Proper assignment of error statistics is essential in the field of Bayesian inference. This paper studies the impact of correlated observation errors in the case of the estimation of CO2 surface fluxes from NASA's forthcoming Orbiting Carbon Observatory (OCO). Using a series of observation simulation system experiments, it is shown that hypothetical observation error correlations of 0.5 in neighbouring observations have a rather limited impact on the accuracy of the inverted fluxes when they are correctly taken into account. The information loss induced by commonly-used approximate treatments of the observation error correlations (neglecting, observation thinning and error inflating), that are computationally more efficient, is quantified. Error inflation has the least detrimental impact among the suboptimal set-ups and limits the loss in uncertainty reduction to a few per cent, in spite of its very low reduced chi-squared.