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Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA. Plain Language Summary The Arctic has been warming four times as quickly as the global mean since 1980. This so‐called Arctic Amplification (AA) has unprecedented impacts on Arctic environments and livelihoods. AA is robustly simulated by climate models, but simulations rarely reproduce the observed levels of AA for 1980–2022. This may be due to a model misrepresentation of the Arctic's sensitivity to increasing greenhouse gases. Another possibility is that the large, observed value of AA is inflated by natural fluctuations in the climate system. Here, we use machine learning to quantify the contribution of natural fluctuations to observed AA. We show that natural fluctuations have inflated AA by 38%, and thus reconcile model‐observation differences and suggest that the observed large AA over 1980 to present would not persist into the future. Key Points Internally generated and externally forced temperature trends over the Arctic and globe can be partitioned using machine learning methods Internal variability has enhanced Arctic warming while damping global warming over 1980‐2022 Accounting for internal variability in observations reconciles discrepancies between simulated and observed Arctic Amplification

Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming. Plain Language Summary Different global climate models exhibit large variability in how clouds across the tropics will respond to future warming. This is largely due to the complexity and diversity of responses that differing cloud types may experience under warming. A long‐term goal of the community has been to narrow this disagreement between different models. Over the past 15 years, several studies have proposed ways in which the variability in future cloud changes might be related to errors in how these models represent present‐day properties. Here, we use three collections of models to show that variability in tropical cloud changes is closely tied to shortwave cloud radiative properties across the Southern Ocean. We then use this intermodel relationship along with observations to produce a best estimate of cloud feedback across the tropics. Key Points We find a relationship between tropical cloud feedback and mean‐state biases in Southern Hemisphere extratropical cloud properties This intermodel relationship is found to be present in three different ensembles of global climate models, a sign of robustness This relationship suggests a likely tropical cloud feedback value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty

Anthropogenic climate change has become clearly observable through many metrics. These include an increase in global annual temperatures, growing heat content of the oceans, and sea level rise owing to the melting of the polar ice sheets and glaciers. Now, Santer et al. report that a human-caused signal in the seasonal cycle of tropospheric temperature can also be measured (see the Perspective by Randel). They use satellite data and the anthropogenic “fingerprint” predicted by climate models to show the extent of the effects and discuss how these changes have been caused. Science , this issue p. eaas8806 ; see also p. 227 Human activity is causing changes in the seasonal cycle of tropospheric temperature. We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic “fingerprint” predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics. Simple physical mechanisms explain these features. The model fingerprint of seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. Our results suggest that attribution studies with the changing seasonal cycle provide powerful evidence for a significant human effect on Earth’s climate.

Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns. Plain Language Summary When comparing model simulations of Earth's recent warming to real‐world observations, differences may arise from several factors. Two important factors are the model errors in the simulated response to increased greenhouse gases, and natural fluctuations within the climate system that produced discrepancies between observations and models. Thus, quantifying the role of these natural fluctuations is important for the assessment of model‐observation differences. Previous studies have shown that natural climate variability has depressed global warming and enhanced Arctic warming. By compositing the multi‐decadal trend patterns from CMIP6 simulations in which natural variability warms the Arctic but has a global cooling effect, we find that the majority of these model simulations also produce enhanced warming in the Barents and Kara Seas and cooling in the Tropical Eastern Pacific and Southern Ocean due to natural variability. Since these are the exact features imprinted on observed surface temperature changes over 1980–2022, our work suggests that natural variability is an important component of several noteworthy differences between models and observations. Key Points Internal variability has enhanced Arctic warming but suppressed global warming over 1980–2022 This manifestation of internal variability is rare in model simulations but has a robust global surface air temperature (SAT) trend pattern This internal SAT pattern features warming in the Barents and Kara Sea and cooling of the Tropical Eastern Pacific and Southern Ocean

