Remote Sens. 2013, 5(6), 2857-2882; doi:10.3390/rs5062857
Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets
1
Department of Geological Sciences, The University of Texas at Austin, 1 University Station C1100, Austin, TX 78712, USA
2
Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
3
Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA
*
Author to whom correspondence should be addressed.
Received: 14 March 2013 / Revised: 3 May 2013 / Accepted: 31 May 2013 / Published: 5 June 2013
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Abstract
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. Our 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. 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. View Full-Text
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Wang, K.; Mao, J.; Dickinson, R.E.; Shi, X.; Post, W.M.; Zhu, Z.; Myneni, R.B. Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets. Remote Sens. 2013, 5, 2857-2882.
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