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

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 thismore » naturally complicated process.« less
Authors:
 [1] ;  [2] ;  [1] ;  [2] ;  [2] ;  [3] ;  [3]
  1. Univ. of Texas, Austin, TX (United States). Dept. of Geological Sciences
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Climate Change Science Inst.
  3. Boston Univ., MA (United States). Dept. of Earth and Environment
Publication Date:
Grant/Contract Number:
AC05-00OR22725; FG02-01ER63198
Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 5; Journal Issue: 6; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; land surface; solar radiation partitioning; climate modeling; evaluation
OSTI Identifier:
1376647

Wang, Kai, Mao, Jiafu, Dickinson, Robert, Shi, Xiaoying, Post, Wilfred, Zhu, Zaichun, and Myneni, Ranga. Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets. United States: N. p., Web. doi:10.3390/rs5062857.
Wang, Kai, Mao, Jiafu, Dickinson, Robert, Shi, Xiaoying, Post, Wilfred, Zhu, Zaichun, & Myneni, Ranga. Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets. United States. doi:10.3390/rs5062857.
Wang, Kai, Mao, Jiafu, Dickinson, Robert, Shi, Xiaoying, Post, Wilfred, Zhu, Zaichun, and Myneni, Ranga. 2013. "Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets". United States. doi:10.3390/rs5062857. https://www.osti.gov/servlets/purl/1376647.
@article{osti_1376647,
title = {Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets},
author = {Wang, Kai and Mao, Jiafu and Dickinson, Robert and Shi, Xiaoying and Post, Wilfred and Zhu, Zaichun and Myneni, Ranga},
abstractNote = {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.},
doi = {10.3390/rs5062857},
journal = {Remote Sensing},
number = 6,
volume = 5,
place = {United States},
year = {2013},
month = {6}
}