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Abstract The role of cloud feedbacks in Arctic amplification (AA) of anthropogenic warming remains unclear. Traditional feedback analysis diagnoses the net cloud feedback as strongly positive in the tropics but either weak or negative in the Arctic, suggesting that AA would be amplified if cloud feedbacks were suppressed. However, in cloud-locking experiments using the slab ocean version of the Energy Exascale Earth System Model (E3SM), we find that suppressing cloud feedbacks results in a substantial decrease in AA under greenhouse gas forcing. We show that the increase in AA from cloud feedbacks arises from two main mechanisms: 1) the additional energy contributed by positive cloud feedbacks in the tropics leads to increased poleward moist atmospheric heat transport (AHT) which then amplifies Arctic warming; and 2) the additional Arctic warming is amplified by positive noncloud feedbacks in the region, together making extrapolar cloud feedbacks amplify AA. We also find that cloud changes can modify the strength of noncloud feedback, but that modification has a small effect on Arctic warming. We further examine the role of cloud feedbacks in AA using a moist energy balance model, which demonstrates that interactions of cloud feedbacks with moist AHT and other positive feedbacks dominate the influence of clouds on the pattern of surface warming. However, the contribution of cloud-induced changes in noncloud feedbacks on AA is relatively minor. These results demonstrate that traditional attributions of AA, that are based on local feedback analysis, overlook key interactions between extrapolar cloud changes, poleward AHT, and noncloud feedbacks in the Arctic. 

Abstract Atmospheric models forced with observed sea surface temperatures (SSTs) suggest a trend toward a more-stabilizing cloud feedback in recent decades, partly due to the surface cooling trend in the eastern Pacific (EP) and the warming trend in the western Pacific (WP). Here, we show model evidence that the low-cloud feedback has contributions from both forced and unforced feedback components and that its time variation arises in large part through changes in the relative importance of the two over time, rather than through variations in forced or unforced feedbacks themselves. Initial-condition large ensembles (LEs) suggest that the SST patterns are dominated by unforced variations for 30-yr windows ending prior to the 1980s. In general, unforced SSTs are representative of an ENSO-like pattern, which corresponds to weak low-level stability in the tropics and less-stabilizing low-cloud feedback. Since the 1980s, the forced signals have become stronger, outweighing the unforced signals for the 30-yr windows ending after the 2010s. Forced SSTs are characterized by relatively uniform warming with an enhancement in the WP, corresponding to a more-stabilizing low-cloud feedback in most cases. The time-evolving SST pattern due to this increasing importance of forced signals is the dominant contributor to the recent stabilizing shift of low-cloud feedback in the LEs. Using single-forcing LEs, we further find that if only greenhouse gases evolve with time, the transition to the domination of forced signals occurs 10–20 years earlier compared to the LEs with full forcings, which can be understood through the compensating effect between aerosols and greenhouse gases. 

Abstract Paleoclimate records have been used to estimate the modern equilibrium climate sensitivity. However, this requires understanding how the feedbacks governing the climate response vary with the climate itself. Here we warm and cool a state-of-the-art climate model to simulate a continuum of climates ranging from a nearly ice-covered Snowball Earth to a nearly ice-free hothouse. We find that the pre-industrial (PI) climate is near a stability optimum: warming leads to a less-stable (more-sensitive) climate, as does cooling of more than 2K. Physically interpreting the results, we find that the decrease in stability for climates colder than the PI occurs mainly due to the albedo and lapse-rate feedbacks, and the decrease in stability for warmer climates occurs mainly due to the cloud feedback. These results imply that paleoclimate records provide a stronger constraint than has been calculated in previous studies, suggesting a reduction in the uncertainty range of the climate sensitivity. 

Abstract We compare abrupt CO₂‐quadrupling (abrupt‐4xCO2) and ‐doubling (abrupt‐2xCO2) simulations across 10 CMIP6 models. Two models (CESM2 and MRI‐ESM2‐0) warm substantially more than twice as much under abrupt‐4xCO2 than abrupt‐2xCO2, which cannot be explained by the non‐logarithmic scaling of CO₂forcing. Using an energy balance model, we show that increased warming rates within these two models are driven by both less‐negative radiative feedbacks and smaller global effective heat capacity under abrupt‐4xCO2. These differences are caused by a decrease in low cloud cover andshallower ocean heat storage, respectively; both are linked to smaller fractional declines in the Atlantic Meridional Overturning Circulation (AMOC) under abrupt‐4xCO2 (relative to abrupt‐2xCO2). On a global scale, higher climate sensitivity under larger forcing can be explained by a feedback‐temperature dependence; however, we find that forcing‐dependent spatial warming patterns due to AMOC decline are an important physical mechanism which reduces warming in a way that is not captured by a global‐mean framework. 

Abstract Arctic warming under increased CO₂peaks in winter, but is influenced by summer forcing via seasonal ocean heat storage. Yet changes in atmospheric heat transport into the Arctic have mainly been investigated in the annual mean or winter, with limited focus on other seasons. We investigate the full seasonal cycle of poleward heat transport modeled with increased CO₂or with individually applied Arctic sea‐ice loss and global sea‐surface warming. We find that a winter reduction in dry heat transport is driven by Arctic sea‐ice loss and warming, while a summer increase in moist heat transport is driven by sub‐Arctic warming and moistening. Intermodel spread in Arctic warming controls spread in seasonal poleward heat transport. These seasonal changes and their intermodel spread are well‐captured by down‐gradient diffusive heat transport. While changes in moist and dry heat transport compensate in the annual‐mean, their opposite seasonality may support non‐compensating effects on Arctic warming. 

