Assimilation of Remotely Sensed LAI Into CLM4CN Using DART

Abstract Plant leaves play an important role in water, carbon, and energy exchanges between terrestrial ecosystems and atmosphere. Assimilating remotely sensed leaf area index (LAI) into land surface models is a promising approach to improve our understanding of those processes. Toward this goal, this study uses the Community Land Model with carbon and nitrogen components (CLM4CN) coupled with the Data Assimilation Research Testbed (DART). Global Land Surface Satellite (GLASS) LAI data are assimilated via the Ensemble Adjustment Kalman Filter. A random 40‐member atmospheric forcing ensemble is used to drive the CLM4CN to provide background error covariance. The results show that assimilating GLASS LAI and updating both LAI and leaf C/N is an effective method to provide a high‐accuracy estimate of LAI. The simulations always systematically overestimate LAI, especially in low‐latitude regions, with the largest bias up to 5 m2/m2, which are effectively corrected in the analyzed LAI, with the bias reduced to ±1 m2/m2. Significantly improved regions are located in central Africa, Amazonia, southern Eurasia, northeastern China, and western Europe, where evergreen/deciduous forests and mixed forests are dominant. Except for the temperate zone in the Southern Hemisphere, the analyzed LAI can well represent seasonal variations. The most pronounced assimilation impact in low‐latitude regions is attributed to large initial forecast error covariance and sufficient background errors. The MOD 16 evapotranspiration estimates and upscaled gross primary production have been used to evaluate the assimilation impact, which highlight neutral to highly positive improvement.