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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. 

Abstract. The melting of sea ice floes from the edges (lateral melting) results in open-water formation and subsequently increases absorption of solar shortwave energy. However, lateral melt plays a small role in the sea ice mass budget in both hemispheres in most climate models. This is likely influenced by the simple parameterization of lateral melting in sea ice models that are constrained by limited observations. Here we use a coupled climate model (CESM2.0) to assess the sensitivity of modeled sea ice state to the lateral melt parameterization in preindustrial and 2×CO2 runs. The runs explore the implications of how lateral melting is parameterized and structural changes in how it is applied. The results show that sea ice is sensitive both to the parameters determining the effective lateral melt rate and the nuances in how lateral melting is applied to the ice pack. Increasing the lateral melt rate is largely compensated for by decreases in the basal melt rate but still results in a significant decrease in sea ice concentration and thickness, particularly in the marginal ice zone. Our analysis suggests that this is tied to the increased efficiency of lateral melting at forming open water during the summer melt season, which drives the majority of the ice–albedo feedback. The more seasonal Southern Hemisphere ice cover undergoes larger relative reductions in sea ice concentration and thickness for the same relative increase in lateral melt rate, likely due to the hemispheric differences in the role of the sea-ice–upper-ocean coupling.Additionally, increasing the lateral melt rate under a 2×CO2 forcing, where sea ice is thinner, results in a smaller relative change in sea ice mean state but suggests that open-water-formation feedbacks are likely to steepen the decline to ice-free summer conditions.Overall, melt processes are more efficient at forming open water in thinner ice scenarios (as we are likely to see in the future), suggesting the importance of accurately representing thermodynamic evolution. Revisiting model parameterizations of lateral melting with observations will require finding new ways to represent salient physical processes. 

The Arctic is undergoing a pronounced and rapid transformation in response to changing greenhouse gasses, including reduction in sea ice extent and thickness. There are also projected increases in near‐surface Arctic wind. This study addresses how the winds trends may be driven by changing surface roughness and/or stability in different Arctic regions and seasons, something that has not yet been thoroughly investigated. We analyze 50 experiments from the Community Earth System Model Version 2 (CESM2) Large Ensemble and five experiments using CESM2 with an artificially decreased sea ice roughness to match that of the open ocean. We find that with a smoother surface there are higher mean wind speeds and slower mean ice speeds in the autumn, winter, and spring. The artificially reduced surface roughness also strongly impacts the wind speed trends in autumn and winter, and we find that atmospheric stability changes are also important contributors to driving wind trends in both experiments. In contrast to the clear impacts on winds, the sea ice mean state and trends are statistically indistinguishable, suggesting that near‐surface winds are not major drivers of Arctic sea ice loss. Two major results of this work are: (a) the near‐surface wind trends are driven by changes in both surface roughness and near‐surface atmospheric stability that are themselves changing from sea ice loss, and (b) the sea ice mean state and trends are driven by the overall warming trend due to increasing greenhouse gas emissions and not significantly impacted by coupled feedbacks with the surface winds.

 

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

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. 

Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.

 

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.