Evaluation of a land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets
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Land surface covers only 30% of the global surface, but contributes largely to the intricacy of the climate system by exchanging water and energy with the overlying atmosphere. The partitioning of incident solar radiation among various components at the land surface, especially vegetation and underlying soil, determines the energy absorbed by vegetation, evapotranspiration, partitioning between surface sensible and latent heat fluxes, and the energy and water exchange between the land surface and the atmosphere. Because of its significance in climate model, land surface model solar radiation partitioning scheme should be evaluated in order to ensure its accuracy in reproducing these naturally complicated processes. However, few studies evaluated this part of climate model. This study examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of multiple remote sensing fraction of absorbed photosynthetically active radiation (FPAR) datasets, ground observations and a unique 28-year FPAR dataset derived from the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset, we evaluated the CLM4 FPAR’s seasonal cycle, diurnal cycle, long-term trends and spatial patterns. Our findings show the model roughly agrees with observations in the seasonal cycle , long-tern trend and spatial patterns but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months and spatial heterogeneity. We identified the discrepancy in the diurnal cycle as due to the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating vegetation to climate in the model indicated by long-term trends is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process.