Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations at Hateruma and Yonaguni Islands, Japan

Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations at Hateruma and Yonaguni Islands, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Methodology Near-real-time estimation of fossil fuel CO 2 emissions from China based on atmospheric observations at Hateruma and Yonaguni Islands, Japan Yasunori Tohjima, Yosuke Niwa, Prabir K. Patra, Hitoshi Mukai, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2009154/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2023 Read the published version in Progress in Earth and Planetary Science → Version 1 posted 8 You are reading this latest preprint version Abstract We developed a near-real-time estimation method for temporal changes in fossil fuel CO 2 (FFCO 2 ) emissions from China for three months (January, February, March, (JFM)) based on atmospheric CO 2 and CH 4 observations on Hateruma Island (HAT, 24.06°N, 123.81°E) and Yonaguni Island (YON, 24.47°N, 123.01°E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO 2 and CH 4 (ΔCO 2 /ΔCH 4 ) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO 2 and CH 4 fluxes, we found that the ΔCO 2 /ΔCH 4 ratio was linearly related to the FFCO 2 /CH 4 emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO 2 /ΔCH 4 ratios into FFCO 2 /CH 4 emission ratios in China. The change rates of the emission ratios were calculated relative to those for the preceding 9-year period (2011–2019), during which relatively stable ΔCO 2 /ΔCH 4 ratios were observed. These changes in the emission ratios can be read as FFCO 2 emission changes under the assumption of no interannual variations in CH 4 emissions and biospheric CO 2 fluxes for JFM. The resulting average changes in the FFCO 2 emissions in January, February, and March 2020 were 17 ± 8%, − 36 ± 7%, and − 12 ± 8%, respectively, (− 10 ± 9% for JFM overall) relative to 2011–2019. These results were generally consistent with previous estimates. The emission changes for the two most recent JFM were 18 ± 8%, − 2 ± 10%, 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, − 3 ± 10%, − 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO 2 emissions from China rebounded to the normal level or set a new high in early 2021 after the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai. Fossil fuel CO2 emissions Synoptic-scale variations Atmospheric CO2 Atmospheric CH4 COVID-19 lockdown East Asian Monsoon Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction 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 (CO 2 ) emissions from fossil fuel combustion and cement manufacture (FFCO 2 ) 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 CO 2 and methane (CH 4 ), 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 NO x , CO 2 , and so on. For example, ground-based and satellite-borne observations revealed that atmospheric NO 2 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 NO 2 distributions, models, and a variety of bottom-up information, Zheng et al. ( 2020 ) estimated an 11.5% decrease in China’s CO 2 emissions during January-April 2020 compared to the same period in 2019. Meanwhile, estimation studies on changes in CO 2 emissions during the COVID-19 period were conducted based on a variety of activity data: the change in the FFCO 2 emissions from China estimated by Le Quéré et al. ( 2020 ) was − 242 (− 108 to − 394) MtCO 2 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 FFCO 2 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 CO 2 were able to detect the signals related to the FFCO 2 emission reduction caused by the COVID-19 outbreak. Short-lived pollution constituents like NO x , whose estimated lifetime over China is less than a day even in winter when NO x 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 CO 2 mole fraction caused by the COVID-19 pandemic is considered to be relatively small in comparison with the atmospheric CO 2 level because of its huge atmospheric burden and a rather long lifetime. The estimated decrease in the annual global FFCO 2 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 CO 2 mole fraction of 0.2 ~ 0.3 ppm, it’s quite difficult to detect such subtle signals in the atmospheric CO 2 trends after the emitted CO 2 is mixed globally (Lovenduski et al., 2021). Nevertheless, a variety of studies succeeded in detecting signals related to the FFCO 2 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 CO 2 . 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 FFCO 2 emission decreases caused by the COVID-19 lockdown in China from the synoptic scale variability ratio of the atmospheric CO 2 and CH 4 (ΔCO 2 /ΔCH 4 ) 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 ΔCO 2 /ΔCH 4 ratio roughly reflected the emission ratio of CO 2 to CH 4 from continental East Asia, especially China (Tohjima et al., 2014 ). The monthly mean ΔCO 2 /ΔCH 4 ratio showed a marked decrease in February 2020 when a severe lockdown was implemented almost across China. By using the observed changes in the ΔCO 2 /ΔCH 4 ratios and the simulated relationship between the ΔCO 2 /ΔCH 4 ratio and the FFCO 2 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 FFCO 2 reductions to be 32 ± 12% and 19 ± 15% for February and March 2020, respectively. More recently, examining the ΔCO 2 /ΔCH 4 ratio on Yonaguni Island (YON, 24.47°N, 123.01°E), located only about 90 km northwest of HAT, we found that the ΔCO 2 /ΔCH 4 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 ΔCO 2 /ΔCH 4 ratio as an indicator of the relative emission strength in China. In this study, we revisited the ΔCO 2 /ΔCH 4 ratios observed at HAT and YON to develop a near-real-time estimation method for the temporal change in the FFCO 2 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 FFCO 2 emissions based on a bottom-up estimation by Le Quéré et al. ( 2020 ) to evaluate the FFCO 2 emission change in China in 2020. Here we developed a method based on the ΔCO 2 /ΔCH 4 ratio observed at HAT and YON without any prior information about the temporal emission changes. We examined the relationship between the ΔCO 2 /ΔCH 4 ratio and the FFCO 2 /CH 4 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 ΔCO 2 /ΔCH 4 ratios observed at HAT and YON, we estimated the FFCO 2 emission changes in China during January-March (JFM) in 2020 under the assumption of invariable biospheric CO 2 fluxes and all CH 4 emissions and compared them with the previously reported estimations. Finally, we applied the above evaluation method to the ΔCO 2 /ΔCH 4 ratios at HAT and YON and evaluated the FFCO 2 emission changes in China for JFM in 2021 and 2022. 