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Most observed patterns of recent Arctic surface warming and sea ice loss lie outside of unforced internal climate variability. In contrast, human influence on related changes in outgoing longwave radiation has not been assessed. Outgoing longwave radiation captures the flow of thermal energy from the surface through the atmosphere to space, making it an essential indicator of Arctic change. Furthermore, satellites have measured pan-Arctic radiation for two decades while surface temperature observations remain spatially and temporally sparse. Here, two climate model initial-condition large ensembles and satellite observations are used to investigate when and why twenty-first-century Arctic outgoing longwave radiation changes emerge from unforced internal climate variability. Observationally, outgoing longwave radiation changes from 2001 to 2021 are within the range of unforced internal variability for all months except October. The model-predicted timing of Arctic longwave radiation emergence varies throughout the year. Specifically, fall emergence occurs a decade earlier than spring emergence. These large emergence timing differences result from seasonally dependent sea ice loss and surface warming. The atmosphere and clouds then widen these seasonal differences by delaying emergence more in the spring and winter than in the fall. Finally, comparison of the two ensembles shows that more sea ice andmore »a more transparent atmosphere during the melt season led to an earlier emergence of forced longwave radiation changes. Overall, these findings demonstrate that attributing changes in Arctic outgoing longwave radiation to human influence requires understanding the seasonality of both forced change and internal climate variability.

Ice nucleation in mixed‐phase clouds has been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. However, with present‐day global mean cloud phase adjusted to replicate satellite retrievals, similar total cloud feedback is attained whether ice nucleation is simulated as aerosol‐sensitive, insensitive, or absent. These model experiments all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings indicate that this update reduced global cloud phase bias, with CESM2's high climate sensitivity reflecting improved mixed‐phase cloud representation.

The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time‐step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed‐phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid‐to‐high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate‐calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol‐cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics thanmore »CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection.