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Abstract Transient climate sensitivity is strongly shaped by geographical patterns of ocean heat uptake (OHU). To isolate the effects of uncertainties associated with OHU, a single slab ocean model is forced with doubled CO₂and an ensemble of OHU patterns diagnosed from transient warming scenarios in 12 fully coupled models. The single-model ensemble produces a wide range of Southern Ocean (SO) sea surface temperature (SST) and Antarctic sea ice responses, which are in turn associated with a 1.1–2.0-K range of transient climate response (TCR). Feedback analysis attributes the TCR spread primarily to shortwave effects of low clouds in the Southern Hemisphere (SH) midlatitudes. These cloud changes are strongly positively correlated with storm-track eddy kinetic energy. It is argued that midlatitude clouds (and thus planetary albedo) are remotely driven by SO SST and Antarctic sea ice, mediated by large-scale changes in SH baroclinicity and lower-tropospheric stability. The robustness of this atmospheric teleconnection between SO SST, Antarctic sea ice, and global feedback through midlatitude clouds is supported through additional simulations that explore more extreme SST and sea ice perturbations. These results highlight the importance of understanding physical relationships between SST, sea ice, circulation, and cloud changes in the SH as a pathway to better constraining transient climate sensitivity. Significance StatementAlthough it is well known that Earth’s global-mean surface temperature increases with increasing atmospheric CO₂, there are still significant uncertainties in the temperature and sea ice trends over the Southern Ocean region. Using a climate model, we find that Southern Ocean temperature and Antarctic sea ice changes can result in substantial cloud cover changes over the Southern Hemisphere, which play a primary role in determining the amount of warming in our experiments. We suggest that, in order to reduce uncertainty in future climate change, more work is needed to understand how the climate of the southern polar region can affect the circulation and clouds of the midlatitudes. 

Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The internal components of AMV and PMV can enhance or weaken the decadal sea ice loss rates over the marginal Arctic Ocean by more than 50%. The AMV- or PMV-induced anomalous atmospheric energy transport and downward longwave radiation related to low clouds (thermodynamical processes) and sea ice motion (dynamical processes) contribute to the Arctic surface air temperature and sea ice concentration and thickness changes. Anomalous oceanic heat flux is mainly a response to rather than a cause of sea ice variations. The dynamic processes contribute to the winter Arctic sea ice variations as much as the thermodynamic processes, but they contribute less (more) to the summer Arctic sea ice variability than the thermodynamic processes over the marginal Arctic Ocean (parts of the central Arctic Ocean). Sea ice loss enhances air–sea heat fluxes, which cause oceanic heat convergence and warm near-surface air and the lower troposphere, which in turn melt more sea ice. 

Abstract This study quantifies the contribution to Arctic winter surface warming from changes in the tropospheric energy transport (F_trop) and the efficiency with whichF_tropheats the surface in the RCP8.5 warming scenario of the Community Earth System Model Large Ensemble. A metric for this efficiency,E_trop, measures the fraction of anomalousF_tropthat is balanced by an anomalous net surface flux (NSF). Drivers ofE_tropare identified in synoptic‐scale events during whichF_tropis the dominant driver of NSF.E_tropis sensitive to the vertical structure ofF_tropand pre‐existing Arctic lower‐tropospheric stability (LTS). In RCP8.5, winter‐meanF_tropdecreases from 95.1 to 85.4 W m⁻², whileE_tropincreases by 5.7%, likely driven by decreased Arctic LTS, indicating an increased coupling betweenF_tropand the surface energy budget. The net impact of decreasingF_tropand increasing efficiency is a positive 0.7 W m⁻²contribution to winter‐season surface heating.