Over the last few decades, the modeling of atmospheric systems across all scales (global to catchment) has attracted a lot of attention within the global change community due to concerns about climate change and its potential impacts. Many of the widely used atmospheric models include a surface module through which land surface boundary conditions based on land cover, land use and elevation data are introduced into the model simulations. These land surface data sets are derived primarily from satellite sources, and by integrating them into atmospheric modeling systems; significant advances are being made in delineating the role of land surface structure and heterogeneity on global and regional climate. A key area of interest is the impact of land use and landscape changes on the partitioning of surface turbulent heat fluxes, and the boundary layer structure.

We present results from recent regional modeling case studies focusing on recently developed methodologies for ingesting high-resolution satellite-derived land cover datasets into the Colorado State University Regional Atmospheric Modeling System (RAMS). The experiments were designed to investigate the sensitivity of regional and local climate in the central United States to intra and inter-annually varying vegetation cover, leaf densities, and growth rates in the land surface sub-model of RAMS.