We present an atmospheric inverse modeling framework to constrain terrestrial biosphere CO2 exchange processes at subregional scales. The model is operated at very high spatial and temporal resolution, using the state of Oregon in the northwestern United States as the model domain. The modeling framework includes mesoscale atmospheric simulations coupled to Lagrangian transport, a biosphere flux model that considers, e.g., the effects of drought stress and disturbance on photosynthesis and respiration CO2 fluxes, and a Bayesian optimization approach. This study focuses on the impact of uncertainties in advected background mixing ratios and fossil fuel emissions on simulated flux fields, both taken from external data sets. We found the simulations to be highly sensitive to systematic changes in advected background CO2, while shifts in fossil fuel emissions played a minor role. Correcting for offsets in the background mixing ratios shifted annual CO2 budgets by about 47% and improved the correspondence with the output produced by bottom-up modeling frameworks. Inversion results were robust against shifts in fossil fuel emissions, which is likely a consequence of relatively low emission rates in Oregon.