The Resolvable Scales of Regional-Scale CO2 Transport in the Context of Imperfect Meteorology: The Predictability of CO2 in a Limited-Area Model

Transport model error is an important source of uncertainty when estimating surface CO2 fluxes via an atmospheric model inversion. In this study, the transport error due to uncertainty of meteorological fields is investigated with a high resolution, limited-area model. 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 meteorological ICs and LBCs that possess varying levels of accuracy. We find that the transport error of CO2 is more sensitive to errors in meteorology at smaller scales O(10 km) than at larger scales O(1,000 km), and that surface CO2 fluxes can explain 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 modeling 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. Improvement in meteorological inputs through a data assimilation system could be helpful in further resolving finer spatial scales of CO2 at regional scales.