Natural Resources, School of

 

Authors

Michael C. Dietze, University of Illinois at Urbana-Champaign
Rodrigo Vargas, Centro de Investigación Científica y de Educación Superior de Ensenada
Andrew D. Richardson, Harvard University
Paul C. Stoy, Montana State University-Bozeman
Alan G. Barr, Atmospheric Science and Technology Directorate, Saskatoon
Ryan S. Anderson, University of MontanaFollow
M. Altaf Arain, McMaster University, Hamilton, Ontario
Ian T. Baker, Colorado State University
T. Andrew Black, University of British Columbia
Jing M. Chen, University of TorontoFollow
Philippe Ciais, Centre d’Etudes Orme des Merisiers, Gif-sur-Yvette, France
Lawrence B. Flanagan, University of Lethbridge, Alberta
Christopher M. Gough, Virginia Commonwealth University
Robert F. Grant, University of Alberta
David Hollinger, USDA Forest Service, Durham, NH
R. Cesar Izaurralde, Pacific Northwest National Laboratory
Christopher J. Kucharik, University of Wisconsin- Madison
Peter Lafleur, Trent University, Peterborough, ON
Shugang Liu, Earth Resources Observation and Science Center, Sioux Falls
Erandathie Lokupitiya, Colorado State University
Yiqi Luo, University of Oklahoma College of Law
J. William Munger, Harvard University
Changhui Peng, University of Quebec at Montreal
Benjamin Poulter, Swiss Federal Research Institute WSL, Birmensdorf
David T. Price, Canadian Forest Service
Daniel M. Ricciuto, Oak Ridge National Laboratory
William J. Riley, Lawrence Berkeley National Laboratory
Alok Kumar Sahoo, Princeton University
Kevin Schaefer, University of Colorado at Boulder
Andrew E. Suyker, University of Nebraska-LincolnFollow
Hanqin Tian, Auburn University
Christina Tonitto, Cornell University
Hans Verbeeck, Ghent University
Shashi B. Verma, University of Nebraska-LincolnFollow
Weifeng Wang, University of Quebec at Montreal
Ensheng Weng, University of Oklahoma

ORCID IDs

Ryan S. Anderson

Date of this Version

12-2011

Citation

Dietze, M. C., et al. (2011), Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis, J. Geophys. Res., 116, G04029, doi:10.1029/2011JG001661.

Comments

US government work.

Abstract

Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program’s site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.