Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets

Wang, Kai; Mao, Jiafu; Dickinson, Robert; Shi, Xiaoying; Post, Wilfred; Zhu, Zaichun; Myneni, Ranga

doi:10.3390/rs5062857

Title: Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets

Journal Article · Wed Jun 05 00:00:00 EDT 2013 · Remote Sensing

DOI:https://doi.org/10.3390/rs5062857· OSTI ID:1376647

Wang, Kai ^[1]; Mao, Jiafu ^[2]; Dickinson, Robert ^[1]; Shi, Xiaoying ^[2]; Post, Wilfred ^[2]; Zhu, Zaichun ^[3]; Myneni, Ranga ^[3]

Univ. of Texas, Austin, TX (United States). Dept. of Geological Sciences
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Climate Change Science Inst.
Boston Univ., MA (United States). Dept. of Earth and Environment

This paper 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 a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR ’s seasonal cycle, diurnal cycle, long-term trends and spatial patterns. These findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns, but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. Here, we identify 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 of 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725; FG02-01ER63198

OSTI ID:: 1376647

Journal Information:: Remote Sensing, Vol. 5, Issue 6; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 11 works

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