2 Methods/experimental 2.1 Atmospheric observations at HAT and YON The National Institute for Environmental Studies (NIES) and the Japan Meteorological Agency (JMA) began monitoring the atmospheric GHGs, including CO 2 and CH 4 at HAT and YON, respectively, in the 1990s (Fig. 1 ). The technical details for the measurements of atmospheric CO 2 and CH 4 were given elsewhere (e.g., Tohjima et al., 2002 and Tohjima et al., 2010 for HAT, and Watanabe et al., 2000 and Tsutsumi et al., 2006 for YON). Both islands belong to the western part of the Ryukyu Islands, located between the East China Sea and the western Pacific. Air masses are predominantly transported from the continental region of East Asia during winter and from the Pacific region during summer due to the East Asian monsoon (Wada et al., 2013 ; Tohjima et al., 2014 ). Since HAT and YON are relatively closely located within a distance of about 90 km, almost identical seasonal cycles and trends of the atmospheric CO 2 and CH 4 were observed on both islands (Zhang et al., 2007 ). Additionally, similar synoptic-scale variations with periods of several hours to several days were also observed at both sites, especially during winter, as shown in Fig. 2 . Enhanced mole fractions of GHGs and related species are often observed when the continental air masses are transported to the islands. However, a previous study revealed that a substantial diurnal variation was superimposed on the synoptic-scale variation of CO 2 at YON, preventing us from extracting the continental emission signals from the variability ratio (Tohjima et al., 2022 ). Such a diurnal cycle with a deep trough in the daytime was attributed to the local biospheric CO 2 exchange on the island. The rather large local influences at YON were attributed to the differences in the site conditions: the monitoring station with a sampling tower at HAT was built at the eastern tip of the island, whereas that at YON is located inland. We needed a different treatment for the data at YON to suppress the local influences (Tohjima et al., 2022 ). 2.1 Data processing procedure The ratio of the synoptic-scale variations of the atmospheric CO 2 and CH 4 was calculated using the same methods as those adopted by previous studies (Tohjima, et al., 2014 ; 2020 ). Here, we provide a brief explanation of the calculation procedure. The variability ratio, ΔCO 2 /ΔCH 4 , was computed as a correlation slope of a scatter plot of the consecutive time series of the two species within a certain time window. The slope was computed by a reduced major axis regression (RMA) (Hirsch and Gilroy, 1984 ). The standard deviations and the correlation coefficient were also computed at the same time. These calculations were repeated for the whole data set by shifting the time window by one hour. Then, if the standard deviation and the correlation coefficient were lower than certain criteria, those correlation slopes were discarded. Finally, the selected correlation slopes were used to compute the monthly average or the moving averages of ΔCO 2 /ΔCH 4 . We set 0.1 ppm and 0.7 as the criteria for the standard deviation of CO 2 and the correlation coefficient, respectively, as was done in our previous study. As for the time window for the correlation analysis, a duration of 24 hours was used for HAT, while a much longer duration was used for YON. As mentioned in the previous section, the CO 2 diurnal cycle at YON showed a larger decrease in the daytime than that at HAT, which can be attributed to a larger local CO 2 uptake at YON. The larger diurnal cycle, enlarging the absolute value of the ΔCO 2 /ΔCH 4 ratio at YON, made it difficult to extract the signals related to the relative emission strengths in the upwind region. However, since such a local influence was effectively eliminated by using a longer time window (84 hours) and only nighttime data in a previous study (20 − 06 LST), we adopted the same approach as Tohjima et al. ( 2022 ) to calculate the ΔCO 2 /ΔCH 4 ratio at YON in this study. 2.1 Model simulation To quantitatively evaluate the relationship between the continental CO 2 and CH 4 emissions and the ΔCO 2 /ΔCH 4 ratio at HAT and YON, we used an atmospheric transport model of a Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-based transport model (NICAM-TM: Niwa et al., 2011 ). The NICAM dynamical framework inherently guarantees the conservation of tracer mass in the atmospheric transport process without any numerical mass fixer (Satoh, 2002 ), which makes NICAM-TM suitable for studying long-lived species like greenhouse gases (e.g., Niwa et al., 2012 ). The Japanese 55-year Reanalysis data (JRA-55: Kobayashi et al., 2015 ) for the period between 2000 and 2021 were used to nudge horizontal winds in the NICAM-TM simulation, and the horizontal resolution of NICAM-TM used was approximately 112 km. For the simulation of the atmospheric CO 2 , we used all components of the global surface CO 2 fluxes, which consist of fluxes of FFCO 2 , ocean CO 2 , and land biosphere CO 2 (BioCO 2 ). For the FFCO 2 , we used global high-resolution flux maps from the Open-source Data Inventory for Anthropogenic CO 2 of version 2019 (ODIAC2019), which were available for the period from 2000 to 2018 (Oda and Maksyutov, 2011 ; Oda et al., 2018 ). For the ocean CO 2 , we used monthly air-sea flux maps developed by the Japan Meteorological Agency for the period from 2000 to 2018 (Takatani et al., 2014 ; Iida et al., 2015 , 2021 ). For the BioCO 2 , we used averaged monthly flux maps based on the inversion for the period of 2006–2008, conducted with NICAM-TM (Niwa et al. 2012 ). As for the global surface CH 4 fluxes, we also used monthly inversion flux maps computed by the NICAM-TM 4D-Var system for the period from 2000 to 2017 (Niwa et al., 2017a , 2017b ; Saunois et al., 2020 ). In this study, the atmospheric CO 2 and CH 4 mole fractions at HAT were simulated during 2000–2021 by using the corresponding climate dataset from the JRA-55 reanalysis and the above CO 