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| Titel |
A comprehensive benchmarking system for evaluating global vegetation models |
| VerfasserIn |
D. I. Kelley, I. C. Prentice, S. P. Harrison, H. Wang, M. Simard, J. B. Fisher, K. O. Willis |
| 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 ; 10, no. 5 ; Nr. 10, no. 5 (2013-05-17), S.3313-3340 |
| Datensatznummer |
250018253
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| Publikation (Nr.) |
 copernicus.org/bg-10-3313-2013.pdf |
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| Zusammenfassung |
| We present a benchmark system for global vegetation models. This system
provides a quantitative evaluation of multiple simulated vegetation
properties, including primary production; seasonal net ecosystem production;
vegetation cover; composition and height; fire regime; and runoff. The
benchmarks are derived from remotely sensed gridded datasets and site-based
observations. The datasets allow comparisons of annual average conditions and
seasonal and inter-annual variability, and they allow the impact of spatial
and temporal biases in means and variability to be assessed separately.
Specifically designed metrics quantify model performance for each process,
and are compared to scores based on the temporal or spatial mean value of the
observations and a "random" model produced by bootstrap resampling of the
observations. The benchmark system is applied to three models: a simple
light-use efficiency and water-balance model (the Simple Diagnostic Biosphere
Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges
(LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs
better than either of the DGVMs. It reproduces independent measurements of
net primary production (NPP) but underestimates the amplitude of the observed
CO2 seasonal cycle. The two DGVMs show little difference for most
benchmarks (including the inter-annual variability in the growth rate and
seasonal cycle of atmospheric CO2), but LPX represents burnt fraction
demonstrably more accurately. Benchmarking also identified several weaknesses
common to both DGVMs. The benchmarking system provides a quantitative
approach for evaluating how adequately processes are represented in a model,
identifying errors and biases, tracking improvements in performance through
model development, and discriminating among models. Adoption of such a system
would do much to improve confidence in terrestrial model predictions of
climate change impacts and feedbacks. |
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