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Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis


Michael C. Dietze
Rodrigo Vargas
Andrew D. Richardson
Paul C. Stoy
Alan G. Barr
Ryan S. Anderson
M. Altaf Arain
Ian T. Baker
T. Andrew Black
Jing M. Chen
Philippe Ciais
Lawrence B. Flanagan
Christopher M. Gough
Robert F. Grant
R. Cesar Izaurralde
Christopher J. Kucharik
Peter Lafleur
Shugang Liu
Erandathie Lokupitiya
Yiqi Luo
J. William Munger
Changhui Peng
Benjamin Poulter
David T. Price
Daniel M. Ricciuto
William J. Riley
Alok Kumar Sahoo
Kevin Schaefer
Andrew E. Suyker
Hanqin Tian
Christina Tonitto
Hans Verbeeck
Shashi B. Verma
Weifeng Wang
Ensheng Weng



Publication type:

Scientific Journal (JRNL)

Primary Station(s):

Northern Research Station


Journal of Geophysical Research


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.


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.; Ciais, Philippe; Flanagan, Lawrence B.; Gough, Christopher M.; Grant, Robert F.; Hollinger, David; Izaurralde, R. Cesar; Kucharik, Christopher J.; Lafleur, Peter; 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. 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. 116(G4): 383-.


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