Testing the Sensitivity of a WRF-based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging

Abstract

The Weather Research and Forecasting (WRF) model is used to dynamically down-scale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes Region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2 m temperature and precipitation accumulation in the Great Lakes Drainage Basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated.