Assessment of Regional Climate Model-Simulated Snow Density Over the Greenland and Antarctic Ice Sheets Using In-Situ Measurements

Friday, 19 December 2014
Patrick M Alexander1,2, Lora Koenig3, Marco Tedesco1,2, Rajashree Datta1,2 and Xavier Fettweis4, (1)CUNY Graduate School and University Center, New York, NY, United States, (2)City College, City University of New York, New York, NY, United States, (3)NASA, Greenbelt, MD, United States, (4)University of Liège, Geography, Liège, Belgium
An accurate representation of density of snow and firn at the surface of the Greenland and Antarctic ice sheets is important for both models and measurements of ice sheet mass change, and therefore estimates of ice sheet contribution to past and future sea level rise. In particular, mass change derived from satellite and airborne snow accumulation measurements (e.g. accumulation from IceBridge, and volume changes from ICESat and Cryosat-2) rely on estimates of snow and firn density to convert measured surface elevation changes into estimates of mass change. While numerous firn densification models (FDMs) have been applied over both ice sheets, there has been little evaluation of the density simulated by regional climate models (RCMs). RCMs capture the coupling between the surface-and the atmosphere, as well as sub-surface hydrology and thermodynamics, and are used to make future projections of ice sheet mass change. Improving accuracy of simulated density is important for improved representation of RCM-simulated surface processes. Here we present an initial validation of density profiles in the Modèle Atmosphérique Régionale (MAR) RCM against in situ data from the SUMup community dataset. An analysis of initial results indicates that MAR tends underestimate surface density in the first two meters of the snowpack at the cores examined. Additionally, initialization of the MAR snowpack may lead to errors in subsurface density in some locations. These biases and errors may impact simulation of storage of meltwater within the firn and may lead to an underestimation of mass changes if simulated density is combined with remote-sensing-derived accumulation estimates.