Improving Model Performance for Air Temperature and Precipitation by Assimilating Global Leaf Area Index Products

Vegetation parameter, especially the leaf area index (LAI), is a key variable that take part in the land-atmosphere interaction. Many studies have found that by updating LAI in land surface models can improve the simulation ability for land surface variables. This study used the coupled Community Land Model with explicit carbon and nitrogen components (CLM4CN) and Community Atmosphere Model (CAM) as well as Data Assimilation Research Testbed (DART) to update assimilated LAI, to improve the simulation ability for air temperature and precipitation. The Global Land Surface Data Assimilation System (GLDAS) reanalysis data were used to estimate the coupled CLM4CN/CAM/DART performance.