Abstract
Abstract. The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. To investigate whether a regional-scale reduction of anthropogenic CO2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO2, we have analysed a small ensemble of OCO-2 and GOSAT satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2. We focus on East China and use a simple data-driven analysis method. We present estimates of the relative change of East China monthly emissions in 2020 relative to previous periods, limiting the analysis to October-to-May periods to minimize the impact of biogenic CO2 fluxes. The ensemble mean indicates an emission reduction by approximately 10 % ± 10 % in March and April 2020. However, our results show considerable month-to-month variability and significant differences across the ensemble of satellite data products analysed. For example, OCO-2 suggests a much smaller reduction (∼ 1 %–2 % ± 2 %). This indicates that it is challenging to reliably detect and to accurately quantify the emission reduction with current satellite data sets. There are several reasons for this, including the sparseness of the satellite data but also the weak signal; the expected regional XCO2 reduction is only on the order of 0.1–0.2 ppm. 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.
Highlights
Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas significantly contributing to global warming (IPCC, 2013)
Compared to fluctuations originating from the interaction of the terrestrial biosphere and the atmosphere, the spatiotemporal XCO2 variations due to anthropogenic fossil fuel (FF) CO2 emissions are typically much smaller (e.g. 1 ppm compared to 10 ppm; Schneising et al, 2008, 2013, 2014; Agustí-Panareda et al, 2019)
We focus on results based on XCO2 derived with the DAM method and refer to Appendix A for results based on the target minus surrounding” (TmS) method
Summary
Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas significantly contributing to global warming (IPCC, 2013). Retrievals of XCO2 from the satellite sensors SCIAMACHY/ENVISAT (Burrows et al, 1995; Bovensmann et al, 1999; Reuter et al, 2010, 2011) and TANSOFTS/GOSAT (Kuze et al, 2016) and from the Orbiting Carbon Observatory-2 (OCO-2) satellite (Crisp et al, 2004; Eldering et al, 2017; O’Dell et al, 2012, 2018) have been used in recent years to obtain information on natural CO2 sources and sinks Retrievals of XCO2 from the satellite sensors SCIAMACHY/ENVISAT (Burrows et al, 1995; Bovensmann et al, 1999; Reuter et al, 2010, 2011) and TANSOFTS/GOSAT (Kuze et al, 2016) and from the Orbiting Carbon Observatory-2 (OCO-2) satellite (Crisp et al, 2004; Eldering et al, 2017; O’Dell et al, 2012, 2018) have been used in recent years to obtain information on natural CO2 sources and sinks (e.g. Basu et al, 2013; Chevallier et al, 2014; Chevallier, 2015; Reuter et al, 2014a, 2017c; Schneising et al, 2014; Houweling et al, 2015; Kaminski et al, 2017; Liu et al, 2017; Eldering et al, 2017; Yin et al, 2018; Palmer et al, 2019; Miller and Michalak, 2020), on anthropogenic CO2 emissions (e.g. Schneising et al, 2008, 2013; Reuter et al, 2014b, 2019; Nassar et al, 2017; Schwandner et al, 2017; Matsunaga and Maksyutov, 2018; Miller et al, 2019; Labzovskii et al, 2019; Wu et al, 2020; Zheng et al, 2020a; Ye et al, 2020), and for other applications such as climate model assessments (e.g. Lauer et al, 2017; Gier et al, 2020) or data assimilation (e.g. Massart et al, 2016)
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