ALBERT

Description: This study investigates the role of physical parameterization in regional climate model simulations. The authors also present a comprehensive assessment of errors arising from use of physical parameterization schemes, and their consequent impact on model performance in a region of complex topography. An error range related to the choice of physical parameterization is provided for 2-m air temperature T2m and precipitation. Two state-of-the-art nonhydrostatic mesoscale regional climate models, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model, are used to dynamically downscale the 40-yr ECMWF Re-Analysis (ERA-40) to a spatial resolution of 10 km × 10 km in the European alpine region. Simulated T2m and precipitation are compared with gridded observational datasets. The model performance on regional and subregional scales is evaluated on daily, monthly, seasonal, and annual time scales. The results are based on a mixed physics ensemble of twenty-nine 1-yr-long hindcast simulations generated by choosing different model configurations. These results indicate that performance of both models is sensitive to the choice of physical parameterization and WRF is more sensitive than MM5. This sensitivity is higher during summer than during winter. The cumulus and microphysics scheme have the dominant effect on model performance during summer while boundary layer and radiation schemes affect the results during all seasons. It is found that annual mean error in the alpine region for T2m and precipitation lies between −2.75° and −1.08°C (−1.12° and 1.33°C) and 0.27 and 0.80 mm day−1 (0.13 and 1.51 mm day−1) for MM5 (WRF), respectively. The authors also found that the error range during winter for subregional scales is higher than the regional-scale error range, while during the other seasons the subregional error ranges are higher only in case of precipitation but not for T2m. These results suggest that significant reductions of errors can be achieved by choosing a suitable model configuration for the region of interest. The other outcome of these experiments is a suitable setup, which can be used to conduct further long-term high-resolution climate simulations.