Abstract

Dynamical downscaling modeling (DDM) is important to understand regional climate change and develop local mitigation strategies, and the accuracy of DDM depends on the physical processes involved in the regional climate model as well as the forcing datasets derived from global models. This study ... Show moreDynamical downscaling modeling (DDM) is important to understand regional climate change and develop local mitigation strategies, and the accuracy of DDM depends on the physical processes involved in the regional climate model as well as the forcing datasets derived from global models. This study investigates the relative role of the land surface schemes and forcing datasets in the DDM over the Tibet Plateau (TP), a region complex in topography and vulnerable to climate change. Three Weather Research and Forecasting model dynamical downscaling simulations configured with two land surface schemes [Noah versus Noah with multiparameterization (Noah-MP)] and two forcing datasets are performed over the period of 1980-2005. The downscaled temperature and precipitation are evaluated with observations and inter-compared regarding temporal trends, spatial distributions, and climatology. Results show that the temporal trends of the temperature and precipitation are determined by the forcing datasets, and the forcing dataset with the smallest trend bias performs the best. Relative to the forcing datasets, land surface processes play a more critical role in the DDM over the TP due to the strong heating effects on the atmospheric circulation from a vast area at exceptionally high elevations. By changing the vertical profiles of temperature in the atmosphere and the horizontal patterns of moisture advection during the monsoon seasons, the land surface schemes significantly regulate the downscaled temperature and precipitation in terms of climatology and spatial patterns. This study emphasizes the selection of land surface schemes is of crucial importance in the successful DDM over the TP. Show less