2 and CH 4 flux data. When there were no flux data for the corresponding years, we repeatedly used the latest flux maps instead: FFCO 2 flux maps for 2018, ocean CO 2 flux maps for 2018, and CH 4 flux maps for 2017. The temporal changes in the monthly emissions of the FFCO 2 , land BioCO 2 , and CH 4 from China used in the simulation are plotted in supplementary Fig. S1. It should be noted that we do not necessarily need prior information on emissions for the target years 2020, 2021, and 2022 in this study, because only relative emission changes are estimated based on the simulated relationship between the emission ratios and the variability ratios, as described in the following section. The time series of the simulated atmospheric CO 2 and CH 4 mole fractions at HAT are plotted in Fig. 2 . The simulations generally well reproduced the observed synoptic-scale variations of both CO 2 and CH 4 . Using these simulated time series based on the time-dependent FFCO 2 fluxes, we examined the relationship between the ΔCO 2 /ΔCH 4 ratio and the CO 2 /CH 4 emission ratio in China. Additionally, we simulated the atmospheric CO 2 and CH 4 at HAT in January, February, and March 2020 and 2021 by changing the FFCO 2 emissions from China in 2018 to 55, 70, 85, 115, and 130% emissions and examined the relationship between the simulated variability ratio and emission ratio using these modified FFCO 2 emissions. The simulated relationship between the variability ratio and the emission ratio can also be applied to the observations at YON because the distance between HAT and YON (about 90km) is comparable to the horizontal resolution of the atmospheric transport model (112 km) used in this study and the observed data for both sites show almost identical synoptic-scale variations except for diurnal cycles. 3 Results And Discussion 3.1 Temporal change in the ΔCO 2 /ΔCH 4 ratios at HAT and YON The monthly mean values of the ΔCO 2 /ΔCH 4 ratio observed at HAT and YON between 1998 and 2022 are plotted in Fig. 3 . As our previous study (Tohjima et al., 2022 ) suggested, there are considerable similarities in the temporal change between HAT and YON; the ΔCO 2 /ΔCH 4 ratios at both sites show a gradual increase in 2000s and rather stable values after 2011. These trends in the variability ratio are mostly attributed to the changes in the FFCO 2 emissions from China (Tohjima et al., 2014 ; 2020 ). In fact, the pattern of the temporal changes in the annual FFCO 2 emissions from China taken from estimations of the Global Carbon Project (GCP) (Friedlingstein, et al., 2021) and ODIAC (Oda et al., 2018 ) generally agree with that of the ΔCO 2 /ΔCH 4 ratio (Fig. 3 ). In Fig. 3 , we also plotted the average ratios of the estimated FFCO 2 and CH 4 emissions from China for JFM between 2000 and 2018. Note that these emission ratios were based on the emission estimates used in the model simulation of this study (Fig. S1). Since the interannual variability in the estimated CH 4 emissions is rather suppressed during JFM except for a gradually increasing trend, the FFCO 2 /CH 4 emission ratio for China shows a similar temporal pattern to the FFCO 2 emissions from China. Previous studies also showed marked decreases in the ΔCO 2 /ΔCH 4 ratios at HAT and YON in February 2020, when the COVID-19-related nationwide lockdown in China considerably reduced the FFCO 2 emissions (Tohjima et al., 2020 ; 2022 ). In Fig. 3 , the averages of the ΔCO 2 /ΔCH 4 ratios for HAT and YON during the preceding 9-year (2011–2019) period are drawn as red and black broken lines, respectively, with a 95% confidence interval for YON depicted as gray shaded area. The ΔCO 2 /ΔCH 4 ratios at both sites fall below the 95% confidence limit in February 2020. In contrast, the monthly mean ΔCO 2 /ΔCH 4 ratios at HAT and YON during JFM in 2021 and 2022 returned to the previous 9-year (2011–2019) level or higher. This suggests that the FFCO 2 emissions from China in early 2021 returned to the same level as or higher than before the COVID-19 lockdown. To take a closer look at the temporal changes in the ΔCO 2 /ΔCH 4 ratio during the JFM in 2020, 2021, and 2022, the 30-day moving averages of the ΔCO 2 /ΔCH 4 ratios for HAT and YON are depicted in Fig. 4 . For comparison, averages of the ΔCO 2 /ΔCH 4 ratios for the preceding 9 years (2011–2019) were also drawn together with the standard deviations in the figure. Although the temporal resolution of the plots is low (± 15 days), the ΔCO 2 /ΔCH 4 ratios show decreases between January and February in 2020, minima in the middle of February, and gradual increases toward the preceding 9-year averages in March 2020. Previous studies suggested that the above temporal patterns of the consecutive ΔCO 2 /ΔCH 4 ratios were consistent with the estimated change in the FFCO 2 emissions from China based on a study by Le Quéré et al. ( 2020 ). On the other hand, the consecutive ΔCO 2 /ΔCH 4 ratios during the JFM in 2021 are larger than the preceding 9-year average and spread. The local minima in the middle of February 2021 might be related to the reduction in economic activity during the Chinese New Year holidays. Note that the period of the Chinese New Year holidays was from January 24 to February 2 in 2020, of which the last three days were extended holidays to fight the spread of COVID-19. The New Year holidays in 2021 were from February 11 to 17, 2021 (see Fig. 4 ). These results seem to be consistent with the recovery of the economic activity in China from the influence of the COVID-19 pandemic in early 2021. It is noteworthy that the consecutive ΔCO 2 /ΔCH 4 ratios in 2022, which were higher than the preceding 9-year average in January and decreased in February, reached the preceding 9-year level or lower in March. Since the COVID-19 infection spread again mostly in Shanghai after March, the reduced ΔCO 2 /ΔCH 4 ratios might reflect the decrease in the FFCO 2 emissions associated with the confinements of the socioeconomic activities in China. 