The Weather Research and Forecasting (WRF) model version 3.0 developed by the National Center for Atmospheric Research (NCAR) includes three land surface schemes: the simple soil thermal diffusion (STD) scheme, the Noah scheme, and the Rapid Update Cycle (RUC) scheme. We have recently coupled the sophisticated NCAR Community Land Model version 3 (CLM3) into WRF to better characterize land surface processes. Among these four land surface schemes, the STD scheme is the simplest in both structure and process physics. The Noah and RUC schemes are at the intermediate level of complexity. CLM3 includes the most sophisticated snow, soil, and vegetation physics among these land surface schemes. WRF simulations with all four land surface schemes over the western United States (WUS) were carried out for the 1 October 1995 through 30 September 1996. The results show that land surface processes strongly affect temperature simulations over the (WUS). As compared to observations, WRF-CLM3 with the highest complexity level significantly improves temperature simulations, except for the wintertime maximum temperature. Precipitation is dramatically overestimated by WRF with all four land surface schemes over the (WUS) analyzed in this study and does not show a close relationship with land surface processes. 1. Introduction Fossil fuel emissions have caused a 0.6°C increase in global temperature during the last 100 years (Hansen et al. [1]), with an anticipated additional 2–5°C temperature increase by the end of this century (The Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) 2007). Climate change impacts (e.g., extreme heat, severe storms, and air pollution inversion episodes) are especially significant at regional scales, where society and ecosystems are most sensitive (IPCC AR4 2007). Thus, accurate regional climate model (RCM) simulations with reduced uncertainties are needed to better assess the limits of climate change impacts. RCM uncertainties include the spatiotemporal distribution of precipitation, its type, amount, and intensity, snow mass accumulation and melt rates, and daily minimum and maximum temperature. Quantifying these uncertainties and improving operational monthly to interannual regional climate predictions are especially important for sustaining the health of local human and ecosystems environments. To improve the accuracy of RCM forecasts, we need to understand physical mechanisms and processes that control regional climate change. An important process that regulates regional climate is the global increase in the
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