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| Titel |
Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model |
| VerfasserIn |
C. Safta, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, P. E. Thornton |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 7 ; Nr. 8, no. 7 (2015-07-01), S.1899-1918 |
| Datensatznummer |
250116445
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| Publikation (Nr.) |
 copernicus.org/gmd-8-1899-2015.pdf |
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| Zusammenfassung |
| In this paper we propose a probabilistic framework for an uncertainty
quantification (UQ) study of a carbon cycle model and focus on the
comparison between steady-state and transient simulation setups.
A global sensitivity analysis (GSA) study indicates the parameters and
parameter couplings that are important at different times of the year for
quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and
a statistical model error term to calibrate the parameters of DALEC
using net ecosystem exchange (NEE) observations at the Harvard Forest
site. The calibration results are employed in the second part of the
paper to assess the predictive skill of the model via posterior predictive checks. |
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