible for the global pervasiveness of saltwater intrusion and have a greater effect on low‐lying areas. Plain Language Summary Coastal watersheds around the globe are facing perilous changes to their freshwater systems. Driven by climatic changes in recharge and sea level working in tandem, sea water encroaches into coastal groundwater aquifers and consequently salinizes fresh groundwater, in a process called saltwater intrusion. To assess the vulnerability of coastal watersheds to future saltwater intrusion, we applied projections of sea level and groundwater recharge to a global analytical modeling framework. Nearly 77% of the global coast is expected to undergo measurable salinization by the year 2100. Changes in recharge have a greater effect on the magnitude of salinization, whereas sea level rise drives the widespread extensiveness of salinization around the global coast. Our results highlight the variable pressures of climate change on coastal regions and have implications for prioritizing management solutions. Key Points First global analysis of future saltwater intrusion vulnerability responding to spatially variable recharge and sea level rise is provided Recharge drives the extreme cases of saltwater intrusion, while sea level rise is responsible for its global pervasiveness Nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100

The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The data set of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We do not find a conclusive result for how cloud clustering changes as the climate warms. Key Points Temperature, humidity, and clouds in radiative‐convective equilibrium vary substantially across models Models agree that self‐aggregation dries the atmosphere and reduces high cloudiness There is no consistency in how self‐aggregation depends on warming

Air‐sea heat and moisture fluxes modulate the surface energy balance and oceanic and atmospheric heat transport across all timescales. Spatial gradients of these fluxes, on a multitude of spatial scales, also have significant impacts on the ocean and atmosphere. Nevertheless, analysis of these gradients, and discussion regarding our ability to represent them, is relatively absent within the community. This letter discusses their importance and presents a wintertime climatology. Their sensitivity to spatiotemporal scale and choice of data set is also examined in the mid‐latitudes. A lead‐lag analysis illustrates that wintertime air‐sea heat flux gradients in the Gulf Stream can precede the North Atlantic Oscillation by ∼1 month. A lack of observations and thus validation of air‐sea heat flux gradients represents a significant gap in our understanding of how air‐sea processes affect weather and climate, and warrants increased attention from the observational and modeling communities. Plain Language Summary The oceans impact both weather and climate by heating and cooling the lower atmosphere. Surface latent (sensible) heat flux is a quantity that measures the exchange of heat associated with evaporation of seawater (an air‐sea temperature difference). In addition to the absolute exchange, the manner in which the exchange varies spatially (the heat flux gradients) is also known to be important for the development of weather systems and longer‐term climate. Despite this, relatively little attention is paid in the literature to variability in these gradients. This study provides a brief overview of their importance and provides a wintertime climatology in these gradients. It is also illustrated that when considering gradients, the importance of specifying the spatial scale over which the gradient is calculated is critical. Although many differences exist between air‐sea heat flux data products in these gradients, there are currently almost no observations to validate them in key areas of interest, which represents a significant deficiency in our understanding of ocean‐atmosphere interactions. This is emphasized by demonstrating that these gradients in the mid‐latitudes can statistically precede variability in the North Atlantic Oscillation, the most important mode of monthly atmospheric variability in the North Atlantic. Key Points Air‐sea heat and moisture flux gradients modulate important oceanic and atmospheric processes across a multitude of spatiotemporal scales Air‐sea heat flux gradient variability can statistically precede mid‐latitude atmospheric variability Notable air‐sea heat and moisture flux gradient inconsistencies exist in data products, yet the ability to validate them remains elusive

Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi‐scenario, and multi GCM‐ RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature (4.3±0.7K)$(4.3\\pm 0.7\\,\\mathrm{K})$and substantial bottom warming (1.7±0.7K)$(1.7\\pm 0.7\\,\\mathrm{K})$ , longer stratification periods (+55 days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems. Plain Language Summary Lakes are threatened by climate change because of effects related to the increasing temperature, long stratification, and ice disappearance. One of the best tools to predict these effects on lakes is numerical modeling of lakes that benefit from climate modeling. Climate modeling is normally done globally or in the so‐called general circulation model (GCM) or more detailed simulations on regional levels (RCM) like the CORDEX data set. In this study, we used the CORDEX data, which employed several climate models from several regions (domains) for several climatic scenarios (emissions scenarios) to force multiple lake models. This approach gave us an extensive prediction about various possible outputs. We applied this approach to Lake Sevan (Armenia), a large mountain lake. Our study predicted for the worst‐case scenario, an increase of the surface temperature by almost 4.3 K by the end of the 21st century, 1.75 K for bottom temperature, a total disappearance of ice cover, and about 55 extra days of stratification, showing its vulnerability for climate change. This optimized workflow uses the strength of a wide variety of models on the climate and lake levels to better understand the impact of climate change and quantify the sources of uncertainty in the workflow. Key Points Dual multi‐model ensemble of climate data and lake models is used for robust projections of climate change impacts Variance decomposition effectively identified the sources of uncertainty and contributions of different models to the overall uncertainty Significant warming, longer stratification periods, and loss of ice cover are predicted for Lake Sevan by the end of the 21st century

