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 (MREs) 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 are estimated using the GEOS-Chem adjoint model. The relationship between the MREff and FFCO₂ is determined using the model based on a regional FFCO₂ anomaly suggested by posterior NO_x emissions from air-quality data assimilation. The MREff is typically one-tenth in magnitude, but some positively skewed outliers exceed 1 ppm because the prior emissions lack lockdown impacts, thereby exerting considerable observation forcing given the satellite retrieval uncertainties. We initialize the FFCO₂ with posterior NO_x emissions and optimize the colinear emission ratio. Synthetic data experiments demonstrate that this approach reduces the FFCO₂ bias to less than 10%. The real-data experiments estimate 19% lower FFCO₂ with GOSAT XCO₂ and 26% lower with OCO-2 XCO₂ than the bottom-up estimations. This study proves the feasibility of our regional FFCO₂ inversion, highlighting the importance of addressing the outlier behaviors observed in satellite XCO₂ retrievals.