Natural Resources, School of

 

Authors

Christopher R. Schwalm, Clark University
Christopher A. Williams, Clark UniversityFollow
Kevin Schaefer, University of Colorado Boulder
Ryan S. Anderson, University of MontanaFollow
M. Altaf Arain, McMaster University
Ian Baker, Colorado State University
Alan Barr, Atmospheric Science and Technology Directorate
T. Andrew Black, University of British Columbia
Guangsheng Chen, Auburn UniversityFollow
Jing Ming Chen, University of TorontoFollow
Philippe Ciais, Laboratoire des Sciences du Climat et de l’Environnement
Kenneth J. Davis, Pennsylvania State University
Ankur Desai, University of Wisconsin - Madison
Michael Dietze, University of Illinois at Urbana-Champaign
Danilo Dragoni, Indiana University
Marc L. Fischer, Lawrence Berkeley National LaboratoryFollow
Lawrence B. Flanagan, University of Lethbridge
Robert Grant, University of Alberta
Lianhong Gu, Oak Ridge National Laboratory
David Hollinger, USDA Forest Service
R. Cesar Izaurralde, University of MarylandFollow
Chris Kucharik, University of Wisconsin - Madison
Peter Lafleur, Trent University
Beverly E. Law, Oregon State UniversityFollow
Longhui Li, Laboratoire des Sciences du Climat et de l’Environnement
Zhengpeng Li, ASRC Research and Technology Solutions
Shuguang Liu, Earth Resources Observation and Science
Erandathie Lokupitiya, Colorado State University
Yiqi Luo, University of Oklahoma
Siyan Ma, University of California - Berkeley
Hank Margolis, Université Laval
Roser Matamala, Argonne National Laboratory
Harry McCaughey, Queen’s University
Russell K. Monson, University of Colorado, Boulder
Walter C. Oechel, San Diego State University
Changhui Peng, University of Quebec at Montreal
Benjamin Poulter, Swiss Federal Research Institute WSL
David T. Price, Canadian Forest Service
Dan M. Riciutto, Oak Ridge National Laboratory
William Riley, Lawrence Berkeley National Laboratory
Alok Kumar Sahoo, Princeton University
Michael Sprintsin, University of Toronto
Jianfeng Sun, University of Quebec at Montreal
Hanqin Tian, Auburn University
Christian Tonitto, Cornell University
Hans Verbeeck, Ghent University
Shashi B. Verma, University of Nebraska-LincolnFollow

ORCID IDs

Ryan S. Anderson

Date of this Version

2010

Citation

Schwalm, C. R., et al. (2010), A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis, J. Geophys. Res., 115, G00H05, doi:10.1029/2009JG001229.

Comments

Copyright 2010 by the American Geophysical Union. Used by permission.

Abstract

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ~220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models’ ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ~10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

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