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| Titel |
A systematic approach for comparing modeled biospheric carbon fluxes across regional scales |
| VerfasserIn |
D. N. Huntzinger, S. M. Gourdji, K. L. Mueller, A. M. Michalak |
| 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 ; 8, no. 6 ; Nr. 8, no. 6 (2011-06-21), S.1579-1593 |
| Datensatznummer |
250005958
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| Publikation (Nr.) |
 copernicus.org/bg-8-1579-2011.pdf |
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| Zusammenfassung |
| Given the large differences between biospheric model estimates of regional
carbon exchange, there is a need to understand and reconcile the predicted
spatial variability of fluxes across models. This paper presents a set of
quantitative tools that can be applied to systematically compare flux
estimates despite the inherent differences in model formulation. The
presented methods include variogram analysis, variable selection, and
geostatistical regression. These methods are evaluated in terms of their
ability to assess and identify differences in spatial variability in flux
estimates across North America among a small subset of models, as well as
differences in the environmental drivers that best explain the spatial
variability of predicted fluxes. The examined models are the Simple Biosphere
(SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the
Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed
on model-predicted net ecosystem exchange, gross primary production, and
ecosystem respiration. Variogram analysis reveals consistent seasonal
differences in spatial variability among modeled fluxes at a
1° × 1° spatial resolution. However, significant
differences are observed in the overall magnitude of the carbon flux spatial
variability across models, in both net ecosystem exchange and component
fluxes. Results of the variable selection and geostatistical regression
analyses suggest fundamental differences between the models in terms of the
factors that explain the spatial variability of predicted flux. For example,
carbon flux is more strongly correlated with percent land cover in CASA
GFEDv2 than in SiB or CASA. Some of the differences in spatial patterns of
estimated flux can be linked back to differences in model formulation, and
would have been difficult to identify simply by comparing net fluxes between
models. Overall, the systematic approach presented here provides a set of
tools for comparing predicted grid-scale fluxes across models, a task that
has historically been difficult unless standardized forcing data were
prescribed, or a detailed sensitivity analysis performed. |
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