Proceedings of the 11th International Carbon Dioxide Conference

Constraining the signals of anthropogenic CO₂ emissions in high-resolution inverse models is significant for the efficient monitoring and climate mitigation actions. Such estimates are often limited to smaller domains and regional models with very high spatial resolution. In this study, a global high-resolution inverse model is employed to estimate the regional scale CO₂ emissions, implementing the FLEXPART tracer transport model at two different spatial resolutions – (i) 0.025°×0.025° and (ii) 0.05°×0.05° for the period 2015-2019 and 2009-2015 respectively. The model optimizes terrestrial biosphere, ocean-atmosphere exchanges and fossil fuel fluxes separately and can reproduce finer details at regions with denser observations. Depending on the availability of the observations, the fluxes from 2015-2019 are used to estimate the emissions from the states/provinces of the U.S and the E.U whereas the fluxes for 2009-2015 are analyzed to comprehend the emissions from Chinese provinces. For case (i), the model utilizes continuous atmospheric measurements from regional surface networks and measurements from background sites worldwide. The estimates of fossil fuel emissions at the subnational level are further evaluated with the regional inventories such as EPA over the U.S. and TNO-CAMS over the countries of Europe. The results show that the estimates from the model are in the right direction and obtain substantial correction over the urban regions of the U.S. In case (ii), the model utilizes GOSAT X_CO2data (NIES L2 V03.05) in addition to the observations from surface networks. The higher fossil flux corrections over eastern China indicate that the regional fluxes benefit from the fossil signals in the urban observations. Moreover, the large-scale terrestrial biosphere and ocean-atmosphere exchanges are well represented in the model and are comparable with the NOAA’s Carbon Tracker fluxes. Hence, this high-resolution model has the potential to estimate regional fossil fuel emissions, provided dense observations at finer scales are available.