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An inverse model using atmospheric CO<sub>2</sub> observations from a European network of stations to reconstruct daily CO<sub>2</sub> fluxes and their uncertainties over Europe at 50 km resolution has been developed within a Bayesian framework. We use the pseudo-data approach in which we try to recover known fluxes using a range of perturbations to the input. In this study, the focus is put on the sensitivity of flux accuracy to the inverse setup, varying the prior flux errors, the pseudo-data errors and the network of stations. We show that, under a range of assumptions about prior error and data error we can recover fluxes reliably at the scale of 1000 km and 10 days. At smaller scales the performance is highly sensitive to details of the inverse set-up. The use of temporal correlations in the flux domain appears to be of the same importance as the spatial correlations. We also note that the use of simple, isotropic correlations on the prior flux errors is more reliable than the use of apparently physically-based errors. Finally, increasing the European atmospheric network density improves the area with significant error reduction in the flux retrieval.