Evaluation of a WRF Dynamical Downscaling Simulation Over the Ogallala Aquifer Region

Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models (GCMs), and the CMIP5 data archive provides the opportunity to dynamically downscale multiple GCM results to develop high-resolution climate projections. This paper presents results from a 6 year (2011-2016) Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the Ogallala Aquifer Region (OAR), and forced by a 0.9375^o x 1.25^o Community Climate System Model Ver. 4 (CCSM4) simulation from CMIP5 data archive. This regional model reproduces the spatial distribution of surface temperature quite well. During the drought years, the surface temperature from WRF shows a 2-3 degree warm bias over the OAR because of overly-dry soil moisture. Modeled precipitation in the spatial distribution agrees quite well with observations, but substantially overestimates rainfall over the ORA, with the mean bias in average annual precipitation being over +50%.