Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 9, issue 7
Biogeosciences, 9, 2537-2564, 2012
https://doi.org/10.5194/bg-9-2537-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 9, 2537-2564, 2012
https://doi.org/10.5194/bg-9-2537-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Jul 2012

Research article | 11 Jul 2012

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

Y. Zhao1, P. Ciais1, P. Peylin2, N. Viovy1, B. Longdoz3, J. M. Bonnefond4, S. Rambal5, K. Klumpp6, A. Olioso7,8, P. Cellier9, F. Maignan1, T. Eglin1, and J. C. Calvet10 Y. Zhao et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, UMR8212, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
  • 2Laboratoire de Biogéochimie Isotopique, LBI, Bâtiment EGER, 78026 Thiverval-Grignon, France
  • 3INRA, Centre INRA de Nancy, UMR1137, Ecologie et Ecophysiologie Forestières, 54280 Champenoux, France
  • 4INRA, UMR1263, EPHYSE, 33140, Villenave d'Ormon, France
  • 5Dream CEFE-CNRS, 1919 route de Mende, 34293 Montpellier, France
  • 6INRA, Grassland Ecosystem Research, 234 Avenue du Brézet, Clermont-Ferrand, 63039, France
  • 7INRA, UMR1114, EMMAH, Domaine Saint-Paul, 84914 Avignon Cedex 9, France
  • 8UAPV, UMR1114, EMMAH, 33, Rue Lois pasteur, 84000, Avignon, France
  • 9INRA, UMR1091, Environnement et Grandes Cultures, 78850 Thiverval-Grignon, France
  • 10CNRM/GAME, Météo-France, CNRS, 42 avenue Coriolis, 31057 Toulouse Cedex 1, France

Abstract. 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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