The overall objective of this project is to improve accuracy of climate simulations in mountain/snow regions that are particularly vulnerable to climate change and global warming. In particular, our preliminary research has demonstrated the significant upscaled effect of local topography on surface energy budgets. In order to meet this challenge, this research will focus on two associative problems, namely:

1. 3D radiative transfer over mountain/snow and its parameterization
2. Incorporation of this parameterization in a coupled model involving the Weather Research and Forecasting (WRF) model and Community Land Model (CLM), including improvement in computational efficiency.

First, a 3D radiative transfer parameterization will be built based on the first principle for application to intense and inhomogeneous topography, a common land feature, including snow in all sky conditions (clear, aerosols, and clouds). In this quest, we have developed a 3D Monte Carlo photon tracing program designed specifically for application to mountains/snow fields and its simplified parameterization on the basis of multiple regression analysis for direct, diffuse, and coupled surface fluxes in the regional context. In order to cover a spectrum of high-resolution models, including the input topographical data, we have proposed an efficient computational approach to time-consuming and tedious 3D Monte Carlo photon tracing simulations. Second, we will investigate the impacts of the proposed 3D radiative transfer parameterization on land-surface energy budget and its potential feedback to regional climate using a coupled land-atmosphere model, based on WRF and CLM as the testbed. We plan to conduct simulations using WRFCLM with and without the 3D solar flux corrections estimated by the regression-based parameterization associated with canopy radiative transfer calculations. Climate simulations will also be performed for a 10-year period using WRF-CLM driven by global reanalysis data to evaluate the effects of mountain-induced solar fluxes with reference to seasonal and interannual variability. The algorithm currently used in generating conservative grid mapping for the flux coupler between WRF and CLM demands substantial computational time and is not robust for high-resolution grids. An approach has been proposed to improve computational efficiency and robustness for generating conservative grid mapping for coupling Earth system components in global and regional models. In view of the above, the proposed research and subsequent results will, for the first time, provide the models and datasets necessary to address a number of unsolved issues, as well as physical insights with respect to the effects of intense topography on direct, diffuse, and coupled solar fluxes at the surface and their impacts on the regional surface energy balance, which are critical to numerous surface processes including snowmelt, vegetation dynamics, and water resources. Moreover, the novel efforts proposed herein will, in conjunction with regional climate modeling, add a new dimension to parameterization of surface radiative processes as a part of capacity/community building activities.