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Article: How errors on meteorological variables impact simulated ecosystem fluxes: a case study for six French sites
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ABSTRACT: We analyze how biases of meteorological drivers impact the calculation of ecosystem CO 2 , water and energy fluxes by models. To do so, we drive the same ecosystem model by meteorology from gridded products and by meteo-rology from local observation at eddy-covariance flux sites. The study is focused on six flux tower sites in France span-ning across a climate gradient of 7–14 • C annual mean sur-face 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 evalu-ation 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 meteoro-logical models (average R 2 = 0.90). At sites located in alti-tude, 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 repro-duced by meteorological models, with R 2 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 mod-eled instead of observed local meteorology drivers to OR-CHIDEE, 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 CO 2 , wa-ter and energy fluxes simulations, and should be taken into consideration in up-scaling studies.Biogeosciences 07/2012; 9:2537-2564. · 3.86 Impact Factor -
Article: Impact of tropospheric ozone on the Euro‐Mediterranean vegetation
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ABSTRACT: The impact of ozone (O3) on European vegetation is largely under-investigated, despite huge areas of Europe are exposed to high O3 levels and which are expected to increase in the next future. We studied the potential effects of O3 on photosynthesis and leaf area index (LAI) as well as the feedback between vegetation and atmospheric chemistry using a land surface model (ORCHIDEE) at high spatial resolution (30 km) coupled with a chemistry transport model (CHIMERE) for the whole year 2002. Our results show that the effect of tropospheric O3 on vegetation leads to a reduction in yearly gross primary production (GPP) of about 22% and a reduction in LAI of 15–20%. Larger impacts have been found during summer, when O3 reaches higher concentrations. During these months the maximum GPP decrease is up to 4 g C m−2 day−1, and the maximum LAI reduction is up to 0.7 m2 m−2. Since CHIMERE uses the LAI computed by ORCHIDEE to estimate the biogenic emissions, a LAI reduction may have severe implications on the simulated atmospheric chemistry. We found a large change in O3 precursors that however leads to small changes in tropospheric O3 concentration, while larger changes have been found for surface NO2 concentrations.Global Change Biology 06/2011; 17(7):2342 - 2359. · 6.86 Impact Factor -
Article: Spatial pattern of terrestrial carbon dioxide and water vapor coupling
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ABSTRACT: The eddy covariance system installed at flux towers is capable to measure simultaneously ecosystem-atmosphere exchanges of carbon dioxide (CO2) and water vapor (H2O). Therefore, such data has been utilized to study an important plant physiological characteristic, the amount of water being lost per unit carbon gain (water use efficiency, WUE) at ecosystem level. By using global fields of leaf area index and land cover based on data from the SPOT Vegetation and TERRA MODIS sensors in conjunction with soil maps we extrapolated flux tower information about inherent WUE from FLUXNET stations to the global land surface. The additional usage of climate fields led to global WUE maps then. Highest uncertainties occur in tropical forests with a coefficient of variability of 50 %. We compare these spatial details of median WUE and its uncertainty with estimates of four prominent land-surface schemes of climate models (CLM, JSBACH, ORCHIDEE, LPJ) which all model the combined flow of CO2 and H2O through the stomata of plants in different ways. Such model evaluation is important for a deeper understanding of the model’s validity w. r. t. the carbon-water coupling. The data-driven WUE map shows a strong latitudinal pattern with peaks in the tropics and the boreal forest. The models mostly agree with this pattern, although WUE values are twice as high in the temperate and semi-arid regions, 25 % higher in the boreal zone, and two times higher in the tundra. Two of the models underestimate WUE in the tropics by 50 % when compared to the other two models and to the data-driven estimate. Disagreement between models and data does not depend on model structure which suggests that their parameterization does not adequately capture mean growing season WUE patterns - a result with implications for future climate simulations.AGU Fall Meeting Abstracts. 11/2009; -1:05. -
Article: Challenges in quantifying biosphere-atmosphere exchange of nitrogen species.
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ABSTRACT: Recent research in nitrogen exchange with the atmosphere has separated research communities according to N form. The integrated perspective needed to quantify the net effect of N on greenhouse-gas balance is being addressed by the NitroEurope Integrated Project (NEU). Recent advances have depended on improved methodologies, while ongoing challenges include gas-aerosol interactions, organic nitrogen and N(2) fluxes. The NEU strategy applies a 3-tier Flux Network together with a Manipulation Network of global-change experiments, linked by common protocols to facilitate model application. Substantial progress has been made in modelling N fluxes, especially for N(2)O, NO and bi-directional NH(3) exchange. Landscape analysis represents an emerging challenge to address the spatial interactions between farms, fields, ecosystems, catchments and air dispersion/deposition. European up-scaling of N fluxes is highly uncertain and a key priority is for better data on agricultural practices. Finally, attention is needed to develop N flux verification procedures to assess compliance with international protocols.Environmental Pollution 12/2007; 150(1):125-39. · 3.75 Impact Factor -
Article: Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models
Global Biogeochemical Cycles 04/2007; · 4.78 Impact Factor
