Representation of the Land Surface in the Regional Arctic System Model (RASM)

Monday, 15 December 2014
Joseph Hamman1, Michael Brunke2, Xubin Zeng2, Andrew Roberts3, Wieslaw Maslowski3, Dennis P Lettenmaier1 and Bart Nijssen4, (1)University of Washington, Seattle, WA, United States, (2)University of Arizona, Tucson, AZ, United States, (3)Naval Postgraduate School, Monterey, CA, United States, (4)University of Washington, seattle, WA, United States
The Arctic region is expected to experience disproportionately severe impacts due to climate change, affecting sea ice, seasonal snow cover, streamflow, permafrost, glaciers, and ice sheets as well as terrestrial and aquatic ecosystems. Through the use of the Regional Arctic System Model (RASM), a fully coupled regional earth system model, we aim to better understand how changes in these processes may prompt non-linear responses and feedbacks throughout the Arctic region climate system. The net effect of these feedbacks is not easily understood without the use of coupled earth system models that allow us to evaluate the interactions between components of the climate system; determine the extent, magnitude, and sign of complex feedback processes; and to project the climate system’s response to future predictions. RASM is a high resolution, regional, coupled atmosphere - land - sea ice - ocean model that uses the Community Earth System Model (CESM) coupling infrastructure over a Pan-Arctic domain. RASM is composed of the Weather Research and Forecasting (WRF) atmospheric model, the Variable Infiltration Capacity (VIC) hydrology model, the RVIC streamflow routing model, the Parallel Ocean Program (POP) model and the Los Alamos Sea Ice model (CICE). We evaluate RASM’s abilities to capture key features of the land surface climate and hydrological cycle over the recent decades (1979-2009) through comparisons with uncoupled simulations, reanalysis data sets, satellite measurements and in-situ observations. Of particular interest are the model’s ability to capture diurnal, seasonal, and interannual variations in hydrological fluxes and states and the interaction between model components.