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An inverse model using atmospheric CO<sub>2</sub> observations from a Europeannetwork of stations to reconstruct daily CO<sub>2</sub> fluxes and theiruncertainties over Europe at 50 km resolution has been developed within aBayesian framework. We use the pseudo-data approach in which we try torecover known fluxes using a range of perturbations to the input. In thisstudy, the focus is put on the sensitivity of flux accuracy to the inversesetup, varying the prior flux errors, the pseudo-data errors and the networkof stations. We show that, under a range of assumptions about prior errorand data error we can recover fluxes reliably at the scale of 1000 km and10 days. At smaller scales the performance is highly sensitive to details ofthe inverse set-up. The use of temporal correlations in the flux domainappears to be of the same importance as the spatial correlations. We alsonote that the use of simple, isotropic correlations on the prior flux errorsis more reliable than the use of apparently physically-based errors.Finally, increasing the European atmospheric network density improves thearea with significant error reduction in the flux retrieval

atmospheric chemistry and physics

What can we learn from European continuous atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; measurements to quantify regional fluxes – Part 2: Sensitivity of flux accuracy to inverse setup