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

Task 5, principal investigator: LSCE.

We have analyzed the resolution-dependency of the prior probability density of terrestrial ecosystem fluxes. We have compared the fluxes calculated by a process-based terrestrial ecosystem model and daily averages of CO2 flux measurements at 156 sites across the world from the Fluxnet network ( All data have been obtained through an accepted la Thuile proposal. 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 autocorrelations were confined within the first few hundred kilometers (< 0.2 after 200 km). Separating the plant functional types did not increase the spatial correlations, except for deciduous broad-leaved forests. Using the statistics of the flux measurements to represent 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 coarser spatial and temporal resolutions used in regional or global models. This quantifies how aggregation reduces error variances, while raising correlations.

Effect of temporal (a) and spatial (b) aggregation of the fluxes on error correlation in the same dimension. The aggregation distance is defined as the length of the side of a square on which the aggregation is performed.