Abstract The climate response to the Mt. Pinatubo volcanic eruption is analyzed using large ensembles of Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. In contrast to previous work, we find that standard measures of the global temperature response to volcanic forcing are not significantly correlated with climate sensitivity across models. Isolating the shortwave response due to non‐cloud effects does not improve the correlation with climate sensitivity. Earlier constraints on climate sensitivity based on the response to Mt. Pinatubo are consistent with having arisen by chance because of the small size of the ensembles used. Our results suggest that the response to Mt. Pinatubo cannot be used to constrain the climate sensitivity to increased greenhouse gas concentrations, as has been proposed, because the radiative feedbacks in response to volcanic eruptions are not well correlated with the feedbacks governing the long‐term response to greenhouse gas forcing. 

Abstract Coupled global climate models (GCMs) generally fail to reproduce the observed sea‐surface temperature (SST) trend pattern since the 1980s. The model‐observation discrepancies may arise in part from the lack of realistic Antarctic ice‐sheet meltwater input in GCMs. Here we employ two sets of CESM1‐CAM5 simulations forced by anomalous Antarctic meltwater fluxes over 1980–2013 and through the 21st century. Both show a reduced global warming rate and an SST trend pattern that better resembles observations. The meltwater drives surface cooling in the Southern Ocean and the tropical southeast Pacific, in turn increasing low‐cloud cover and driving radiative feedbacks to become more stabilizing (corresponding to a lower effective climate sensitivity). These feedback changes can contribute as substantially as ocean heat uptake efficiency changes in reducing the global warming rate. Accurately projecting historical and future warming thus requires improved representation of Antarctic meltwater and its impacts. 

Abstract Observed surface temperature trends over recent decades are characterized by (a) intensified warming in the Indo‐Pacific Warm Pool and slight cooling in the eastern equatorial Pacific, consistent with Walker circulation strengthening, and (b) Southern Ocean cooling. In contrast, state‐of‐the‐art coupled climate models generally project enhanced warming in the eastern equatorial Pacific, Walker circulation weakening, and Southern Ocean warming. Here we investigate the ability of 16 climate model large ensembles to reproduce observed sea‐surface temperature and sea‐level pressure trends over 1979–2020 through a combination of externally forced climate change and internal variability. We find large‐scale differences between observed and modeled trends that are very unlikely (<5% probability) to occur due to internal variability as represented in models. Disparate trends in the ratio of Indo‐Pacific Warm Pool to tropical‐mean warming, which shows little multi‐decadal variability in models, hint that model biases in the response to historical forcing constitute part of the discrepancy. 

Abstract Arctic surface warming under greenhouse gas forcing peaks in winter and reaches its minimum during summer in both observations and model projections. Many mechanisms have been proposed to explain this seasonal asymmetry, but disentangling these processes remains a challenge in the interpretation of general circulation model (GCM) experiments. To isolate these mechanisms, we use an idealized single-column sea ice model (SCM) that captures the seasonal pattern of Arctic warming. SCM experiments demonstrate that as sea ice melts and exposes open ocean, the accompanying increase in effective surface heat capacity alone can produce the observed pattern of peak warming in early winter (shifting to late winter under increased forcing) by slowing the seasonal heating rate, thus delaying the phase and reducing the amplitude of the seasonal cycle of surface temperature. To investigate warming seasonality in more complex models, we perform GCM experiments that individually isolate sea ice albedo and thermodynamic effects under CO₂forcing. These also show a key role for the effective heat capacity of sea ice in promoting seasonal asymmetry through suppressing summer warming, in addition to precluding summer climatological inversions and a positive summer lapse-rate feedback. Peak winter warming in GCM experiments is further supported by a positive winter lapse-rate feedback, due to cold initial surface temperatures and strong surface-trapped warming that are enabled by the albedo effects of sea ice alone. While many factors contribute to the seasonal pattern of Arctic warming, these results highlight changes in effective surface heat capacity as a central mechanism supporting this seasonality. Significance StatementUnder increasing concentrations of atmospheric greenhouse gases, the strongest Arctic warming has occurred during early winter, but the reasons for this seasonal pattern of warming are not well understood. We use experiments in both simple and complex models with certain sea ice processes turned on and off to disentangle potential drivers of seasonality in Arctic warming. When sea ice melts and open ocean is exposed, surface temperatures are slower to reach the warm-season maximum and slower to cool back down below freezing in early winter. We find that this process alone can produce the observed pattern of maximum Arctic warming in early winter, highlighting a fundamental mechanism for the seasonality of Arctic warming. 

Abstract The realization that atmospheric radiative feedbacks depend on the underlying patterns of surface warming and global temperature, and thus, change over time has lead to a proliferation of feedback definitions and methods to estimate equilibrium climate sensitivity (ECS). We contrast three flavors of radiative feedbacks – equilibrium, effective, and differential feedback – and discuss their physical interpretations and applications. We show that their values at any given time can differ more than 1 and their implied equilibrium or effective climate sensitivity can differ several degrees. With ten (quasi) equilibrated climate models, we show that 400 years might be enough to estimate the true ECS within a 5% error using a simple regression method utilizing the differential feedback parameter. We argue that a community‐wide agreement on the interpretation of the different feedback definitions would advance the quest to narrow the estimate of climate sensitivity.