Impact Assessment of Satellite-Derived Leaf Area Index Datasets Using a General Circulation Model

Abstract

Abstract This study assesses the impact of two different remote sensing–derived leaf area index (RSLAI) datasets retrieved from the same source (i.e., Advanced Very High Resolution Radiometer measurements) on a general circulation model’s (GCM) seasonal climate simulations as well as the mechanisms that lead to the improvement in simulations over several regions. Based on the analysis of these two RSLAI datasets for 17 yr from 1982 to 1998, their spatial distribution patterns and characteristics are discussed. Despite some disagreements in the RSLAI magnitudes and the temporal variability between these two datasets over some areas, their effects on the simulation of near-surface climate and the regions with significant impact are generally similar to each other. Major disagreements in the simulated climate appear in a few limited regions. The GCM experiment using the RSLAI and other satellite-derived land surface products showed substantial improvements in the near-surface climate in the East Asian and West African summer monsoon areas and boreal forests of North America compared to the control experiment that used LAI extrapolated from limited ground surveys. For the East Asia and northwest U.S. regions, the major role of RSLAI changes is in partitioning the net radiative energy into latent and sensible heat fluxes, which results in discernable warming and decrease of precipitation due to the smaller RSLAI values compared to the control. Meanwhile, for the West African semiarid regions, where the LAI difference between RSLAI and control experiments is negligible, the decrease in surface albedo caused by the high vegetation cover fraction in the satellite-derived dataset plays an important role in altering local circulation that produces a positive feedback in land/atmosphere interaction.