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Biogeosciences An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 2
Biogeosciences, 11, 217–235, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 11, 217–235, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Jan 2014

Research article | 21 Jan 2014

Evaluating terrestrial CO2 flux diagnoses and uncertainties from a simple land surface model and its residuals

T. W. Hilton*,1, K. J. Davis1, and K. Keller2 T. W. Hilton et al.
  • 1Department of Meteorology, Pennsylvania State University, University Park, PA, USA
  • 2Department of Geosciences, Pennsylvania State University, University Park, PA,, USA
  • *now at: School of Engineering, University of California, Merced, Merced, CA, USA

Abstract. Global terrestrial atmosphere–ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

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