Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data

• Published in: Advances in atmospheric sciences Vol. 38; no. 2; pp. 168 - 186
• Main Authors: Chen, Shengzhe, Liu, Jiping, Ding, Yifan, Zhang, Yuanyuan, Cheng, Xiao, Hu, Yongyun
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.02.2021; Springer Nature B.V; Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY 12222, USA%College of Global Change and Earth System Science, and State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; University Corporation for Polar Research, Beijing 100875, China%Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China; University Corporation for Polar Research, Beijing 100875, China%School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519000, China
• Subjects: Arctic sea ice; Atmospheric models; Atmospheric Sciences; Biogeochemistry; Climate models; Climatology; CMIP6 Experiments: Model and Dataset Descriptions; Depth; Earth and Environmental Science; Earth Sciences; Energy budget; Geophysics/Geodesy; Interannual variability; Intercomparison; Meteorology; Original Paper; Satellite data; Satellites; Sea ice; Simulation; Snow; Snow accumulation; Snow depth; Thinning; Variability; Arctic region; Arctic sea ice; 北极; CMIP6; 积雪; 海冰; 预测; 厚度; satellite; projection; 卫星; snow depth
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-020-0213-5

Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.