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| Titel |
Ecosystem model optimization using in situ flux observations: benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances |
| VerfasserIn |
D. Santaren, P. Peylin, C. Bacour, P. Ciais, B. Longdoz |
| 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. 24 ; Nr. 11, no. 24 (2014-12-16), S.7137-7158 |
| Datensatznummer |
250117740
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| Publikation (Nr.) |
 copernicus.org/bg-11-7137-2014.pdf |
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| Zusammenfassung |
Terrestrial ecosystem models can provide major insights into the responses of
Earth's ecosystems to environmental changes and rising levels of atmospheric
CO2. To achieve this goal, biosphere models need mechanistic formulations
of the processes that drive the ecosystem functioning from diurnal to decadal
timescales. However, the subsequent complexity of model equations is
associated with unknown or poorly calibrated parameters that limit the
accuracy of long-term simulations of carbon or water fluxes and their
interannual variations. In this study, we develop a data assimilation
framework to constrain the parameters of a mechanistic land surface model
(ORCHIDEE) with eddy-covariance observations of CO2 and latent heat fluxes
made during the years 2001–2004 at the temperate beech forest site of Hesse,
in eastern France.
As a first technical issue, we show that for a complex process-based model
such as ORCHIDEE with many (28) parameters to be retrieved, a Monte Carlo
approach (genetic algorithm, GA) provides more reliable optimal parameter
values than a gradient-based minimization algorithm (variational scheme). The
GA allows the global minimum to be found more efficiently, whilst the
variational scheme often provides values relative to local minima.
The ORCHIDEE model is then optimized for each year, and for
the whole 2001–2004 period. We first find that a reduced (<10) set of
parameters can be tightly constrained by the eddy-covariance observations,
with a typical error reduction of 90%. We then show that including
contrasted weather regimes (dry in 2003 and wet in 2002) is necessary to
optimize a few specific parameters (like the temperature dependence of the
photosynthetic activity).
Furthermore, we find that parameters inverted from 4 years of flux
measurements are successful at enhancing the model fit to the data on several
timescales (from monthly to interannual), resulting in a typical modeling
efficiency of 92% over the 2001–2004 period (Nash–Sutcliffe
coefficient). This suggests that ORCHIDEE is able robustly to predict, after
optimization, the fluxes of CO2 and the latent heat of a specific
temperate beech forest (Hesse site). Finally, it is shown that using only
1 year of data does not produce robust enough optimized parameter sets in
order to simulate properly the year-to-year flux variability. This emphasizes
the need to assimilate data over several years, including contrasted weather
regimes, to improve the simulated flux interannual variability. |
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