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


Kevin Schaefer, Cooperative Institute for Research in Environmental Sciences
Christopher R. Schwalm, Northern Arizona University
Chris Williams, Clark University
M. Altaf Arain, McMaster Centre For Climate Change
Alan Barr, Environment and Climate Change Canada
Jing M. Chen, University of Toronto
Kenneth J. Davis, Pennsylvania State University
Dimitre Dimitrov, Northern Forestry Centre
Timothy W. Hilton, The University of New Mexico
David Y. Hollinger, United States Department of Agriculture
Elyn Humphreys, Carleton University
Benjamin Poulter, Laboratoire des Sciences du Climat et de l'Environnement
Brett M. Raczka, Pennsylvania State University
Andrew D. Richardson, Harvard University
Alok Sahoo, Princeton University
Peter Thornton, ORNL Environmental Sciences Division
Rodrigo Vargas, Centro de Investigacion Cientifica y de Educacion Superior de Ensenada
Hans Verbeeck, Universiteit Gent
Ryan Anderson, University of Montana
Ian Baker, University of Colombo
T. Andrew Black, The University of British Columbia
Paul Bolstad, University of Minnesota Twin Cities
Jiquan Chen, The University of Toledo
Peter S. Curtis, The Ohio State University
Ankur R. Desai, University of Minnesota Twin Cities
Michael Dietze, Boston University
Danilo Dragoni, Indiana University Bloomington
Christopher Gough, Virginia Commonwealth University
Robert F. Grant, University of Alberta
Lianhong Gu, ORNL Environmental Sciences Division
Atul Jain, University of Illinois Urbana-Champaign
Chris Kucharik, University of Wisconsin-Madison

Document Type



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). © 2012. American Geophysical Union.

Publication Title

Journal of Geophysical Research: Biogeosciences

Publication Date











biogeochemistry, biomass, drought, ecosystem response, eddy covariance, error analysis, flux measurement, humidity, light use efficiency, net primary production, numerical model, phenology, soil moisture, uncertainty analysis