3.2 Simulated ΔCO 2 /ΔCH 4 ratio Using the simulated time series of the atmospheric CO 2 and CH 4 at HAT for the period from 2000 to 2021 based on the time-dependent FFCO 2 emissions from 2000 to 2018, we calculated the monthly averages of the ΔCO 2 /ΔCH 4 ratio from January to March in the same way as the observed data at HAT were computed. The simulated monthly averaged ΔCO 2 /ΔCH 4 ratios during 2000–2021 are plotted as purple circles in Fig. 5 for the control emission cases of CO 2 and CH 4 , and the observed ratios are also plotted as gray circles for comparison. The simulated ΔCO 2 /ΔCH 4 ratios roughly trace the observed increasing trend in 2000s and plateau after 2011 except for five points enclosed by dotted lines, which are more than 20 mol mol − 1 larger than the corresponding observed ratios. These discrepancies were attributed to the fact that the monthly ΔCO 2 /ΔCH 4 ratios included some extraordinarily large values with very small CH 4 variability. These erroneous ΔCO 2 /ΔCH 4 ratios might be attributed to uncertainties in model transport or flux distributions used in the simulation or both. Thus, we rejected these five data as outliers in the subsequent analysis. The scatter plot of the ΔCO 2 /ΔCH 4 ratios between the observation and simulation without the above-mentioned five outliers shows a clear positive correlation with a correlation coefficient of 0.74 and a linear regression line slope of 1.2 ± 0.1 (simulation/observation) (supplementary Fig. S2). The regression line was determined by the RMA method and the uncertainties (1σ) of the parameters were evaluated by a bootstrap method, in which the regression calculations were repeatedly applied to the datasets prepared by iterative resampling with replacement (n = 10,000). Note that we also used the above approach for the regression analyses of the scatter plots in the following section. Additional sensitivity simulations are also shown for the ΔCO 2 /ΔCH 4 ratio based on the modified FFCO 2 fluxes and the meteorological reanalysis data during 2020–2021 in Fig. 5 , where the data are plotted as color-coded triangles. The relationship between the simulated ΔCO 2 /ΔCH 4 ratios and the FFCO 2 /CH 4 emission ratios in China are shown as scatter plots in Fig. 6 . The results based on the time-dependent FFCO 2 emissions (1997–2021) and the modified FFCO 2 emissions (2020–2021) from China are plotted as red and blue circles, respectively. As is expected, both datasets show positive and consistent correlations and slopes. From the linear regression analysis, we obtained slopes (variability ratio/emission ratio) and y-intercepts of 1.08 ± 0.08 and 45 ± 5 (mol mol − 1 ), respectively, for the time-dependent FFCO 2 and those of 1.07 ± 0.10 and 45 ± 7 (mol mol − 1 ), respectively, for the modified FFCO 2 . There is no significant difference between the two regression lines (p = 0.63), suggesting that the contribution of the year-to-year differences in atmospheric transport does not strongly influence the ΔCO 2 /ΔCH 4 ratios. Such characteristics are brought about by the very fact that calculating the variability ratio cancels out the transport influences. Therefore, combining these two datasets, we obtained a single regression line, depicted in Fig. 6 as a black line, with a slope of 1.08 ± 0.07 and a y-intercept of 46 ± 4 (mol mol − 1 ). The results of the regression analysis are summarized in Table 1 . Table 1 Summary of the regression analysis of the scatter plots of the simulated ΔCO 2 /ΔCH 4 ratio against FFCO 2 emissions Emission ratio or emission Time-dependent fluxes (A) (2000–2021) Modified fluxes (B) (2020–2021) All fluxes (A + B) p-value c Slope Intercept a Slope Intercept a Slope Intercept a FFCO 2 /CH 4 1.10 ± 0.08 45 ± 5 1.07 ± 0.10 45 ± 7 1.08 ± 0.07 47 ± 4 0.63 (FFCO 2 + BioCO 2 )/CH 4 1.19 ± 0.08 16 ± 7 1.07 ± 0.10 26 ± 9 1.12 ± 0.07 22 ± 6 0.51 FFCO 2 0.36 ± 0.02 b 58 ± 4 0.40 ± 0.04 b 46 ± 7 0.37 ± 0.02 b 56 ± 04 0.04 a Units of intercepts are given in mol mol − 1 . b Units of slopes for FFCO 2 emission are given in (mol mol − 1 )/TgC. c p-values are for the hypothesis that there is no significant difference between the slopes for the time-dependent fluxes and modified fluxes. The simulated ΔCO 2 /ΔCH 4 ratios also bore linear relationships to the (FFCO 2 + BioCO 2 )/CH 4 emission ratios and FFCO 2 emissions in China, as shown in supplementary Fig. S3. The results based on the above analyses are summarized in Table 1 . The difference of the y-intercepts for the FFCO 2 /CH 4 from those for the (FFCO 2 + BioCO 2 )/CH 4 corresponds to the influence of the BioCO 2 emissions on the ΔCO 2 /ΔCH 4 ratios. The regression slopes for the emission ratios are close to unity and the y-intercepts for the (FFCO 2 + BioCO 2 )/CH 4 are roughly close to the origin, indicating that the ΔCO 2 /ΔCH 4 ratios at HAT straightforwardly reflect the CO 2 /CH 4 emission ratios in China, as was indicated in a previous study (Tohjima et al., 2014 ). As for the relationship to the FFCO 2 emissions, there is a significant difference (p < 0.05) between the regression slopes for the time-dependent and the modified emissions, indicating that the different rate of increase in the CH 4 emissions and potentially the spatial heterogeneity in the change rates of the time-dependent FFCO 2 emissions contribute to the temporal change in the ΔCO 2 /ΔCH 4 ratios. In the following sections, assuming that the land biospheric CO 2 emissions from China have no interannual variations, we used the linear relationship between the ΔCO 2 /ΔCH 4 ratios and the FFCO 2 /CH 4 emission ratio to evaluate the change in the FFCO 2 /CH 4 emission ratio in China. Although the JFM chosen for the analysis corresponds to the period when the biotic activities are relatively dormant, there can be a measurable interannual variability in the BioCO 2 and CH 4 emissions (Fig. S1). From the inversely estimated BioCO 2 and CH 4 emissions from China after 2011 based on NICAM-TM (see Fig. S1), we obtained averages and standard deviations (1σ) of 2.0 ± 1.3 TgC day − 1 and 0.118 ± 0.008 TgCH 4 day − 1 for the BioCO 2 and CH 4 emissions, respectively. These standard deviations for the BioCO 2 and CH 4 emissions correspond to the uncertainties of about ± 14% and ± 7% for the (FFCO 2 + BioCO 2 )/CH 4 emission ratio in China for the recent decadal period, respectively. Meanwhile, the CH 4 emissions from China for the three months are mostly derived from anthropogenic sources, including coal mining, landfills, enteric fermentation, and other anthropogenic sources, except for paddy fields (e.g., Ito et al., 2019 ). Among these anthropogenic sources, coal mining is the largest source