Global remote sensing and in-situ land surface data is crucial to the understanding of the land-atmosphere interaction which is one of the most important elements in the global climate system. We have recently developed a comprehensive land surface dataset for use in global and regional land modeling studies. The most important data include the global 1 km fractional vegetation cover and land cover classification, global 8 km green leaf area index, vegetation root distribution from global field survey, and vegetation albedo (under dense canopy conditions) and soil color (related to soil albedo) based on literature survey and remote sensing data. Other parameters are derived from the above data or prescribed based on literature survey.

Using the above dataset, the Common Land Model, which results from a multiyear joint effort among seven land modeling groups, has been coupled with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3). The mosaic approach is used to treat the land-atmosphere interface with up to five tiles (i.e., the first two dominant vegetation types, bare soil, lakes, and wetland) in each atmospheric model grid box over land. Two 15-year simulations of CCM3 coupled with CLM and the NCAR Land Surface Model (LSM) respectively are used to document the relative impact of CLM versus LSM on land surface climate.

It is found that CLM significantly reduces the cold bias of surface air temperature in LSM (particularly in summer). CLM also significantly improves the simulation of the annual cycle of runoff in LSM. In addition, CLM simulates the snow mass better than LSM during the snow accumulation stage. Other aspects of the simulations of CLM versus LSM are relatively close.