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

  1. Kevin Schaefer1,*,
  2. Christopher R. Schwalm2,
  3. Chris Williams3,
  4. M. Altaf Arain4,
  5. Alan Barr5,
  6. Jing M. Chen6,
  7. Kenneth J. Davis7,
  8. Dimitre Dimitrov8,
  9. Timothy W. Hilton9,
  10. David Y. Hollinger10,
  11. Elyn Humphreys11,
  12. Benjamin Poulter12,
  13. Brett M. Raczka7,
  14. Andrew D. Richardson13,
  15. Alok Sahoo14,
  16. Peter Thornton15,
  17. Rodrigo Vargas16,17,
  18. Hans Verbeeck18,
  19. Ryan Anderson19,
  20. Ian Baker20,
  21. T. Andrew Black21,
  22. Paul Bolstad22,
  23. Jiquan Chen23,
  24. Peter S. Curtis24,
  25. Ankur R. Desai22,
  26. Michael Dietze25,
  27. Danilo Dragoni26,
  28. Christopher Gough27,
  29. Robert F. Grant28,
  30. Lianhong Gu15,
  31. Atul Jain29,
  32. Chris Kucharik30,
  33. Beverly Law31,
  34. Shuguang Liu32,
  35. Erandathie Lokipitiya20,
  36. Hank A. Margolis33,
  37. Roser Matamala34,
  38. J. Harry McCaughey35,
  39. Russ Monson36,
  40. J. William Munger37,
  41. Walter Oechel38,39,
  42. Changhui Peng40,
  43. David T. Price8,
  44. Dan Ricciuto15,
  45. William J. Riley41,
  46. Nigel Roulet42,
  47. Hanqin Tian43,
  48. Christina Tonitto44,
  49. Margaret Torn41,
  50. Ensheng Weng45,
  51. Xiaolu Zhou40

Article first published online: 19 JUL 2012

DOI: 10.1029/2012JG001960

Issue

Journal of Geophysical Research: Biogeosciences (2005–2012)

Journal of Geophysical Research: Biogeosciences (2005–2012)

Additional Information

How to Cite

Schaefer, K., et al. (2012), A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis, J. Geophys. Res., 117, G03010, doi:10.1029/2012JG001960.

Author Information

  1. 1

    National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA

  2. 2

    School of Earth Science and Environmental Sustainability, Northern Arizona University, Flagstaff, Arizona, USA

  3. 3

    Graduate School of Geography, Clark University, Worcester, Massachusetts, USA

  4. 4

    School of Geography and Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, Ontario, Canada

  5. 5

    Climate Research Division, Atmospheric Sciences and Technology Directorate, Environment Canada, Saskatoon, Saskatchewan, Canada

  6. 6

    Department of Geography, University of Toronto, Toronto, Ontario, Canada

  7. 7

    Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA

  8. 8

    Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, Edmonton, Alberta, Canada

  9. 9

    Department of Biology, University of New Mexico, Albuquerque, New Mexico, USA

  10. 10

    Forest Service, United States Department of Agriculture, Durham, New Hampshire, USA

  11. 11

    Department of Geography and Environmental Studies, Carleton University, Ottawa, Ontario, Canada

  12. 12

    Laboratoire des Sciences du Climat et l'Environnement, Gif-sur-Yvette, France

  13. 13

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA

  14. 14

    Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA

  15. 15

    Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

  16. 16

    Departamento de Biología de la Conservación, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico

  17. 17

    Delaware Environmental Institute, University of Delaware, Newark, Delaware, USA

  18. 18

    Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Ghent University, Ghent, Belgium

  19. 19

    Numerical Terradynamic Simulation Group, University of Montana, Missoula, Montana, USA

  20. 20

    Department of Zoology, University of Colombo, Colombo, Sri Lanka

  21. 21

    Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada

  22. 22

    Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, USA

  23. 23

    Department of Environmental Sciences (DES), University of Toledo, Toledo, Ohio, USA

  24. 24

    Department Evolution, Ecology, and Organismal Biology, Ohio State University, Columbus, Ohio, USA

  25. 25

    Department of Earth and Environment, Boston University, Boston, Massachusetts, USA

  26. 26

    Department of Geography, Indiana University, Bloomington, Indiana, USA

  27. 27

    Department of Biology, Virginia Commonwealth University, Richmond, Virginia, USA

  28. 28

    Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada

  29. 29

    Department of Atmospheric Science, University of Illinois, Urbana, Illinois, USA

  30. 30

    Department of Agronomy and Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin–Madison, Madison, Wisconsin, USA

  31. 31

    Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, USA

  32. 32

    Earth Resources Observation and Science, United States Geological Survey, Sioux Falls, South Dakota, USA

  33. 33

    Centre d'Étude de la Forêt, Laval University, Quebec City, Quebec, Canada

  34. 34

    Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA

  35. 35

    Department of Geography, Queen's University, Kingston, Ontario, Canada

  36. 36

    Laboratory for Tree Ring Research, University of Arizona, Tucson, Arizona, USA

  37. 37

    School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, USA

  38. 38

    Department of Biology and the Global Change Research Group, San Diego State University, San Diego, California, USA

  39. 39

    CRI Fondazione Edmund Mach, San Michele all'Adige, Italy

  40. 40

    Instititut of Environment Sciences, Biology Science Department, University of Quebec at Montreal, Montreal, Quebec, Canada

  41. 41

    Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

  42. 42

    Department of Geography, McGill University, Montreal, Quebec, Canada

  43. 43

    International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, USA

  44. 44

    Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA

  45. 45

    Department of Botany and Microbiology, University of Oklahoma, Norman, Oklahoma, USA

*Corresponding author: K. Schaefer, National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA. (kevin.schaefer@nsidc.org)

Publication History

  1. Issue published online: 19 JUL 2012
  2. Article first published online: 19 JUL 2012
  3. Manuscript Accepted: 26 MAY 2012
  4. Manuscript Revised: 16 MAY 2012
  5. Manuscript Received: 20 JAN 2012

Funded by

  • NOAA. Grant Numbers: NA07OAR4310115, N9-TE09-26

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Keywords:

  • gross primary productivity;
  • model performance;
  • modeling

[1] 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 asεmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).

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