NASA's Orbiting Carbon Observatory will monitor the atmospheric concentrations of carbon dioxide (CO2) along the satellite subtrack over the sunlit hemisphere of the Earth for more than 2 years, starting in late 2008. This paper demonstrates the application of a variational Bayesian formalism to retrieve fluxes at high spatial and temporal resolution from the satellite retrievals. We use a randomization approach to estimate the posterior error statistics of the calculated fluxes. Given our prior information about the fluxes (with error standard deviations about 0.4 g C m−2 d−1 over ocean and 4 g C m−2 d−1 over vegetated areas) and our observation characteristics (with error standard deviations about 2 ppm), we show error reductions of up to about 40% at weekly scale for a grid point of the transport model. We simulate the impact of undetected biases by perturbing the observations and show that regional biases of a few tenths of a part per million in column-averaged CO2 can bias the inverted yearly subcontinental fluxes by a few tenths of a gigaton of carbon, which is larger than the uncertainty on the anthropogenic carbon fluxes but smaller than that of natural fluxes over most vegetated areas.