Anthropogenic aerosols (AER) and greenhouse gases (GHG)—the leading drivers of the forced historical change—produce different large‐scale climate response patterns, with correlations trending from negative to positive over the past century. To understand what caused the time‐evolving comparison between GHG and AER response patterns, we apply a low‐frequency component analysis to historical surface ocean changes from CESM1 single‐forcing large‐ensemble simulations. While GHG response is characterized by its first leading mode, AER response consists of two distinct modes. The first one, featuring long‐term global AER increase and global cooling, opposes GHG response patterns up to the mid‐twentieth century. The second one, featuring multidecadal variations in AER distributions and interhemispheric asymmetric surface ocean changes, appears to reinforce the GHG warming effect over recent decades. AER thus can have both competing and synergistic effects with GHG as their emissions change temporally and spatially. Plain Language Summary Anthropogenically forced climate change over the past century has been mainly caused by two types of emissions: greenhouse gases (GHG) and aerosols (AER). In general, sulfate aerosols from industrial sources can reflect shortwave radiation to yield a cooling effect opposite to the GHG warming effect. However, model simulations isolating GHG and AER forcings show that the large‐scale climate effect of AER does not always dampen the GHG effect. Instead, over recent decades, AER have produced surface ocean response patterns more like the GHG response. Using a novel low‐frequency statistical decomposion, we find that aerosols have driven two distinct modes of climate change patterns over the historical period. The first mode is associated with global aerosol increase, resulting in global‐wide cooling damping the GHG‐induced warming. The second mode is associated with the shift in aerosol emissions from north America/western Europe to southeast Asia, which drives regional changes enhancing the GHG effect. Our results highlight the importance of considering the temporal and spatial evolutions of AER emissions in assessing GHG and AER climate effects and attributing historical anthropogenic climate changes to GHG and AER forcings. Key Points Over the past century, GHG forced response is characterized by a single dominant mode while AER response consists of two distinct modes Monotonic global aerosol increases, mainly from Southeast Asia emissions, produce a global aerosol cooling mode opposing greenhouse warming Important in recent decades, geographic redistribution of AER emissions produces a second aerosol mode that reinforces greenhouse warming

The concentration of chlorophyll‐a (CHL) is an important proxy for autotrophic biomass and primary production in the ocean. Quantifying trends and variability in CHL are essential to understanding how marine ecosystems are affected by climate change. Previous analyses have focused on assessing trends in CHL mean, but little is known about observed changes in CHL extremes and variance. Here we apply a quantile regression model to detect trends in CHL distribution over the period of 1997–2022 for several quantiles. We find that the magnitude of trends in upper quantiles of global CHL (>90th) are larger than those in lower quantiles (≤50th) and in the mean, suggesting a growing asymmetry in CHL distribution. On a regional scale, trends in different quantiles are statistically significant at high latitude, equatorial, and oligotrophic regions. Assessing changes in CHL distribution has potential to yield a more comprehensive understanding of climate change impacts on CHL. Plain Language Summary The marine environment is essential to nature and society, as it provides food and other important services such as Earth's climate regulation and habitat for species. Marine primary productivity is increasingly stressed due to global climate change. Detecting the impact of climate change on primary producers should be a priority given their critical role in the climate system. Most studies focus on the impact of climate change by evaluating the mean state of primary productivity, but little is known about whether and how climate change is impacting variance and extremes. Here we assess changes in chlorophyll‐a (CHL), which is an important proxy for primary production of marine ecosystems. We quantify long‐term changes in different aspects of the CHL distribution (mean, variance, and extremes) using a quantile regression model. We find that CHL high extremes and variability are slightly intensified globally during the 26 years of observational record. Trends in regional scales, especially in high‐latitude and North Atlantic Subtropical Gyre, show that CHL high extremes have been increasing since 1997. Our results suggest that more emphasis should be put into understanding the impact of climate change on the variance and extremes of primary productivity for climate change adaptation and mitigation. Key Points Long‐term changes are detected in different aspects of the distribution of chlorophyll‐a (not just the mean state) Oceanic chlorophyll‐a high extremes are changing faster than chlorophyll‐a mean globally during 1997–2022 On a regional scale, chlorophyll‐a extremes trends are predominant at high latitude (+), equatorial (−), and oligotrophic regions (−)

Extreme heat and drought often reduce the yields of important food crops around the world, putting stress on regional and global food security. The probability of concurrently hot and dry conditions, which can have compounding impacts on crops, has already increased in many regions of the globe. The evolution of these trends in coming decades could have important impacts on global food security. However, regional variation and the influence of natural climate variability on these trends remains an important gap in understanding future climate risk to crops. In this study, we examine trends in concurrent hot-and-dry extremes over global maize and wheat croplands since 1950. We find that the mean extent of cropland in a joint hot-and-dry extreme increased by ∼2% over 1950–2009, and this trend has accelerated substantially since the mid-2000s, notably in the tropics. While joint hot-and-dry seasons affected at most 1%–2% of global cropland per year during the mid-20th century, they regularly exceeded this extent after about 1980, affecting up to 5% of global crop area. These results suggest that the global climate is transitioning from one in which concurrent heat and drought occur rarely to one in which they occur over an important fraction of croplands every year. While these long-term global trends are primarily attributable to anthropogenic climate change, we find they have been suppressed by decadal climate variability in some regions, especially ones with chronic food insecurity. Potential reversals in these tendencies of decadal variability would accelerate exposure of croplands to concurrent heat and drought in coming decades. We conclude by highlighting the need for research and adaptive interventions around multivariate hazards to global crops across timescales.