in China, contributing about 40% of the total emission during JFM. Since the increase in coal consumption historically enhanced the FFCO 2 and CH 4 emissions in China, it was inferred that these emissions positively correlated, as was pointed out by Saeki and Patra ( 2017 ). Therefore, it should be noted that such a positive correlation of the emissions might, to some extent, attenuate the temporal change in the observed ΔCO 2 /ΔCH 4 ratios at HAT and YON. The influence of the correlative change in the CH 4 emissions on the ΔCO 2 /ΔCH 4 ratios in this model simulation can be evaluated from the linear relationships of the ΔCO 2 /ΔCH 4 ratios against the FFCO 2 emissions for the two cases listed in Table 1 : (A) the time-dependent FFCO 2 and CH 4 fluxes and (B) the modified FFCO 2 fluxes. For the same change in the FFCO 2 emissions from 220 TgC, the change in the simulated ΔCO 2 /ΔCH 4 ratios for case A is about 8% lower than that for case B. 3.3 Estimation of the FFCO 2 /CH 4 emission ratio in China 3.3.1 Conversion of the ΔCO 2 /ΔCH 4 ratio to FFCO 2 /CH 4 emission ratio Using the observed monthly averages of the ΔCO 2 /ΔCH 4 ratios at HAT and YON for the JFM, we evaluated the changes in the FFCO 2 /CH 4 emission ratio in China in 2020, 2021, and 2022 from the preceding 9-year (2011–2019) averages. The monthly averages and the preceding 9-year averages of the ΔCO 2 /ΔCH 4 ratio are listed in Table 2 . The uncertainties associated with the monthly averages correspond to the standard errors, and those for the 9-year averages correspond to the standard deviations of the monthly averages during the 9-year period. These ΔCO 2 /ΔCH 4 ratios were translated into the FFCO 2 /CH 4 emission ratio in China by the linear function deduced in the previous section (Section 3.2 ). Then, we calculated the change rate of the FFCO 2 /CH 4 emission ratios from the 9-year averages and the weighted averages for those of HAT and YON. Here we set the reciprocal of the square of the uncertainty associated with each FFCO 2 /CH 4 emission ratio for the weight. Table 2 lists these estimated changes in the FFCO 2 /CH 4 emission ratio ranging from − 36–48%. Table 2 Changes in the observed ΔCO 2 /ΔCH 4 ratio and the estimated FFCO 2 /CH 4 emission ratio in China Date (year/month) Monthly ΔCO 2 /ΔCH 4 (mol mol − 1 ) 9-year averaged monthlyΔCO 2 /ΔCH 4 (mol mol − 1 ) Estimated change in the FFCO 2 /CH 4 emission ratio (%) HAT YON HAT YON HAT YON Weighted Ave. 2020/01 147 ± 2 145 ± 1 131 ± 7 133 ± 11 18 ± 10 14 ± 15 17 ± 8 2020/02 100 ± 2 97 ± 2 129 ± 11 126 ± 12 −35 ± 9 −36 ± 10 −36 ± 7 2020/03 117 ± 2 126 ± 2 133 ± 11 130 ± 11 −18 ± 11 −5 ± 13 −12 ± 8 2021/01 146 ± 2 149 ± 1 131 ± 7 133 ± 11 18 ± 10 19 ± 15 18 ± 8 2021/02 126 ± 2 126 ± 1 129 ± 11 126 ± 12 −4 ± 13 1 ± 15 −2 ± 10 2021/03 147 ± 2 170 ± 2 133 ± 11 130 ± 11 16 ± 16 48 ± 20 29 ± 12 2022/01 146 ± 2 156 ± 1 131 ± 7 133 ± 11 18 ± 10 27 ± 16 20 ± 9 2022/02 126 ± 3 124 ± 1 129 ± 11 126 ± 12 −4 ± 13 −2 ± 14 −3 ± 10 2022/03 117 ± 2 132 ± 2 133 ± 11 130 ± 11 −19 ± 11 2 ± 13 −10 ± 9 In our previous study (Tohjima et al., 2020 ), using the same monthly average ΔCO 2 /ΔCH 4 ratios at HAT and the atmospheric model simulation, we estimated the relative changes in the FFCO 2 emissions from China to be − 32 ± 12% and − 19 ± 15% for February and March 2020, respectively, under the assumption of invariable CH 4 emissions. The slight differences in the FFCO 2 emission changes in this study are attributed to the different approach of the previous study, in which the FFCO 2 emissions from China were reduced in proportion to the bottom-up estimate based on the economic activity data of Le Quéré et al. ( 2020 ). We also estimated the consecutive changes in the FFCO 2 /CH 4 emission ratio based on the 30-day moving averages of the ΔCO 2 /ΔCH 4 ratio at HAT and YON. As was done for the monthly averages, the consecutive variability ratios were converted to emission ratios, and the rate of change in the emission ratios for the preceding 9-year averages was computed. The estimated rates of change in the FFCO 2 /CH 4 emission ratio for HAT and YON are depicted in supplementary Fig. S4, and their weighted averages with the propagated uncertainties (1σ) are shown in Fig. 7 . Hereinafter, we discuss the weighted averages of the estimated FFCO 2 /CH 4 emission ratios as for the FFCO 2 emission change in China. 3.3.2 FFCO 2 /CH 4 emission change in 2020 The estimated monthly change in the FFCO 2 /CH 4 emission ratios for 2020 compared with the preceding 9-year averages were 17 ± 8%, − 36 ± 7%, and − 12 ± 8% for January, February, and March, respectively (Table 2 ). The average change for the three months is − 10 ± 9% compared with the preceding 9-year average. The value is consistent with previous estimates based on bottom-up approaches: −10.1% (− 4.6% to − 16.5%) by Le Quéré et al. ( 2020 ) and − 13% by Liu et al. ( 2020 ) for JFM. Note that these bottom-up values were reported as the changes from the emissions in the previous year (2019). As was discussed in Tohjima et al. ( 2020 ), the marked decrease in February corresponded to the period of the nationwide lockdown in China and the slight recovery in March corresponded to the transition period to normal conditions. Such a temporal change is more clearly shown in the plot of the consecutive estimation of the FFCO 2 /CH 4 emission ratio (Fig. 7 ). For comparison, the temporal changes in the FFCO 2 emissions from China based on bottom-up estimates (Le Quéré et al., 2020 and Liu et al., 2020 ) are also depicted in the figure. Both bottom-up estimates begin to decrease in late January, reach a minimum in the middle of February, then gradually return to the normal emission (0%), although the estimate of Liu et al. ( 2020 ) shows a sharp maximum at the beginning of February. The sharp maximum corresponds to the estimated FFCO 2 emission minimum of the previous year (2019), which was attributed by Liu et al. ( 2020 ) to the reduction in economic activity in China during the Chinese New Year holidays from February 4 to 10 in 2019. Except for the sharp maximum, our estimation agrees well with the bottom-up estimations. These results seem to support the reliability of our simple estimation approach based on the atmospheric ΔCO 2 /ΔCH 4 ratio. 