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| Titel |
How errors on meteorological variables impact simulated ecosystem fluxes: a case study for six French sites |
| VerfasserIn |
Y. Zhao, P. Ciais, P. Peylin, N. Viovy, B. Longdoz, J. M. Bonnefond, S. Rambal, K. Klumpp, A. Olioso, P. Cellier, F. Maignan, T. Eglin, J. C. Calvet |
| 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 ; 9, no. 7 ; Nr. 9, no. 7 (2012-07-11), S.2537-2564 |
| Datensatznummer |
250007185
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| Publikation (Nr.) |
 copernicus.org/bg-9-2537-2012.pdf |
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| Zusammenfassung |
| We analyze how biases of meteorological drivers impact the calculation of
ecosystem CO2, water and energy fluxes by models. To do so, we drive
the same ecosystem model by meteorology from gridded products and by
meteorology from local observation at eddy-covariance flux sites. The study
is focused on six flux tower sites in France spanning across a climate
gradient of 7–14 °C annual mean surface air temperature and
600–1040 mm mean annual rainfall, with forest, grassland and cropland
ecosystems. We evaluate the results of the ORCHIDEE process-based model
driven by meteorology from four different analysis data sets against the
same model driven by site-observed meteorology. The evaluation is decomposed
into characteristic time scales. The main result is that there are
significant differences in meteorology between analysis data sets and local
observation. The phase of seasonal cycle of air temperature, humidity and
shortwave downward radiation is reproduced correctly by all meteorological
models (average R2 = 0.90). At sites located in altitude, the misfit of
meteorological drivers from analysis data sets and tower meteorology is the
largest. We show that day-to-day variations in weather are not completely
well reproduced by meteorological models, with R2 between analysis data
sets and measured local meteorology going from 0.35 to 0.70. The bias of
meteorological driver impacts the flux simulation by ORCHIDEE, and thus
would have an effect on regional and global budgets. The forcing error,
defined by the simulated flux difference resulting from prescribing modeled
instead of observed local meteorology drivers to ORCHIDEE, is quantified
for the six studied sites at different time scales. The magnitude of this
forcing error is compared to that of the model error defined as the
modeled-minus-observed flux, thus containing uncertain parameterizations,
parameter values, and initialization. The forcing error is on average
smaller than but still comparable to model error, with the ratio of forcing
error to model error being the largest on daily time scale (86%) and
annual time scales (80%). The forcing error incurred from using a gridded
meteorological data set to drive vegetation models is therefore an important
component of the uncertainty budget of regional CO2, water and energy
fluxes simulations, and should be taken into consideration in up-scaling
studies. |
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