929A Navigating Our Understanding of Future Greenland Ice Sheet Precipitation with CESM Simulations and CloudSat Observations

Wednesday, 9 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Michael A. Camron, Univ. of Colorado Boulder, Boulder, CO; and J. Lenaerts, J. Kay, and T. L'Ecuyer

The Greenland Ice Sheet (GIS) represents a complex feedback system with impact on sea-level rise in a warming climate. Whereas recent work has mostly focused how ice sheets will melt in a warming climate in response to changes in clouds, we aim to understand precipitation, the largest positive contributor to ice sheet mass balance. Snowfall becoming lighter or transitioning to rainfall could accelerate future ice mass loss. Here, we employ the CloudSat cloud-profiling radar level 2C PRECIP-COLUMN (2CPC) precipitation frequency product to characterize our current view of rain and snow over the GIS. To co-evaluate this current method of satellite precipitation observation alongside simulations of Earth System Models, a satellite observation simulator (CFMIP-COSP) provides us with a unique opportunity to simulate a modern-day CloudSat mission in the Community Earth System Model version 1 (CESM1) and recreate an identical 2CPC product within multiple 100-year simulations of various emissions scenarios. We use this frequency of near-surface precipitation categorized by intensity to fully describe spacial and temporal patterns in precipitation. In the snowfall and rainfall categories, CESM1 COSP well represents the structure of average seasonal snowfall over the GIS, but exhibits a frequently-documented bias in higher precipitation throughout all categories, especially over oceans surrounding the GIS. For total snowfall relative to total rainfall, CESM1 COSP agrees with observed spatial and temporal patterns, especially over the interior of the ice sheet. In the CESM1 simulations driven through 2100 along the RCP8.5 trajectory, we discover seasonal shifts of heavy snowfall towards reduced snowfall or even fully transitioning from snow to rain, especially in the particularly sensitive GIS coastal region. These consistent comparisons develop a unique perspective for present model understanding while highlighting considerations for future remote sensing products and development.
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