3.3.3 FFCO 2 /CH 4 emission change in 2021 The estimated FFCO 2 /CH 4 emission ratios for 2021 were equal to or larger than the preceding 9-year average; the average changes are 18 ± 8%, − 2 ± 10%, and 29 ± 12% for January, February, and March, respectively, and the average change for JFM is 15 ± 10%. The relatively lower ratio for February than those for January and March may be attributed to the temporal decrease in FFCO 2 emissions during the Chinese New Year holidays. From the extended estimates of the bottom-up study of Liu et al. ( 2020 ) ( https://www.carbonmonitor.org.cn ), we obtained monthly changes relative to 2019 of 16%, − 1%, and 14% for January, February, and March, respectively, and the average change for JFM was 10%, which are again consistent with the estimations of this study. The consecutive variations in 2021 are shown in Fig. 7 . Compared with the bottom-up estimate of Liu et al. ( 2020 ), the temporal variability of our estimation is rather suppressed, especially during the period related to the Chinese New Year holidays. The difference is partially explained by the low time resolution (± 15 days) of our estimation. In addition, our estimated FFCO 2 emissions for March 2021 are slightly larger than the bottom-up estimates of Liu et al. ( 2020 ). These enhanced estimates were supported by the ΔCO 2 /ΔCH 4 ratios observed at both HAT and YON (see Fig. S4). Note that the ΔCO 2 /ΔCH 4 ratios at YON were more than double those at HAT. Possibly, influences from local emissions were not sufficiently eliminated by the previously determined treatment (see Section 2.2), enhancing the ΔCO 2 /ΔCH 4 ratios at YON in March 2021. Our observational result, although still having a large uncertainty, might suggest that the FFCO 2 emissions from China considerably rebounded in early 2021 despite the global effort to reduce GHGs emissions. 3.3.4 FFCO 2 /CH 4 emission change in 2022 The estimated changes in the monthly FFCO 2 /CH 4 emission ratios for 2022 are 20 ± 9%, − 3 ± 10%, and − 10 ± 9% for January, February, and March, respectively, and the average change for JFM is 2 ± 9%. The monthly FFCO 2 emission changes relative to 2019 taken from the extended estimates of the bottom-up study of Liu et al. ( 2020 ) were 15%, 3%, and 8% for January, February, and March, respectively, and the average change for JFM was 9%, which are again consistent with the estimations of this study except for March. The consecutive variations in our emission estimate for 2022 shown in Fig. 7 persistently decrease in February even after the Chinese New Year holidays and maintain the low level in March, whereas the bottom-up result of Liu et al. ( 2020 ) shows a gradual increase after the Chinese New Year holidays. The emissions from Shanghai strongly affected the ΔCO 2 /ΔCH 4 ratios observed at HAT and YON because of the relatively short distance. Therefore, our estimated changes based on atmospheric observations might reflect the FFCO 2 emission decreases due to the spread of COVID-19 in Shanghai after March. 4 Conclusions We developed a near-real-time estimation method for the change in the FFCO 2 emissions from China based on the synoptic-scale variability ratio of atmospheric CO 2 and CH 4 (ΔCO 2 /ΔCH 4 ratio) in January, February, and March (JFM) on two remote islands in Japan, HAT and YON. From simulation results based on an atmospheric transport model (NICAM-TM) with all components of realistic CO 2 and CH 4 fluxes, we found a linear relationship between the monthly averaged ΔCO 2 /ΔCH 4 ratios and the FFCO 2 /CH 4 emission ratios in China. This simulated linear relationship was used to translate the observed ΔCO 2 /ΔCH 4 ratio into FFCO 2 /CH 4 emission ratios under the assumption of no interannual BioCO 2 change during JFM. The change in the estimated FFCO 2 /CH 4 emission ratio can be interpreted as the change in the FFCO 2 emissions by assuming no interannual CH 4 emission change during JFM. Because this method is simple compared to an inverse method, near-real-time monitoring is feasible. Using the developed method, we estimated the change in the FFCO 2 /CH 4 emission ratios for 2020, 2021, and 2022 with respect to the average emission ratios for the preceding 9-year period (2011–2019), during which relatively stable ΔCO 2 /ΔCH 4 ratios were observed at both HAT and YON. The resulting changes in the FFCO 2 emissions for January, February, and March were 17 ± 8%, − 36 ± 7%, and − 12 ± 8%, respectively, in 2020 (− 10 ± 9% for JFM overall), 18 ± 8%, − 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall), and 20 ± 9%, − 3 ± 10%, and − 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). The estimations for 2020 of not only the average change but also the temporal pattern of the FFCO 2 emission change agreed well with the reported estimations based on bottom-up studies (Le Quéré et al., 2020 and Liu et al., 2021 ). Therefore, our estimations for 2021 strongly suggest that FFCO 2 emissions from China rebounded with the recovery of the socioeconomic activities after the COVID lockdown in China. However, our estimated FFCO 2 change showed a slight decrease in March 2022, suggesting that the FFCO 2 emissions from China were still affected by the infection status of the COVID-19 in China. This early estimation method proposed in this study only gives us quick but rough estimations because of a variety of assumptions. Especially the assumption that the BioCO 2 and CH 4 have no interannual variations should be validated in future studies to refine the estimated FFCO 2 change based on more comprehensive analyses. Nevertheless, we consider this estimation method useful for the verification of the GHG emission mitigation strategy in China or elsewhere with strategically positioned measurement sites. Abbreviations HAT: Hateruma Island; YON: Yonaguni Island; NIES: National Institute for Environmental Studies; JMA: Japan Meteorological Agency; RMA: Reduced major axis regression; NICAM-TM: Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-based transport model; ODIAC: Open-source Data Inventory for Anthropogenic CO 2 . Declarations Availability of data and material The time series of the atmospheric CO 2 and CH 4 mole fractions at HAT are available through the NIES database, Global Environmental Database (GED) (https://db.cger.nies.go.jp/ged/en/index.html). The time series of the atmospheric CO 2 and