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What eddy-covariance measurements tell us about prior land flux errors in CO₂-flux inversion schemes

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  • To guide the future development of CO₂-atmospheric inversion modeling systems, we analyzed the errors arising from prior information about terrestrial ecosystem fluxes. We compared the surface fluxes calculated by a process-based terrestrial ecosystem model with daily averages of CO₂ flux measurements at 156 sites across the world in the FLUXNET network. At the daily scale, the standard deviation of the model-data fit was 2.5 gC·m⁻²·d⁻¹; temporal autocorrelations were significant at the weekly scale (>0.3 for lags less than four weeks), while spatial correlations were confined to within the first few hundred kilometers (<0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6.
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  • Chevallier, F., et al. (2012), What eddy-covariance measurements tell us about prior land flux errors in CO₂-flux inversion schemes, Global Biogeochem. Cycles, 26, GB1021, doi:10.1029/2010GB003974.
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  • 26
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  • GB1021
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  • The study was co-funded by the European Commission under the EU Seventh Research Framework Programme (grant agreement 212196, COCOS) and by the French Agence Nationale pour la Recherche (grant agreement ANR-08-SYSC-014, MSDAG). The authors would like to thank N. Viovy (LSCE) who provided the gap-filling tools for the local atmospheric variables, F. Marabelle and his team at LSCE for computational support, Gil Bohrer (Ohio State University) and two anonymous reviewers for their stimulating comments on the text. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FLUXNET–Canada Research Network/Canadian Carbon Program (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS–Siberia, and the USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO–GTOS–TCO, iLEAPS, Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia, Université Laval, Environment Canada, and the U.S. Department of Energy, and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California, Berkeley, and the University of Virginia.
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