Arctic amplification of anthropogenic climate change is widely attributed to the sea-ice albedo feedback, with its attendant increase in absorbed solar radiation, and to the effect of the vertical structure of atmospheric warming on Earth’s outgoing longwave radiation. The latter lapse rate feedback is subject, at high latitudes, to a myriad of local and remote influences whose relative contributions remain unquantified. The distinct controls on the high-latitude lapse rate feedback are here partitioned into “upper” and “lower” contributions originating above and below a characteristic climatological isentropic surface that separates the high-latitude lower troposphere from the rest of the atmosphere. This decomposition clarifies how the positive high-latitude lapse rate feedback over polar oceans arises primarily as an atmospheric response to local sea ice loss and is reduced in subpolar latitudes by an increase in poleward atmospheric energy transport. The separation of the locally driven component of the high-latitude lapse rate feedback further reveals how it and the sea-ice albedo feedback together dominate Arctic amplification as a coupled mechanism operating across the seasonal cycle.

Observed temperature changes from 2002 to 2022 reveal a pronounced warming of the Southern Hemisphere (SH) subtropical lower stratosphere, and a cooling of the Antarctic lower stratosphere. In contrast, model simulations of 21st‐century stratospheric temperature changes show widespread cooling driven by increasing greenhouse gases, with local warming in the Antarctic lower stratosphere due to ozone healing. We provide evidence that these discrepancies between observed and simulated stratospheric temperature changes are linked to a slowdown of the Brewer‐Dobson Circulation, particularly in the SH. These changes in the stratospheric circulation are strongest from October through December. This altered circulation warms the SH subtropical lower stratosphere while cooling the Antarctic lower stratosphere, canceling and even reversing the Antarctic ozone recovery that would have occurred in its absence during this period. When circulation changes are accounted for, the SH subtropical lower‐stratospheric warming is removed, and Antarctic lower‐stratospheric warming is revealed with enhanced ozone healing, highlighting the crucial role of the stratospheric circulation in shaping temperature and ozone changes. Plain Language Summary Climate models predict that rising greenhouse gas levels cool the stratosphere, while the healing of the Antarctic ozone hole—driven by the reduction of ozone‐depleting substances under the Montreal Protocol since the beginning of the 21st century—should warm the Antarctic lower stratosphere. However, observations for the period from 2002 to 2022 reveal unexpected changes: warming in the Southern Hemisphere (SH) subtropical lower stratosphere and cooling over Antarctica. This study identifies the cause as a slowdown in stratospheric circulation that moves stratospheric air and chemicals from low to high latitudes. These circulation changes, most pronounced from October to December, lead to warming in the subtropical lower stratosphere of the SH and cooling in the Antarctic lower stratosphere. They also mask the anticipated ozone recovery over Antarctica during this period. Accounting for these circulation changes removes the anomalous warming of the SH subtropical lower stratosphere and reveals an obvious Antarctic lower stratospheric warming and enhanced ozone recovery. These findings highlight the crucial role of the stratospheric circulation in shaping temperature and ozone changes. Key Points The warming of the Southern Hemisphere (SH) subtropical lower stratosphere over 2002–2022 is linked to a Brewer‐Dobson Circulation slowdown These circulation changes cool the Antarctic lower stratosphere and mask the Antarctic ozone healing from October to December Removing circulation changes eliminates SH subtropical stratospheric warming and reveals Antarctic warming and enhanced ozone healing

The Arctic has seen rapid sea-ice decline in the past three decades, whilst warming at about twice the global average rate. Yet the relationship between Arctic warming and sea-ice loss is not well understood. Here, we present evidence that trends in summertime atmospheric circulation may have contributed as much as 60% to the September sea-ice extent decline since 1979. A tendency towards a stronger anticyclonic circulation over Greenland and the Arctic Ocean with a barotropic structure in the troposphere increased the downwelling longwave radiation above the ice by warming and moistening the lower troposphere. Model experiments, with reanalysis data constraining atmospheric circulation, replicate the observed thermodynamic response and indicate that the near-surface changes are dominated by circulation changes rather than feedbacks from the changing sea-ice cover. Internal variability dominates the Arctic summer circulation trend and may be responsible for about 30–50% of the overall decline in September sea ice since 1979. The Arctic is warming and sea ice is declining, but how the two link is unclear. This study shows changes in summertime atmospheric circulation and internal variability may have caused up to 60% of September sea-ice decline since 1979.

