Snow on Arctic Sea Ice in a Warming Climate as Simulated in CESM

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 similar to 30-60 years before an ice-free central Arctic, which may promote faster sea-ice melt.

Automated summary

Earth system models are valuable for understanding the Arctic snow-ice system and its responses to a warming climate. The study reveals that CESM2 underestimates snow depth while CESM1-LE produces a highly variable snow cover. The trends in snow depth over the years differ significantly between the two models.