A model-data comparison of gross primary productivity: Results from the north American carbon program site synthesis

Kevin Schaefer, Christopher R. Schwalm, Chris Williams, M. Altaf Arain, Alan Barr, Jing M. Chen, Kenneth J. Davis, Dimitre Dimitrov, Timothy W. Hilton, David Y. Hollinger, Elyn Humphreys, Benjamin Poulter, Brett M. Raczka, Andrew D. Richardson, Alok Sahoo, Peter Thornton, Rodrigo Vargas, Hans Verbeeck, Ryan Anderson, Ian BakerT. Andrew Black, Paul Bolstad, Jiquan Chen, Peter S. Curtis, Ankur R. Desai, Michael Dietze, Danilo Dragoni, Christopher Gough, Robert F. Grant, Lianhong Gu, Atul Jain, Chris Kucharik, Beverly Law, Shuguang Liu, Erandathie Lokipitiya, Hank A. Margolis, Roser Matamala, J. Harry McCaughey, Russ Monson, J. William Munger, Walter Oechel, Changhui Peng, David T. Price, Dan Ricciuto, William J. Riley, Nigel Roulet, Hanqin Tian, Christina Tonitto, Margaret Torn, Ensheng Weng, Xiaolu Zhou

Research output: Contribution to journalArticle

214 Scopus citations

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°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°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 εmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).

Original languageEnglish (US)
Article numberG03010
JournalJournal of Geophysical Research: Biogeosciences
Volume117
Issue number3
DOIs
StatePublished - Sep 1 2012

ASJC Scopus subject areas

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

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    Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, J. M., Davis, K. J., Dimitrov, D., Hilton, T. W., Hollinger, D. Y., Humphreys, E., Poulter, B., Raczka, B. M., Richardson, A. D., Sahoo, A., Thornton, P., Vargas, R., Verbeeck, H., Anderson, R., ... Zhou, X. (2012). A model-data comparison of gross primary productivity: Results from the north American carbon program site synthesis. Journal of Geophysical Research: Biogeosciences, 117(3), [G03010]. https://doi.org/10.1029/2012JG001960