A SUBGRID PARAMETERIZATION OF OROGRAPHIC PRECIPITATION

Estimates of the impact of global climate change on land surface hydrology require climate information on spatial scales far smaller than those explicitly resolved by global climate models of today and the foreseeable future. To bridge the gap between what is required and what is resolved, we propose a subgrid-scale parameterization of the influence of topography on clouds, precipitation, and land surface hydrology. The parameterization represents subgrid variations in surface elevation in terms of probability distributions of discrete elevation classes. Separate cloud, radiative, and surface processes are calculated for each elevation class. Rainshadow effects are not treated by the parameterization; they have to be explicitly resolved by the host model. The simulated surface temperature, precipitation, and snow cover for each elevation class are distributed to different geographical locations according to the spatial distribution of surface elevation within each grid cell. The subgrid parameterization has been implemented in the Pacific Northwest Laboratory's climate version of the Penn State/NCAR Mesoscale Model. The scheme is evaluated by driving the regional climate model with observed lateral boundary conditions for the Pacific Northwest and comparing simulated fields with surface observations. The method yields more realistic spatial distributions of precipitation and snow cover in mountainous areas and is considerably more computationally efficient than achieving high resolution by the use of nesting in the regional climate model.