Assimilation of Remotely Sensed LAI Into CLM4CN Using DART. (22nd August 2019)

• Record Type: Journal Article
• Authors: Ling, X. L.; Fu, C. B.; Guo, W. D.; Yang, Z.‐L.
• Is Part Of: Journal of advances in modeling earth systems. Volume 11:Number 8(2019)
• Page Start: 2768
• Page End: 2786
• Publication Date: 2019-08-22
• Subjects: Community Land Model -- leaf area index -- Data Assimilation Research Testbed -- leaf carbon and leaf nitrogen; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001634
• Languages: English
• ISSNs: 1942-2466

Abstract

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 m 2 /m 2, which are effectively corrected in the analyzed LAI, with the bias reduced to ±1 m 2 /m 2 . 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. Key Points: Assimilating GLASS LAI and updating LAI and leaf C/N is an effective method to provide a high‐accuracy estimate of LAI using DART/CLM4CN Assimilation is more effective in growing season due to large initial forecast error covariances and sufficient background errors A clear added value of the assimilation has been highlighted based on GPP and evapotranspiration observation‐based estimates