资源环境科技发展态势分析平台

The Greenland Ice Sheet is now the single largest cryospheric contributor to global sea-level rise yet uncertainty remains about its future contribution due to complex interactions between increasing snowfall and surface melt. Reducing uncertainty in future snowfall predictions requires sophisticated, physically based climate models evaluated with present-day observations. The accuracy of modeled snowfall rates, however, has yet to be systematically assessed because observations are sparse. Here, we produce high spatial resolution (15 km) snowfall climatologies (2006-2016) derived from CloudSat's 2C-SNOW-PROFILE product to evaluate climate model simulations of snowfall across the Greenland Ice Sheet. In comparison to accumulation datasets acquired from ice cores and airborne accumulation radar, we find that our CloudSat climatologies capture broad spatial patterns of snowfall in both the accumulation and ablation zones. By comparing our CloudSat snowfall climatologies with the Regional Atmospheric Climate Model Version 2.3p2 (RACMO2.3p2), Modele Atmospherique Regional 3.9 (MAR3.9), ERA5, and Community Earth System Model version 1 (CESM1), we demonstrate that climate models likely overestimate snowfall rates at the margins of the ice sheet, particularly in South, Southeast, and Northwest Greenland during autumn and winter. Despite this overestimation, there are few areas of the ice sheet where the models and CloudSat substantially disagree about the spatial pattern and seasonality of snowfall rates. We conclude that a combination of CloudSat snowfall observations and the latest generation of climate models has the potential to improve understanding of how snowfall rates respond to increasing air temperatures, thereby constraining one of the largest sources of uncertainty in Greenland's future contribution to global sea levels.