Top–down constraint on regional fossil fuel CO2 emissions in China using GOSAT and OCO-2 satellite XCO2 retrievals: A case of the COVID-19 lockdown

The challenge of establishing top–down constraints for regional emissions of fossil fuel CO2 (FFCO2) arises from the difficulty in distinguishing between atmospheric CO2 concentrations released from fossil fuels and background variability, particularly owing to the influence of terrestrial biospheric fluxes. This necessitates the development of a regional inversion methodology based on atmospheric CO2 observations to verify bottom–up estimations independently. This study presents a promising approach for estimating China’s FFCO2 emissions by incorporating the model residual errors (MRE) of the column-averaged dry-air mole fractions of CO2 (XCO2) from FFCO2 emissions (MREff) retained in the analysis of natural flux optimization. China’s FFCO2 emissions during the COVID-19 lockdown in 2020 were estimated using the GEOS-Chem adjoint model. The relationship between the MREff and FFCO2 was determined using the model based on a regional FFCO2 anomaly suggested by posterior NOx emissions from air quality data assimilation. The MREff was typically one-tenth in magnitude, but some positively skewed outliers exceeded 1 ppm because the prior emissions lacked lockdown impacts, thereby exerting considerable observation forcing given the satellite retrieval uncertainties. We initialized the FFCO2 with posterior NOx emissions and optimized the colinear emission ratio. Synthetic data experiments demonstrated that this approach reduced the FFCO2 bias by <10%. The real-data experiments estimated 19% lower FFCO2 with GOSAT XCO2 and 26% lower with OCO-2 XCO2 than the bottom–up estimations. This study proved the feasibility of our regional FFCO2 inversion, highlighting the importance of addressing the outlier behaviors observed in satellite XCO2 retrievals.