Spatiotemporal analysis of global atmospheric XCO2 concentrations before and after COVID-19 using HASM data fusion method - Liu, Yu; Wu, Binwei; Yue, Tianxiang (Frontiers in Environmental Science, 2023)
Abstract: The COVID-19 outbreak that began in 2020 has changed human activities and thus reduced anthropogenic carbon emissions in most parts of the world. To accurately study the impact of the COVID-19 pandemic on changes in atmospheric XCO2 concentrations, a data fusion method called High Accuracy Surface Modeling (HASM) is applied using the CO2 simulation from GEOS-Chem as the driving field and GOSAT XCO2 observations as the accuracy control conditions to obtain continuous spatiotemporal global XCO2 concentrations. Cross-validation shows that using HASM greatly improves the mean absolute error and root mean square error of the XCO2 data compared with those for GEOS-Chem simulation data before fusion, and the R2 is also increased from 0.54 to 0.79 after fusion. Moreover, OCO-2/OCO-3 XCO2 observational data verify that the fused XCO2 data achieve a lower MAE and RMSE. Spatiotemporal analysis shows that the global XCO2 concentration exhibited no obvious trend before or after the COVID-19 outbreak, but the growth of global and terrestrial atmospheric XCO2 in 2020 can reflect the impact of the COVID-19 pandemic; that is, the rapid growth in terrestrial atmospheric XCO2 observed before 2019 slowed, and high-speed growth resumed in 2021. Finally, obvious differences in the pattern of XCO2 growth are found on different continents.