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How Errors on Meteorological Variables Impact Simulated Ecosystem Fluxes: a Case Study for Six French Sites : Volume 8, Issue 2 (09/03/2011)

By Zhao, Y.

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Book Id: WPLBN0004005795
Format Type: PDF Article :
File Size: Pages 56
Reproduction Date: 2015

Title: How Errors on Meteorological Variables Impact Simulated Ecosystem Fluxes: a Case Study for Six French Sites : Volume 8, Issue 2 (09/03/2011)  
Author: Zhao, Y.
Volume: Vol. 8, Issue 2
Language: English
Subject: Science, Biogeosciences, Discussions
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Peylin, P., Viovy, N., Eglin, T., Maignan, F., Bonnefond, J. M., Cellier, P.,...Longdoz, B. (2011). How Errors on Meteorological Variables Impact Simulated Ecosystem Fluxes: a Case Study for Six French Sites : Volume 8, Issue 2 (09/03/2011). Retrieved from

Description: Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVS, UMR 1572, 91191 Gif-sur-Yvette, France. 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 ''true meteorology from local observation at eddy-covariance flux sites. The study is focused on six flux tower sites in France spanning across a 7–14 °C and 600–1040 mm yr−1 climate gradient, with forest, grassland and cropland ecosystems. We evaluate the results of the ORCHIDEE process-based model driven by four different meteorological models 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 between meteorological models and local tower meteorology. The seasonal cycle of air temperature, humidity and shortwave downward radiation is reproduced correctly by all meteorological models (average R2=0.90). At sites located near the coast and influenced by sea-breeze, or located in altitude, the misfit of meteorological drivers from gridded dataproducts 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 modeled grid point and measured local meteorology going from 0.35 (REMO model) to 0.70 (SAFRAN model). The bias of meteorological models 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 than observed local meteorology drivers to ORCHIDEE is quantified for the six studied sites and 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 the largest on a daily time scale, for which it is as large as the model error. The forcing error incurring from using gridded meteorological model 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.

How errors on meteorological variables impact simulated ecosystem fluxes: a case study for six French sites

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