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A model-data comparison of gross primary productivity : results from the North American Carbon Program site synthesis

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Abstract
Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0 degrees C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0 degrees C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as epsilon(max) (LUE), V-cmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).
Keywords
GLOBAL VEGETATION MODEL, NET ECOSYSTEM PRODUCTIVITY, SUB-ALPINE FOREST, DIOXIDE FLUXES, INTERANNUAL VARIABILITY, CO2 EXCHANGE, STOMATAL CONDUCTANCE, BOREAL FORESTS, HIGH-ELEVATION, SOIL-MOISTURE

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Chicago
Schaefer, Kevin, Christopher R Schwalm, Chris Williams, M Altaf Arain, Alan Barr, Jing M Chen, Kenneth J Davis, et al. 2012. “A Model-data Comparison of Gross Primary Productivity : Results from the North American Carbon Program Site Synthesis.” Journal of Geophysical Research-biogeosciences 117.
APA
Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, J. M., Davis, K. J., et al. (2012). A model-data comparison of gross primary productivity : results from the North American Carbon Program site synthesis. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 117.
Vancouver
1.
Schaefer K, Schwalm CR, Williams C, Arain MA, Barr A, Chen JM, et al. A model-data comparison of gross primary productivity : results from the North American Carbon Program site synthesis. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES. 2012;117.
MLA
Schaefer, Kevin, Christopher R Schwalm, Chris Williams, et al. “A Model-data Comparison of Gross Primary Productivity : Results from the North American Carbon Program Site Synthesis.” JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES 117 (2012): n. pag. Print.
@article{2978401,
  abstract     = {Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0 degrees C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0 degrees C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as epsilon(max) (LUE), V-cmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).},
  articleno    = {G03010},
  author       = {Schaefer, Kevin and Schwalm, Christopher R and Williams, Chris and Arain, M Altaf and Barr, Alan and Chen, Jing M and Davis, Kenneth J and Dimitrov, Dimitre and Hilton, Timothy W and Hollinger, David Y and Humphreys, Elyn and Poulter, Benjamin and Raczka, Brett M and Richardson, Andrew D and Sahoo, Alok and Thornton, Peter and Vargas, Rodrigo and Verbeeck, Hans and Anderson, Ryan and Baker, Ian and Black, T Andrew and Bolstad, Paul and Chen, Jiquan and Curtis, Peter S and Desai, Ankur R and Dietze, Michael and Dragoni, Danilo and Gough, Christopher and Grant, Robert F and Gu, Lianhong and Jain, Atul and Kucharik, Chris and Law, Beverly and Liu, Shuguang and Lokipitiya, Erandathie and Margolis, Hank A and Matamala, Roser and McCaughey, J Harry and Monson, Russ and Munger, J William and Oechel, Walter and Peng, Changhui and Price, David T and Ricciuto, Dan and Riley, William J and Roulet, Nigel and Tian, Hanqin and Tonitto, Christina and Torn, Margaret and Weng, Ensheng and Zhou, Xiaolu},
  issn         = {0148-0227},
  journal      = {JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES},
  language     = {eng},
  pages        = {15},
  title        = {A model-data comparison of gross primary productivity : results from the North American Carbon Program site synthesis},
  url          = {http://dx.doi.org/10.1029/2012JG001960},
  volume       = {117},
  year         = {2012},
}

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