Can a regional-scale reduction of atmospheric CO<sub>2</sub> during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO<sub>2</sub> retrievals

Abstract. The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We report results from a first attempt to determine whether a regional-scale reduction of anthropogenic CO2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO2. For this purpose, we have analysed a small ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2. We focus on East China because COVID-19 related CO2 emission reductions are expected to be largest there early in the pandemic. We analysed four XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach that does not rely on a priori information about CO2 sources and sinks and ignores atmospheric transport. Our approach utilises the computation of XCO2 anomalies, ΔXCO2, from the satellite Level 2 data products using a method called DAM (Daily Anomalies via (latitude band) Medians). DAM removes large-scale, daily XCO2 background variations, yielding XCO2 anomalies that correlate with the location of major CO2 source regions such as East China. We analysed satellite data between January 2015 and May 2020 and compared monthly XCO2 anomalies in 2020 with corresponding monthly XCO2 anomalies of previous years. In order to link the XCO2 anomalies to East China fossil fuel (FF) emissions, we used XCO2 and corresponding FF emissions from NOAA’s (National Oceanic and Atmospheric Administration) CarbonTracker version CT2019 from 2015 to 2018. Using this CT2019 data set, we found that the relationship between target region ΔXCO2 and the FF emissions of the target region is approximately linear and we quantified slope and offset via a linear fit. We use the empirically obtained linear equation to compute ΔXCO2FF, an estimate of the target region FF emissions, from the satellite-derived XCO2 anomalies, ΔXCO2. We focus on October to May periods to minimize contributions from biospheric carbon fluxes and quantified the error of our FF estimation method for this period by applying it to CT2019. We found that the difference of the retrieved FF emissions and the CT2019 FF emissions in terms of the root-mean-square-error (RMSE) is 0.39 GtCO2/year (4 %). We applied our method to NASA’s (National Aeronautics and Space Administration) OCO-2 XCO2 data product (version 10r) and to three GOSAT products. We focus on estimates of the relative change of East China monthly emissions in 2020 relative to previous months. Our results show considerable month-to-month variability (especially for the GOSAT products) and significant differences across the ensemble of satellite data products analysed. The ensemble mean indicates emission reductions by approximately 8 % ± 10 % in March 2020 and 10 % ± 10 % in April 2020 (uncertainties are 1-sigma) and somewhat lower reductions for the other months in 2020. Using only the OCO-2 data product, we obtain smaller reductions of 1–2 % (depending on month) with an uncertainty of ± 2 %. The large uncertainty and the differences of the results obtained for the individual ensemble members indicates that it is challenging to reliably detect and to accurately quantify the emission reduction. There are several reasons for this including the weak signal (the expected regional XCO2 reduction is only on the order of 0.1–0.2 ppm), the sparseness of the satellite data, remaining biases and limitations of our relatively simple data-driven analysis approach. Inferring COVID-19 related information on regional-scale CO2 emissions using current satellite XCO2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO2 surface fluxes.