CH 4 mole fractions at YON are available through the website of the World Data Centre for Greenhouse Gases (WDCGG). (https://xml.kishou.go.jp/) Competing interests The authors declare that they have no competing interests. Funding This study was supported by funds provided by the Environment Research and Technology Development Fund (JPMEERF21S20800) and the Global Environmental Research Coordinate System from the Ministry of the Environment, Japan (grant no. E1451). Authors' contributions YT conceived and designed the study, YT, HM, TM, MS, KT, and KS conducted the measurements, YN carried out the model simulation, and YT, YN, and PKP developed the analysis strategy. All authors participated in the discussions and preparation of the manuscript. Authors' information Authors and Affiliations Earth System Division, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan: Yasunori Tohjima, Hitoshi Mukai, Toshinobu Machida, Motoki Sasakawa, Akihiko Ito Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan: Prabir K. Patra Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan: Kazuhiro Tsuboi Japan Meteorological Agency, Minato-ku, Tokyo 105-8431, Japan: Kazuyuki Saito Acknowledgements We are grateful to the staff members of the Global Environment Forum and the Center for Global Environmental Research, as well as the local staff members for their continued support in conducting the in-situ measurements of CO 2 and CH 4 at HAT. Great thanks are also given to many staff members of the Japan Meteorological Agency for their work in the long-term observations of atmospheric CO 2 and CH 4 at YON. The NICAM-TM simulations were performed using the NIES supercomputer system (NEC SX-Aurora). References Bauwens M, Compernolle S, Stavrakou T, Muller J-F, van Gent J, Eskes H, Levelt PF, van der A R, Veefkind JP, Vlietinck J, Yu H, Zehner C (2020) Impact of coronavirus outbreak on NO 2 pollution assessed using TROPOMI and OMI observations. Geophys Res Lett 47. https://doi.org/10.1029/2020GL087978 . e2020GL087978 Buchwitz M, Reuter M, Noël S, Bramstedt K, Schneising O, Hilker M, Andrade BF, Bovensmann H, Burrows JP, Di Noia A, Boesch H, Wu L, Landgraf J, Aben I, Retscher C, O’Dell CW, Crisp D (2021) Can a regional-scale reduction of atmospheirc CO 2 during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO 2 retrievals. Atmos Meas Tech 14:2141–2166. https://doi.org/10.5194/amt-14-2141-2021 Chevallier F, Zheng B, Broquet G, Ciais P, Liu Z, Davis SJ, Deng Z, Wang Y, Bréon F-M, O’Dell CW (2020) Local anomalies in the column-averaged dry air mole fractions of carbon dioxide across the globe during the first months of the coronavirus recession. Geophys Res Lett 47. https://doi.org/10.1029/2020GL090244 . e2020GL090244 Friedlingstein P, Jones MW, O'Sullivan M, Andrew RM, Bakker DCE, Hauck J, Le Quéré C, Peters GP, Peters W, Pongratz J, Sitch S, Canadell JG, Ciais P, Jackson RB, Alin SR, Anthoni P, Bates NR, Becker M, Bellouin N, Bopp L, Chau TTTC, Chevallier F, Chini LP, Cronin M, Currie KI, Decharme B, Djeutchouang LM, Dou X, Evans W, Feely RA et al (2022) Glocal carbon budget 2021. Earth Syst Sci Data 14:1917–2005. https://doi.org/10.5194/essd-14-1917-2022 Gilfillan D, Marland G (2021) CDIAC-FF: global and national CO. emissions from fossil fuel combustion and cement manufacture: 1751–2017 Earth Syst Sci Data 13:1667–1680. https://doi.org/10.5194/essd-13-1667-2021 Hirsch RM, Gilroy EJ (1984) METHODS OF FITTING A STRAIGHT LINE TO DATA: EXAMPLES IN WATER RESOURCES. J Am Water Resour Assoc 20:705–711 Iida Y, Kojima A, Takatani Y, Nakano T, Sugimoto H, Midorikawa T, Ishii M (2015) Trends in pCO. and sea-air CO2 flux over the global open oceans for the last two decades J Oceanogr 71:637–661. DOI: 10.1007/s10872-015-0306-4 Iida Y, Takatani Y, Kojima A, Ishii M (2021) Global trends of ocean CO 2 sink and ocean acidification: An obsevation-based reconstruction of surface ocean inorganic carbon variables. J Oceanogr 77:323–358. doi: 10.1007/s10872-020-00571-5 Ito A, Tohjima Y, Saito T, Umezawa T, Hajima T, Hirata R, Saito M, Terao Y (2019) Methane budget of East Asia, 1990–2015: A bottom-up evaluation. Sci Total Env 676:40–52. https://doi.org/10.1016/j.scitotenv.2019.04.263 Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The JRA-55 reanalysis: General specifications and basic characteristics. J Meteorol Soc Japan Ser II 93:5–48. DOI:10.215/jmsj.2015-001 Le T, Wang Y, Liu L, Yang J, Yung YL, Li G, Seinfeld JH (2020) Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369(6504):702–706. doi: 10.1126/science.abb7431 Le Quéré C, Jackson RB, Jones MW, Smith AJP, Abernethy S, Andrew RM, De-Gol AJ, Willis DR, Shan Y, Canadell JG, Friedlingstein P, Creutzig F, Peters GP (2020) Temporary reduction in daily global CO 2 emissions during the COVID-19 forced confinement. Nat Clim Chang 10:647–653. https://doi.org/10.1038/s41558-020-0797-x Le Quéré C, Peters GP, Friedlingstein P, Andrew RM, Canadell JG, Davis SJ, Jackson RB, Jones MW (2021) Fossil CO 2 emissions in the post-COVID-19 era. Nat Clim Chang 11:197–199. https://doi.org/10.1038/s41558-021-01001-0 Liu D, Sun W, Zeng N, Han P, Yao B, Liu Z, Wang P, Zheng K, Mei H, Cai Q (2021) Observed decreases in on-road CO 2 concentratrions in Beijing during COVID-19 restrictions. Atmos Chem Phys 21:4599–4614. https://doi.org/10.5194/acp-21-4599-2021 Liu F, Page A, Strode SA, Yoshida Y, Choi S, Zheng B, Lamsal LN, Li C, Krotkov NA, Eskes H, van der Veefkind AR, Levelt P, Hauser PF, Joiner OP J (2020) Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19. Sci Adv 6:eabc2992. https://doi.org/10.1126/sciadv.abc2992 Machida T, Matsueda H, Sawa Y, Nakagawa Y, Hirotani K, Kondo N, Goto K, Nakazawa T, Ishikawa K, Ogawa T (2008) Worldwide measurements of atmospheric CO 2 and other trace gas species using commercial airlines. J Atmos Ocean Technol 25:1744–1754. https://doi.org/10.1175/2008JTECHA1082.1 Niwa Y, Tomita H, Satoh M, Imasu R (2011) A three-dimensional icosahedral grid advection scheme preserving monotonicity and consistency with continuity for atmospheric tracer transport. J Meteorol Soc Japan 89:255–268. https://doi.org/10.2151/jmsj.2011-306 Niwa Y, Machida T, Sawa Y, Matsueda H, Schuck TJ, Brenninkmeijer CAM, Imasu R, Satoh M (2012) Imposing strong constraints on tropical terrestrial CO 2 fluxes using passenger aircraft based