The relative contribution and physical drivers of internal variability in recent Arctic sea ice loss remain open questions, leaving up for debate whether global climate models used for climate projection lack sufficient sensitivity in the Arctic to climate forcing. Here, through analysis of large ensembles of fully coupled climate model simulations with historical radiative forcing, we present an important internal mechanism arising from low-frequency Arctic atmospheric variability in models that can cause substantial summer sea ice melting in addition to that due to anthropogenic forcing. This simulated internal variability shows a strong similarity to the observed Arctic atmospheric change in the past 37 years. Through a fingerprint pattern matching method, we estimate that this internal variability contributes to about 40–50% of observed multi-decadal decline in Arctic sea ice. Our study also suggests that global climate models may not actually underestimate sea ice sensitivities in the Arctic, but have trouble fully replicating an observed linkage between the Arctic and lower latitudes in recent decades. Further improvements in simulating the observed Arctic–global linkage are thus necessary before the Arctic’s sensitivity to global warming in models can be quantified with confidence.

Independent research teams have constructed long-term tropical time series of the temperature of the middle troposphere (TMT) using satellite Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) measurements. Despite careful efforts to homogenize the MSU/AMSU measurements, tropical TMT trends beginning in 1979 disagree by more than a factor of 3. Previous studies suggest that the discrepancy in tropical TMT trends is caused by differences in both theNOAA-9warm target factor and diurnal drift corrections. This work introduces a new observationally based method for removing biases related to satellite diurnal drift. Over land, the derived diurnal correction is similar to a general circulation model (GCM) diurnal cycle. Over ocean, the diurnal corrections have a negligible effect on TMT trends, indicating that oceanic biases are small. It is demonstrated that this method is effective at removing biases between coorbiting satellites and biases between nodes of individual satellites. Using a homogenized TMT dataset, the ratio of tropical tropospheric temperature trends relative to surface temperature trends is in accord with the ratio from GCMs. It is shown that bias corrections for diurnal drift based on a GCM produce tropical trends very similar to those from the observationally based correction, with a trend difference smaller than 0.02 K decade−1. Differences between various TMT datasets are explored further. Large differences in tropical TMT trends between this work and that of the University of Alabama in Huntsville (UAH) are attributed to differences in the treatment of theNOAA-9target factor and the diurnal cycle correction.

Recent studies have examined tropical upper tropospheric warming by comparing coupled atmosphere-ocean global circulation model (GCM) simulations from Phase 3 of the Coupled Model Intercomparison Project (CMIP3) with satellite and radiosonde observations of warming in the tropical upper troposphere relative to the lower-middle troposphere. These studies showed that models tended to overestimate increases in static stability between the upper and lower-middle troposphere. We revisit this issue using atmospheric GCMs with prescribed historical sea surface temperatures (SSTs) and coupled atmosphere-ocean GCMs that participated in the latest model intercomparison project, CMIP5. It is demonstrated that even with historical SSTs as a boundary condition, most atmospheric models exhibit excessive tropical upper tropospheric warming relative to the lower-middle troposphere as compared with satellite-borne microwave sounding unit measurements. It is also shown that the results from CMIP5 coupled atmosphere-ocean GCMs are similar to findings from CMIP3 coupled GCMs. The apparent model-observational difference for tropical upper tropospheric warming represents an important problem, but it is not clear whether the difference is a result of common biases in GCMs, biases in observational datasets, or both.