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Over thousands of years, Indigenous hunters in the Bering and Chukchi seas have adapted to changes in weather, sea ice, and sea state that influence their access to walruses. In recent decades, 10 however, those conditions have been changing at unprecedented rates. Safely adapting to changing conditions will be essential to the well-being of communities. 

Abstract Antarctic sea ice exhibits considerable regional variability that is influenced by ocean and atmospheric conditions. Previous studies have suggested that this variability may be predictable on seasonal-to-interannual time scales. Here, we use initial-value predictability experiments of the Community Earth System Model, version 2 (CESM2), paired with analysis of the CESM2 large ensemble, to further assess the inherent predictability in regional Antarctic sea ice conditions. As in previous studies, we find that Antarctic sea ice area predictability is high for several months after initialization. It is then lost when ice retreats, and predictability is regained in the following ice advance period. In our simulations, this process acts on multiyear time scales with little sensitivity to the seasonal initialization timing but has a strong regional dependence. Long-lived ocean temperature anomalies in the vicinity of the winter ice edge are the primary source of sea ice predictability. Different predictability characteristics occur across regions, depending on how these ocean temperature anomalies are advected relative to regional sea zones. Our results show that sea ice predictability can impart predictability to primary productivity in the Southern Ocean due to its impact on light availability. This has implications for the understanding and management of Southern Ocean marine ecosystems. 

Light transmission through a sea ice cover has strong implications for the heat content of the upper ocean, the magnitude of bottom and lateral ice melt, and primary productivity in the ocean. Light transmittance in the vicinity of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory was estimated by driving a two-stream radiative transfer model with physical property observations. Data include point and transect observations of snow depth, surface scattering layer thickness, ice thickness, and pond depth. The temporal evolution of light transmittance at specific sites and the spatial variability along transect lines were computed. Ponds transmitted 4–6 times as much solar energy per unit area as bare ice. On July 25, ponds covered about 18% of the area and contributed roughly 50% of the sunlight transmitted through the ice cover. Approximating the transmittance along a transect line using average values for the physical properties will always result in lower light transmittance than finding the average light transmittance using the full distribution of points. Transmitted solar energy calculated using the standard five ice thickness categories and three surface types used in the Los Alamos sea ice model CICE, the sea ice component of many weather and climate models, was only about 1 W m−2 less than using all the points along the transect. This minor difference suggests that the important processes and resulting feedbacks relating to solar transmittance can be represented in models that use five or more categories of ice thickness distributions. 

Abstract. Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight into uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations has changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of the model structure and forcing vary across the globe, and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3–7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change, despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1. 

Abstract. In the high-latitude Arctic, wintertime sea ice and snowinsulate the relatively warmer ocean from the colder atmosphere. While theclimate warms, wintertime Arctic surface heat fluxes remain dominated by theinsulating effects of snow and sea ice covering the ocean until the sea icethins enough or sea ice concentrations decrease enough to allow for directocean–atmosphere heat fluxes. The Community Earth System Model version 1 LargeEnsemble (CESM1-LE) simulates increases in wintertime conductive heat fluxesin the ice-covered Arctic Ocean by ∼ 7–11 W m−2 bythe mid-21st century, thereby driving an increased warming of theatmosphere. These increased fluxes are due to both thinning sea ice anddecreasing snow on sea ice. The simulations analyzed here use a sub-grid-scaleice thickness distribution. Surface heat flux estimates calculated usinggrid-cell mean values of sea ice thicknesses underestimate mean heat fluxesby ∼16 %–35 % and overestimate changes in conductive heatfluxes by up to ∼36 % in the wintertime Arctic basin evenwhen sea ice concentrations remain above 95 %. These results highlight howwintertime conductive heat fluxes will increase in a warming world evenduring times when sea ice concentrations remain high and that snow and thedistribution of snow significantly impact large-scale calculations ofwintertime surface heat budgets in the Arctic. 

Under rising atmospheric greenhouse gas concentrations, the Arctic exhibits amplified warming relative to the globe. This Arctic amplification is a defining feature of global warming. However, the Arctic is also home to large internal variability, which can make the detection of a forced climate response difficult. Here we use results from seven model large ensembles, which have different rates of Arctic warming and sea ice loss, to assess the time of emergence of anthropogenically-forced Arctic amplification. We find that this time of emergence occurs at the turn of the century in all models, ranging across the models by a decade from 1994–2005. We also assess transient changes in this amplified signal across the 21st century and beyond. Over the 21st century, the projections indicate that the maximum Arctic warming will transition from fall to winter due to sea ice reductions that extend further into the fall. Additionally, the magnitude of the annual amplification signal declines over the 21st century associated in part with a weakening albedo feedback strength. In a simulation that extends to the 23rd century, we find that as sea ice cover is completely lost, there is little further reduction in the surface albedo and Arctic amplification saturates at a level that is reduced from its 21st century value. 

Vertical heat conduction through young ice is a major source of wintertime sea ice growth in the Arctic. However, field observations indicate that young ice preferentially accumulates wind-blown snow, resulting in greater snow thickness on young ice than would be expected from precipitation alone, and hence greater snow thickness on young ice than climate models represent. As snow has a low thermal conductivity, this additional snow thickness due to redistribution will reduce the actual heat conduction. We present new observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate Expedition which show that young ice rapidly accumulates a snow thickness of 2.5–8 cm, when wind-blown snow is available from the nearby mature ice. By applying a simple redistribution scheme and heat flux model to simulated conditions from the Community Earth System Model 2.0, we suggest that neglecting this snow redistribution onto young ice could result in the potential overestimation of conductive heat flux—and hence ice growth rates—by 3–8% on average in the Arctic in the winter in the absence of climate feedbacks. The impacts of snow redistribution are highest in the springtime and in coastal regions. 

The sensitivity of sea ice to fire emissions highlights climate model uncertainty related to the accuracy of prescribed forcings.