To curb the impacts of global warming, a legally binding international treaty, the Paris Agreement, was adopted at the UN Climate Change Conference (COP21) in Paris on 13 December, 2015. The goal of the Paris Agreement is to limit the temperature rise to well less than 2°C, preferably 1.5°C, in comparison with pre-industrial levels. The implementation of the agreement requires a worldwide effort to immediately reduce emissions of anthropogenic greenhouse gases (GHGs) (United Nations Environment Programme, 2021). The steady implementation of the GHGs reductions pledged by individual countries requires the development of validation methods for regional/country-scale GHGs emissions. The national carbon dioxide (CO2) emissions from fossil fuel combustion and cement manufacture (FFCO2) are usually derived from inventories based on a variety of statistical data, including the production, consumption, and trade of fossil fuels (e.g., Gilfillan and Marland, 2021). However, it is also crucial to develop independent and observational methods for estimation of the emissions for tracking policy implementation.
Systematic observations of atmospheric GHGs, including CO2 and methane (CH4), have been conducted by many laboratories around the world. Even in the East Asian region, intensive GHGs observation networks have been developed using a variety of platforms, such as ground-based stations (e.g., Tsutsumi et al., 2006; Tohjima et al., 2014), ships (e.g., Terao et al., 2011; Tohjima et al., 2012), aircraft (e.g., Machida et al., 2008; Tsuboi et al., 2013; Umezawa et al., 2020), and satellites (e.g., Yokota et al., 2009; Yoshida et al., 2013). There are now denser networks of atmospheric observations around the globe than before. Meanwhile, the objective of the atmospheric observations has been extended from clarification of the global trends and temporal changes in the atmospheric burdens to quantitative evaluation of regional/country-scale fluxes with the help of atmospheric transport models.
In these circumstances, a global pandemic of the novel coronavirus disease, COVID-19, broke out in early 2020, and severe measures restricting socio-economic activity, including city lockdowns, were imposed by the concerned countries to prevent the spread of the COVID-19. These measures were also expected to decrease fossil fuel consumption, resulting in a reduction of the emissions of related species, including NOx, CO2, and so on. For example, ground-based and satellite-borne observations revealed that atmospheric NO2 concentrations decreased by 10 ~ 70% over the cities in East China during the lockdown period from late January to March 2020 compared with those in 2019 (e.g., Bauwens et al., 2020; Le et al., 2020). Using such satellite-based column-averaged NO2 distributions, models, and a variety of bottom-up information, Zheng et al. (2020) estimated an 11.5% decrease in China’s CO2 emissions during January-April 2020 compared to the same period in 2019. Meanwhile, estimation studies on changes in CO2 emissions during the COVID-19 period were conducted based on a variety of activity data: the change in the FFCO2 emissions from China estimated by Le Quéré et al. (2020) was − 242 (− 108 to − 394) MtCO2 during January-April 2020, which corresponds to a − 6.9% (− 3.1% to − 11.2%) decrease compared with the emissions during the same period in 2019. Such activity data-based estimates allow us to evaluate the detailed temporal change. For example, Liu et al. (2020) reported that the changes in the monthly FFCO2 emissions in 2020 from 2019 were − 18.4% in February, − 9.2% in March, and + 0.6% in April. These results raised the question of whether the direct observations of atmospheric CO2 were able to detect the signals related to the FFCO2 emission reduction caused by the COVID-19 outbreak.
Short-lived pollution constituents like NOx, whose estimated lifetime over China is less than a day even in winter when NOx has the longest lifetime (Shah et al., 2020), showed considerable decreases associated with the COVID-19 lockdown in China, as mentioned above. In contrast, the change in the atmospheric CO2 mole fraction caused by the COVID-19 pandemic is considered to be relatively small in comparison with the atmospheric CO2 level because of its huge atmospheric burden and a rather long lifetime. The estimated decrease in the annual global FFCO2 emissions in 2020 was 5 ~ 7% relative to that in 2019, which was about 10 PgC (Le Quéré et al., 2021; Friedlingstein, et al., 2022). Since the estimated change of 0.5 ~ 0.7 PgC corresponds to the globally averaged atmospheric CO2 mole fraction of 0.2 ~ 0.3 ppm, it’s quite difficult to detect such subtle signals in the atmospheric CO2 trends after the emitted CO2 is mixed globally (Lovenduski et al., 2021). Nevertheless, a variety of studies succeeded in detecting signals related to the FFCO2 reductions in China caused by the COVID-19 lockdown in both local-scale observations (Zeng et al., 2020; Liu et al., 2021; Wu et al., 2021) and regional-scale observations (Tojima et al., 2020; Buchwitz et al., 2021; Weir et al., 2021; Sim et al., 2022) of atmospheric CO2.
Tohjima et al. (2020) applied a unique method in their study, which was one of the first studies to observationally detect the regional-scale signals related to FFCO2 emission decreases caused by the COVID-19 lockdown in China from the synoptic scale variability ratio of the atmospheric CO2 and CH4 (ΔCO2/ΔCH4) observed on Hateruma Island (HAT, 24.06°N, 123.81°E). Hateruma island is located in the downwind area of continental East Asia from late autumn to early spring due to the influence of the East Asian monsoon. A previous study revealed that the ΔCO2/ΔCH4 ratio roughly reflected the emission ratio of CO2 to CH4 from continental East Asia, especially China (Tohjima et al., 2014). The monthly mean ΔCO2/ΔCH4 ratio showed a marked decrease in February 2020 when a severe lockdown was implemented almost across China. By using the observed changes in the ΔCO2/ΔCH4 ratios and the simulated relationship between the ΔCO2/ΔCH4 ratio and the FFCO2 emissions from China, whose temporal pattern of the reduction caused by the COVID-19 lockdown was based on the study of Le Quéré et al. (2020), we estimated the FFCO2 reductions to be 32 ± 12% and 19 ± 15% for February and March 2020, respectively. More recently, examining the ΔCO2/ΔCH4 ratio on Yonaguni Island (YON, 24.47°N, 123.01°E), located only about 90 km northwest of HAT, we found that the ΔCO2/ΔCH4 ratio also showed a marked decrease in February 2020 after eliminating the local influences (Tohjima et al., 2022). These results convinced us of the reliability of the ΔCO2/ΔCH4 ratio as an indicator of the relative emission strength in China.
In this study, we revisited the ΔCO2/ΔCH4 ratios observed at HAT and YON to develop a near-real-time estimation method for the temporal change in the FFCO2 emissions from China and updated the results for 2021 and 2022. In our previous study (Tohjima et al., 2020), we used prior information about the temporal variation of the FFCO2 emissions based on a bottom-up estimation by Le Quéré et al. (2020) to evaluate the FFCO2 emission change in China in 2020. Here we developed a method based on the ΔCO2/ΔCH4 ratio observed at HAT and YON without any prior information about the temporal emission changes. We examined the relationship between the ΔCO2/ΔCH4 ratio and the FFCO2/CH4 emission ratio in China by using an atmospheric transport model and including all components of the surface fluxes. Based on the simulated relationship and the ΔCO2/ΔCH4 ratios observed at HAT and YON, we estimated the FFCO2 emission changes in China during January-March (JFM) in 2020 under the assumption of invariable biospheric CO2 fluxes and all CH4 emissions and compared them with the previously reported estimations. Finally, we applied the above evaluation method to the ΔCO2/ΔCH4 ratios at HAT and YON and evaluated the FFCO2 emission changes in China for JFM in 2021 and 2022.