Show simple item record Dietze, Michael C. Vargas, Rodrigo Richardson, Andrew D. Stoy, Paul C. Barr, Alan G. Anderson, Ryan S. Arain, M. Altaf Baker, Ian T. Black, T. Andrew Chen, Jing M. Philippe, Ciais Flanagan, Larry B. Gough, Christopher M. Grant, Robert F. Hollinger, David Y. Izaurralde, R. Cesar Kucharik, Christopher J. Lafleur, Peter M. Liu, Shugang Lokupitiya, Erandathie Luo, Yiqi Munger, J. William Peng, Changhui Poulter, Benjamin Price, David T. Ricciuto, Daniel M. Riley, William J. Sahoo, Alok Kumar Schaefer, Kevin Suyker, Andrew E. Tian, Hanqin Tonitto, Christina Verbeeck, Hans Verma, Shashi B. Wang, Weifeng Weng, Ensheng 2019-08-27T21:53:25Z 2019-08-27T21:53:25Z 2011
dc.identifier.citation Dietze, M. C., Vargas, R., Richardson, A. D., Stoy, P. C., Barr, A. G., Anderson, R. S.,...Weng, E. (2011). Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis. Journal of Geophysical Research (Biogeosciences), 116, G04029. doi:10.1029/2011JG001661 en_US
dc.description Sherpa Romeo green journal. Permission to archive final published version. en_US
dc.description.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 non significant 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. en_US
dc.language.iso en_US en_US
dc.publisher American Geophysical Union en_US
dc.subject Carbon cycle en_US
dc.subject Ecosystem models en_US
dc.subject Eddy covariance en_US
dc.subject Model comparison en_US
dc.subject Uncertainty analysis en_US
dc.subject Wavelet decomposition en_US
dc.subject North American Carbon Program
dc.subject Time scales
dc.subject Wavelet analyses
dc.subject.lcsh Carbon cycle (Biogeochemistry)
dc.title Characterizing the performance of ecosystem models across time scales: a spectral analysis of the North American Carbon Program site-level synthesis en_US
dc.type Article en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Biological Sciences en_US
dc.description.peer-review Yes en_US
dc.publisher.institution University of Illinoise at Urbana-Champaign en_US
dc.publisher.institution Centro de Investigacion Cientifica y de Educacion Superior de Ensenada en_US
dc.publisher.institution Harvard University en_US
dc.publisher.institution Montana State University en_US
dc.publisher.institution Atmospheric Science and Technology Directorate en_US
dc.publisher.institution University of Montana en_US
dc.publisher.institution McMaster University en_US
dc.publisher.institution Colorado State University en_US
dc.publisher.institution University of British Columbia en_US
dc.publisher.institution University of Toronto en_US
dc.publisher.institution Centre d'Etudes Orme des Merisiers en_US
dc.publisher.institution University of Lethbridge en_US
dc.publisher.institution Virginia Commonwealth University en_US
dc.publisher.institution University of Alberta en_US
dc.publisher.institution U.S. Department of Agriculture Forest Service en_US
dc.publisher.institution Pacific Northwest National Laboratory en_US
dc.publisher.institution University of Maryland en_US
dc.publisher.institution University of Wisconsin-Madison en_US
dc.publisher.institution Trent University en_US
dc.publisher.institution Earth Resources Observation and Science Center en_US
dc.publisher.institution University of Oklahoma en_US
dc.publisher.institution University of Quebec at Montreal en_US
dc.publisher.institution Swiss Federal Research Institute en_US
dc.publisher.institution Laboratoire des Sciences du Climat et de l'Environment en_US
dc.publisher.institution Canadian Forest Service en_US
dc.publisher.institution Oak Ridge National Laboratory en_US
dc.publisher.institution Lawrence Berkeley National Laboratory en_US
dc.publisher.institution Princeton University en_US
dc.publisher.institution University of Colorado at Boulder en_US
dc.publisher.institution University of Nebraska-Lincoln en_US
dc.publisher.institution Auburn University en_US
dc.publisher.institution Cornell University en_US
dc.publisher.institution Ghent University en_US

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