Evaluation of the Sensitivity of the Weather Research and Forecasting Model to\ud Parameterization Schemes for Regional Climates of Europe over the Period 1990–95

摘要

The Weather Research and Forecasting model (WRF) is used to downscale interim ECMWF Re-Analysis\ud(ERA-Interim) data for the climate over Europe for the period 1990–95 with grid spacing of 0.448 for 12\udcombinations of physical parameterizations. Two longwave radiation schemes, two land surface models\ud(LSMs), two microphysics schemes, and two planetary boundary layer (PBL) schemes have been investigated\udwhile the remaining physics schemes were unchanged. WRF simulations are compared with Ensemble-Based\udPredictions of Climate Changes and their Impacts (ENSEMBLES) observations gridded dataset (E-OBS)\udfor surface air temperatures (T2), precipitation, and mean sea level pressure (MSLP) in eight subregions within\udthe model domain to assess the performance of the different parameterizations on widely varying regional\udclimates. This work shows that T2 is modeled well byWRF with high correlation coefficients (0.8 , R , 0.95)\udand biases less than 48C. T2 shows greatest sensitivity to land surface models, some sensitivity to longwave\udradiation schemes, and less sensitivity to microphysics and PBL schemes. Precipitation is not well modeled\udby WRF with low correlation coefficients (0.1 , R , 0.3) and high root-mean-square differences (RMSDs;\ud8–9 mm day21). Precipitation shows sensitivity to LSMs in summer. No significant bias has been observed in\udtheMSLP modeled byWRF. Correlation coefficients are typically in the range 0.7,R,0.8 whileRMSDs are\udin the range 6–10 hPa. MSLP output is sensitive to longwave radiation scheme in summer but is relatively\udinsensitive to either microphysics or the choice of LSM. The optimum combination of parameterizations for all\udthree state variables examined is strongly dependent on subregion and demonstrates the need to carefully\udselect parameterization combinations when attempting to use WRF as a regional climate model.