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The tropical climate response to GHG forcing is spatially non-uniform1–3. Even though enhanced equatorial and eastern Pacific warming is simulated by most climate models, the underlying mechanisms—including the relative roles of atmospheric and oceanic feedbacks—remain debated. Here, we use a climate model with idealized CO2-radiative forcing patterns to show that off-equatorial radiative forcing and corresponding coupled circulation/cloud adjustments are responsible for much of equatorial warming in response to global CO2forcing. For equatorial forcing, the atmosphere responds by enhancing atmospheric heat export to the extra-tropics, an associated strengthening of the ascending Hadley circulation branch and strong negative equatorial cloud feedbacks. These processes together greatly dampen equatorial surface warming. Intensification of the oceanic subtropical cells and increased cold subsurface water upwelling in the eastern tropical Pacific provide an additional negative feedback for surface temperatures. In contrast, applying off-equatorial forcing, the atmosphere responds by exporting less heat from the tropics, Hadley circulation weakening and weaker negative equatorial cloud feedbacks, while the subtropical cells slow down in the ocean. Our results demonstrate a delicate balance in the coupled climate system between remote circulation adjustments and regional feedbacks that create the patterns of future climate change.

The Pacific Decadal Oscillation (PDO) is the dominant pattern of observed sea surface temperature variability in the North Pacific. Its characteristic pattern of eastern intensified warming and cooling within the Kuroshio‐Oyashio Extension is pervasive across timescales. We investigate the mechanisms for its decadal persistence in coupled climate models, focusing on the role of ocean circulation changes. We use low‐frequency component analysis to isolate the mechanisms relevant at decadal and longer timescales from those acting at shorter timescales. The PDO warm phase is associated with strengthening and expansion of the North Pacific subpolar gyre in response to a deepening of the Aleutian Low. The subpolar gyre takes several years to respond to wind stress forcing through baroclinic ocean Rossby wave adjustment, such that white noise atmospheric forcing is integrated into red noise, increasing variability at long timescales. Sea level anomalies within the Kuroshio‐Oyashio Extension provide an observable ocean circulation signature of North Pacific decadal variability. North Pacific sea surface temperatures vary from decade to decade with a characteristic pattern, where temperature anomalies near North America are opposite to those off the coast of Japan. These ocean changes influence fish populations as well as climate over the surrounding land regions. Here we investigate the physical mechanisms for this sea surface temperature variability using global climate models that include interactions between the atmosphere and ocean. We find that ocean currents change together with the changes in sea surface temperature and that the time it takes for these ocean currents to adjust to changes in the prevailing wind patterns gives this variability its persistence from decade to decade. Decadal sea surface temperature variability in the North Pacific is linked to variability of the ocean gyre circulationThe gyre circulation integrates white noise Aleutian Low forcing, giving variability that is strongest at decadal and longer timescalesSea level anomalies illustrate the ocean';s role in North Pacific decadal variability

Spring snowpack in the mountains of the western United States has not declined substantially since the 1980s, despite significant global and regional warming. Here we show that this apparent insensitivity of snowpack to warming is a result of changes in the atmospheric circulation over the western United States, which have reduced snowpack losses due to warming. Climate model simulations indicate that the observed circulation changes have been driven in part by a shift in Pacific sea surface temperatures that is attributable to natural variability, and not part of the simulated response to anthropogenic forcing. Removing the influence of natural variability reveals a robust anthropogenic decline in western U.S. snowpack since the 1980s, particularly during the early months of the accumulation season (October–November). These results suggest that the recent stability of western U.S. snowpack will be followed by a period of accelerated decline once the current mode of natural variability subsides. Melting snowpack is a vital source of water in the western United States during the summer, when rainfall is usually scarce. Although the amount of water contained in the snowpack has declined over the past century, it has been surprisingly stable since the 1980s, despite 1 °C of warming over the same period. At first glance, this result might appear to indicate that the snowpack is quite resilient to warming. However, here we show that the contribution of global warming to western U.S. snowpack loss has in reality been large and widespread since the 1980s, but mostly offset by natural variability in the climate system. This result points to a faster rate of snowpack loss in coming decades, when the impact of global warming is more likely to be amplified, rather than offset, by natural variability. Circulation changes have offset most western U.S. snowpack loss from global warming since the 1980sCirculation changes are likely a result of natural variability, not anthropogenic forcingSnowpack loss will likely accelerate in coming decades as the phase of natural variability subsides

The surface temperature response to greenhouse gas forcing displays a characteristic pattern of polar-amplified warming1–5, particularly in the Northern Hemisphere. However, the causes of this polar amplification are still debated. Some studies highlight the importance of surface-albedo feedback6–8, while others find larger contributions from longwave feedbacks4,9,10, with changes in atmospheric and oceanic heat transport also thought to play a role11–16. Here, we determine the causes of polar amplification using climate model simulations in which CO2forcing is prescribed in distinct geographical regions, with the linear sum of climate responses to regional forcings replicating the response to global forcing. The degree of polar amplification depends strongly on the location of CO2forcing. In particular, polar amplification is found to be dominated by forcing in the polar regions, specifically through positive local lapse-rate feedback, with ice-albedo and Planck feedbacks playing subsidiary roles. Extra-polar forcing is further shown to be conducive to polar warming, but given that it induces a largely uniform warming pattern through enhanced poleward heat transport, it contributes little to polar amplification. Therefore, understanding polar amplification requires primarily a better insight into local forcing and feedbacks rather than extra-polar processes.

Climate change has myriad physical and economic impacts. Even those that can be easily quantified indicate the need for ambitious climate action. Other climate impacts have yet to be quantified. We argue here that uncertainties in climate and weather extremes only further increase the social cost of carbon emissions.

Estimates of radiative feedbacks obtained by regressing fluctuations in top‐of‐atmosphere (TOA) energy imbalance and surface temperature depend critically on the sampling interval and on assumptions about the nature of the stochastic forcing driving internal variability. Here we develop an energy balance framework that allows us to model the different impacts of stochastic atmospheric and oceanic forcing on feedback estimates. The contribution of different forcing components is parsed based on their impacts on the covariance structure of near‐surface air temperature and TOA energy fluxes, and the framework is validated in a hierarchy of climate model simulations that span a range of oceanic configurations and reproduce the key features seen in observations. We find that at least three distinct forcing sources, feedbacks, and time scales are needed to explain the full covariance structure. Atmospheric and oceanic forcings drive modes of variability with distinct relationships between temperature and TOA radiation, leading to an effect akin to regression dilution. The net regression‐based feedback estimate is found to be a weighted average of the distinct feedbacks associated with each mode. Moreover, the estimated feedback depends on whether surface temperature and TOA energy fluxes are sampled at monthly or annual time scales. The results suggest that regression‐based feedback estimates reflect contributions from a combination of stochastic forcings and should not be interpreted as providing an estimate of the radiative feedback governing the climate response to greenhouse gas forcing. Climate sensitivity quantifies the long‐term warming the Earth will experience in response to the additional energy trapped in the system due to greenhouse gases. The physical processes that ultimately determine climate sensitivity—termed climate feedbacks—have been extensively investigated using information from natural variability in Earth's temperature and net energy imbalance. However, a complete physical model for what controls this natural variability has been lacking. We derive such a physical model and calibrate it to a hierarchy of numerical climate simulations of increasing complexity. We are able to answer several outstanding questions about previous estimates of climate feedbacks and sensitivity drawn from natural variability, such as what is the source of this variability, and how the estimates depend on the how the data is analyzed. We find that at least three different mechanisms for natural variability are needed to explain the relationship between temperature and energy imbalance and that none provide direct estimates of climate sensitivity. Net interannual radiative feedback is a weighted average of distinct feedbacks associated with distinct modes of variabilityFeedbacks obtained from interannual variability are distinct from those governing the response to CO2forcingTemperature‐independent wind‐driven air‐sea fluxes are a critical forcing for subannual temperature variability

Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed. Atmospheric temperature variability is nonnormal on daily time scalesApparent normal variability on synoptic scales is an artifact of filteringGenerating mechanism for nonnormality does not necessarily act at frequency where greatest nonnormality is observed

This study assesses the effective climate sensitivity (EffCS) and transient climate response (TCR) derived from global energy budget constraints within historical simulations of eight CMIP6 global climate models (GCMs). These calculations are enabled by use of the Radiative Forcing Model Intercomparison Project (RFMIP) simulations, which permit accurate quantification of the radiative forcing. Long‐term historical energy budget constraints generally underestimate EffCS from CO2quadrupling and TCR from CO2ramping, owing to changes in radiative feedbacks and changes in ocean heat uptake efficiency. Atmospheric GCMs forced by observed warming patterns produce lower values of EffCS that are more in line with those inferred from observed historical energy budget changes. The differences in the EffCS estimates from historical energy budget constraints of models and observations are traced to discrepancies between modeled and observed historical surface warming patterns. Here we use climate models and observations to evaluate the extent to which future warming can be inferred from historical climate change. We assess the historical energy budget in 8 climate models by leveraging a recent community effort to quantify radiative forcing within their historical simulations. We find the historical energy budget tends to provide a biased‐low constraint on future warming, represented by two metrics: effective climate sensitivity and transient climate response. Moreover, observations and simulations run with observed surface warming patterns produce even lower values of climate sensitivity. This difference in climate sensitivity estimates can be traced to discrepancies between modeled and observed surface warming patterns. Estimates from historical energy budget constraints underestimate equilibrium climate sensitivity and transient climate response in modelsAtmosphere‐only models forced by observed surface warming patterns produce lower estimates of ECS, in line with historical observationsDiscrepancies between modeled and observed historical surface warming patterns account for the differences in climate sensitivity estimates Estimates from historical energy budget constraints underestimate equilibrium climate sensitivity and transient climate response in models Atmosphere‐only models forced by observed surface warming patterns produce lower estimates of ECS, in line with historical observations Discrepancies between modeled and observed historical surface warming patterns account for the differences in climate sensitivity estimates

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100‐year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions. Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data. Several CMIP6 control simulations show more interdecadal global mean temperature variability than previous model generationsEven the most variable CMIP6 models never show century‐length global mean temperature trends that exceed observed warming trendsUnlike in control simulations, observed global mean temperature variability is coherent with variability in tropical convective regions

We compare top‐of‐atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea‐surface temperature (SST) and sea‐ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so‐called global warming “hiatus” of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low‐cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low‐cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a “pattern effect” that may be too weak compared to observations. Earth's radiation budget describes the balance between radiation from the sun intercepted by Earth and radiation returned back to space through reflection of solar radiation and emission of terrestrial thermal infrared radiation. This balance is a fundamental property of Earth's climate system as it describes how Earth gains and sheds heat. Here we use observations from the Clouds and the Earth's Radiant Energy System (CERES) to evaluate how seven state‐of‐the‐art climate models represent changes in Earth's radiation budget during and following the so‐called global warming “hiatus” of the early 21st century. The models were provided observed sea‐surface temperature and sea‐ice boundary conditions as well as natural and anthropogenic forcings. We find remarkable agreement between observed and simulated differences in reflected solar and emitted thermal infrared radiation between the post‐hiatus and hiatus periods. Furthermore, a model's ability to correctly relate Earth's radiation budget and surface temperature is found to depend upon how well it represents reflected solar radiation changes in regions dominated by low clouds, particularly those over the eastern Pacific ocean. There is good agreement between radiation budget variations observed by CERES and simulated by seven state‐of‐the‐art climate modelsThe relationship between global mean net TOA radiation and surface temperature is sensitive to changes in regions dominated by low cloudsMost models underestimate shortwave flux changes in response to SST changes over the east Pacific, suggesting too weak a “pattern effect”