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| Titel |
Evaluating terrestrial CO2 flux diagnoses and uncertainties from a simple land surface model and its residuals |
| VerfasserIn |
T. W. Hilton, K. J. Davis, K. Keller |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 2 ; Nr. 11, no. 2 (2014-01-21), S.217-235 |
| Datensatznummer |
250117137
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| Publikation (Nr.) |
 copernicus.org/bg-11-217-2014.pdf |
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| Zusammenfassung |
| 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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