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Earth system models are valuable tools for understanding how the Arctic snow‐ice system and the feedbacks therein may respond to a warming climate. In this analysis, we investigate snow on Arctic sea ice to better understand how snow conditions may change under different forcing scenarios. First, we use in situ, airborne, and satellite observations to assess the realism of the Community Earth System Model (CESM) in simulating snow on Arctic sea ice. CESM versions one and two are evaluated, with V1 being the Large Ensemble experiment (CESM1‐LE) and V2 being configured with low‐ and high‐top atmospheric components. The assessment shows CESM2 underestimates snow depth and produces overly uniform snow distributions, whereas CESM1‐LE produces a highly variable, excessively‐thick snow cover. Observations indicate that snow in CESM2 accumulates too slowly in autumn, too quickly in winter‐spring, and melts too soon and rapidly in late spring. The 1950–2050 trends in annual mean snow depths are markedly smaller in CESM2 (−0.8 cm decade⁻¹) than in CESM1‐LE (−3.6 cm decade⁻¹) due to CESM2 having less snow overall. A perennial, thick sea‐ice cover, cool summers, and excessive summer snowfall facilitate a thicker, longer‐lasting snow cover in CESM1‐LE. Under the SSP5‐8.5 forcing scenario, CESM2 shows that, compared to present‐day, snow on Arctic sea ice will: (1) undergo enhanced, earlier spring melt, (2) accumulate less in summer‐autumn, (3) sublimate more, and (4) facilitate marginally more snow‐ice formation. CESM2 also reveals that summers with snow‐free ice can occur ∼30–60 years before an ice‐free central Arctic, which may promote faster sea‐ice melt.

 

We provide an assessment of the current and future states of Arctic sea ice simulated by the Community Earth System Model version 2 (CESM2). The CESM2 is the version of the CESM contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We analyze changes in Arctic sea ice cover in two CESM2 configurations with differing atmospheric components: the CESM2(CAM6) and the CESM2(WACCM6). Over the historical period, the CESM2(CAM6) winter ice thickness distribution is biased thin, which leads to lower summer ice area compared to CESM2(WACCM6) and observations. In both CESM2 configurations, the timing of first ice‐free conditions is insensitive to the choice of CMIP6 future emissions scenario. In fact, the probability of an ice‐free Arctic summer remains low only if global warming stays below 1.5°C, which none of the CMIP6 scenarios achieve. By the end of the 21st century, the CESM2 simulates less ocean heat loss during the fall months compared to its previous version, delaying sea ice formation and leading to ice‐free conditions for up to 8 months under the high emissions scenario. As a result, both CESM2 configurations exhibit an accelerated decline in winter and spring ice area, a behavior that had not been previously seen in CESM simulations. Differences in climate sensitivity and higher levels of atmospheric CO₂by 2100 in the CMIP6 high emissions scenario compared to its CMIP5 analog could explain why this winter ice loss was not previously simulated by the CESM.

 

Arctic and Antarctic sea ice has undergone significant and rapid change with the changing climate. Here, we present preindustrial and historical results from the newly released Community Earth System Model Version 2 (CESM2) to assess the Arctic and Antarctic sea ice. Two configurations of the CESM2 are available that differ only in their atmospheric model top and the inclusion of comprehensive atmospheric chemistry, including prognostic aerosols. The CESM2 configuration with comprehensive atmospheric chemistry has significantly thicker Arctic sea ice year‐round and better captures decreasing trends in sea ice extent and volume over the satellite period. In the Antarctic, both CESM configurations have similar mean state ice extent and volume, but the ice extent trends are opposite to satellite observations. We find that differences in the Arctic sea ice between CESM2 configurations are the result of differences in liquid clouds. Over the Arctic, the CESM2 configuration without prognostic aerosol formation has fewer aerosols to form cloud condensation nuclei, leading to thinner liquid clouds. As a result, the sea ice receives much more shortwave radiation early in the melt season, driving a stronger ice albedo feedback and leading to additional sea ice loss and significantly thinner ice year‐round. The aerosols necessary for the Arctic liquid cloud formation are produced from different precursor emissions and transported to the Arctic. Thus, the main reason sea ice differs in the Arctic is the transport of cloud‐impacting aerosols into the region, while the Antarctic remains relatively pristine from extrapolar aerosol transport.

 

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. 

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. 

Abstract. In recent decades, Arctic sea ice has shifted toward ayounger, thinner, seasonal ice regime. Studying and understanding this“new” Arctic will be the focus of a year-long ship campaign beginning inautumn 2019. Lagrangian tracking of sea ice floes in the Community EarthSystem Model Large Ensemble (CESM-LE) during representative “perennial”and “seasonal” time periods allows for understanding of the conditionsthat a floe could experience throughout the calendar year. These modeltracks, put into context a single year of observations, provide guidance onhow observations can optimally shape model development, and how climatemodels could be used in future campaign planning. The modeled floe tracksshow a range of possible trajectories, though a Transpolar Drift trajectoryis most likely. There is also a small but emerging possibility of high-risktracks, including possible melt of the floe before the end of a calendaryear. We find that a Lagrangian approach is essential in order to correctlycompare the seasonal cycle of sea ice conditions between point-basedobservations and a model. Because of high variability in the melt season seaice conditions, we recommend in situ sampling over a large range of ice conditionsfor a more complete understanding of how ice type and surface conditionsaffect the observed processes. We find that sea ice predictability emergesrapidly during the autumn freeze-up and anticipate that process-basedobservations during this period may help elucidate the processes leading tothis change in predictability.