measurements. J Geophys Res 117:D11303. https://doi.org/10.1029/2012JD017474 Niwa Y, Tomita H, Satoh M, Imasu R, Sawa Y, Tsuboi K, Matsueda H, Machida T, Sasakawa M, Belan B, Saigusa N (2017a) A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 1. Off-line forward and adjoint transport models. Geosci Model Dev 10:1157–1174. https://doi.org/10.5194/gmd-10-1157-2017 Niwa Y, Fujii Y, Sawa Y, Iida Y, Ito A, Satoh M, Imasu R, Tsuboi K, Matsueda H, Saigusa N (2017b) A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 2. Optimization scheme and identical twin experiment of atmospheric CO 2 inversion. Geosci Model Dev 10:2201–2219. https://doi.org/10.5194/gmd-10-2201-2017 Oda T, Maksyutov S (2011) A very high-resolution (1 km×1 km) global fossil fuel CO 2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos Chem Phys 11:543–556 Oda T, Maksyutov S, Andres RJ (2018) The open-source data inventory for anthropogenic CO 2 , version 2016 (ODIAC2016): A global monthly fossil fuel CO 2 gridded emissions data product for tracer transport simulations and surface flux inversions. Earth Syst Sci Data 10:87–107. https://doi.org/10.5194/essd-10-87-2018 Reuter M, Buchwitz M, Hilboll A, Richter R, Schneising O, Hilker M, Heymann J, Bovensmann H, Burrows JP (2014) Decreasing emissions of NOx relative to CO 2 in East Asia inferred from satellite observations. Nat Geosci 7:792–795. https://dor.org/10.1038/NGEO2257 Saeki T, Patra PK (2017) Implications of overestimated anthropogenic CO 2 emissoins on East Asian and global land CO 2 flux inversion. Geosci Lett 4:9. https://dor.org/10.1184/s40562-017-0074-7 Satoh M (2002) Conservative scheme for the compressible nonhydrostatic models with the horizontally explicit and vertically implicit time integration scheme. Mon Wea Rev 130:1227–1245 Saunois M, Stavert AR, Poulter B, Bousquet P, Canadell JG, Jackson RB, Raymond PA, Dlugokencky EJ, Houweling S, Patra PK, Ciais P, Arora VK, Bastviken D, Bergamaschi P, Blake DR, Brailsford G, Bruhwiler L, Carlson KM, Carrol M, Castaldi S, Chandra N, Crevoisier C, Crill PM, Covey K, Curry CL, Etiope G, Frankenberg C, Gedney N, Hegglin MI, Höglund-Isaksson L et al (2020) The Global Methane Budget 2000–2017. Earth Syst Sci Data 12:1561–1623. https://doi.org/10.5194/essd-12-1561-2020 Shah V, Jacob DJ, Li K, Silvern RF, Zhai S, Liu M, Lin J, Zhang Q (2020) Effect of changing NO x lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO 2 columns over China. Atmos Chem Phys 20:1483–1495. https://doi.org/10.5194/acp-20-1483-2020 Sim S, Lee H, Oh E, Kim S, Ciais P, Piao S, Lin JC, Mallia DV, Lee S, Kim Y-H, Park H, Yun J, Jeong S (2022) Short-term reduction of regional enhancement of atmospheric CO 2 in China during the first COVID-19 pandemic period. Environ Res Lett 17:024036. https://doi.org/10.1088/1748-9326/ac507d Takatani Y, Enyo K, Iida Y, Kojima A, Nakano T, Sasano D, Kosugi N, Midorikawa T, Suzuki T, Ishii M (2014) Relationships between total alkalinity in surface water and sea surface dynamic height in the Pacific Ocean. J Geophys Res 119:2806–2814. https://doi.org/10.1002/2013JC009739 Terao Y, Mukai H, Nojiri Y, Machida T, Tohjima Y, Saeki T, Maksyutov S (2011) Interannual variability and trends in atmospheric methane over the western Pacific from 1994 to 2010. J Geophys Res 116:D14303. https://doi.org/10.1029/2010JD015467 Tohjima Y, Machida T, Utiyama M, Katsumoto M, Fujinuma Y, Maksyutov S (2002) Analysis and presentation of in situ atmospheric methane measurements from Cape Ochi-ishi and Hateruma Island. J Geophys Res 107:D12. https://doi.org/10.1029/2001JD001003 Tohjima Y, Mukai H, Hashimoto S, Patra PK (2010) Increasing synoptic scale variability in atmospheric CO 2 at Hateruma Island associated with increasing East-Asian emissions. Atmos Chem Phys 10:453–462. https://doi.org/10.5194/acp-10-453-2010 Tohjima Y, Kubo M, Minejima C, Mukai H, Tanimoto H, Ganshin A, Maksyutov S, Katsumata K, Machida T, Kita K (2014) Temporal changes in the emissions of CH 4 and CO from China estimated from CH 4 / CO 2 and CO / CO 2 correlations observed at Hateruma Island. Atmos Chem Phys 14:1663–1677. https://doi.org/10.5194/acp-14-1663-2014 Tohjima Y, Patra PK, Niwa Y, Mukai H, Sasakawa M, Machida T (2020) Detection of fossil-fuel CO 2 plummet in China due to COVID-19 by observation at Hateruma. Sci Rep 10:18688. https://doi.org/10.1038/s41598-020-75763-6 Tohjima Y, Niwa Y, Tsuboi K, Saito K (2022) Did atmospheric CO 2 and CH 4 observation at Yonagunijima detect fossil-fuel CO 2 reduction due to COVID-19 lockdown? J Meteor Soc Japan 100(2):437–444. https//doi.org/10.2151/jmsj.2022-021 Tsuboi K, Matsueda H, Sawa Y, Niwa Y, Nakamura M, Kuboike D, Saito K, Ohmori H, Iwatsubo S, Nishi H, Hanamiya Y, Tsuji K, Baba Y (2013) Evaluation of a new JMA aircraft flask sampling system and laboratory trace gas analysis system. Atoms Meas Tech 6:1257–1270. https//doi.org/10.5194/amt-6-1257-2013 Tsutsumi Y, Mori K, Ikegami M, Tashiro T, Tsuboi K (2006) Long-term trends of greenhouse gases in regional and background events observed during 1998–2004 at Yonagunijima located to the east of the Asian continent. Atmos Env 40:5868–5879 Umezawa T, Matsueda H, Oda T, Higuchi K, Sawa Y, Machida T, Niwa Y, Maksyutov S (2020) Statistical characterization of urban CO 2 emission signals observed by commercial airliner measurements. Sci Rep 10:7963. https://doi.org/10.1038/s41598-020-64769-9 United Nations Environment Programme (2021) Emissions Gap Report 2021: The heat is on – A world of climate promises not yet delivered. Nairobi, https://www.unep.org/emissions-gap-report-2021 , Accessed August 2, 2022 Wada A, Matsueda H, Murayama S, Taguchi S, Hirao S, Yamazawa H, Moriizumi J, Tsuboi K, Niwa Y, Sawa Y (2013) Quantification of emission estimates of CO 2 , CH 4 , and CO for East Asia derived from atmospheric radon-222 measurements over the western North Pacific. Tellus B 65:18037. https://doi.org/10.3402/tellusb.v65i0.18037 Watanabe F, Uchino O, Joo Y, Aono M, Higashijima K, Hirano Y, Tsuboi K, Suda K (2000) Interannual variation of growth rate of atmospheric carbon dioxide concentration observed at the JMA’s three monitoring stations: large increase in concentration of atmospheric carbon dioxide in 1998. J Meteorol Soc Jpn 78::673–682 Weir B, Crisp D, O’Dell CW, Basu S, Chatterjee A, Kolassa J, Oda T, Pawson S, