SENSITIVITY OF ATMOSPHERIC AND LAND SURFACE PROCESSES ON THE SNOW-RELATED PARAMETERS IN A LAND SURFACE MODEL: IMPLICATIONS FOR REGIONAL CLIMATE PROJECTIONS

Assessment of regional climate risk strongly depends on the reliability of regional climate model outputs. Among the surface conditions in a regional climate model, snow has one of the largest spatial and temporal variations as well as various physical properties from the aging processes. Accurate snow cover simulation is important in climate prediction because it modifies the absorption of solar radiation at the land-atmosphere interface. This process leads to a change in surface albedo, which in turn affects surface temperature and consequently the energy budgets of the lower atmosphere. Furthermore, uncertainty in snow process influences the water budgets via snow density and mass. In this study, we investigate sensitivities of near-surface atmospheric conditions and land surface process to various snow-related parameters by conducting sensitivity experiments using the Noah Land Surface Model (Noah LSM) coupled to the Weather Research and Forecasting (WRF) model. The Noah LSM uses a single-layer representation of the snow processes to simulate the snow cover fraction, albedo, and depth. Based on the sensitivity experiments, we suggest some parameters related to snow processes, which need to be optimized for improving future climate prediction: we also discuss implications of snow-related sensitivity for regional climate projections.