Transport model error is an important source of uncertainty when estimating surface CO2 fluxes via an atmospheric model inversion. However, the relative importance of transport error to flux inversion estimates depends on the spatial scales of interest. At the large scale extreme, global flux estimates should be independent of transport error because constituent transport does not change the global mass of the constituent. However, we are entering a data-rich era of CO2 measurements so there is a desire to estimate CO2 fluxes at increasingly higher spatial resolutions. In this study, the transport error due to uncertainty of meteorological fields is investigated with a high resolution, limited area model. This source of transport error is often neglected in atmospheric inverse models. We characterize the extent to which errors in meteorological initial conditions (ICs) and lateral boundary conditions (LBCs) impact the quality of atmospheric CO2 transport across spatial scales. A series of experiments is conducted using different permutations of meteorological ICs and LBCs that possess varying levels of accuracy. We find that the predictability or transport error of CO2 is more sensitive to errors in meteorology at smaller scales than at larger scales, and that surface CO2 fluxes are important for the predictability of CO2 at the largest scales. We also determine the spatial scales resolvable in the context of uncertain meteorology. These findings have implications for the development of regional-scale inverse modelling systems. When assimilating CO2 observations near the surface, using accurate meteorological ICs is important for resolving fine-scale spatial variability of CO2 because CO2 transport at lower levels is more sensitive to meteorological ICs and surface CO2 fluxes than to meteorological LBCs. However, when assimilating aircraft CO2 measurements or XCO2 satellite retrievals which contain information at higher altitudes, using accurate meteorological LBCs is also important.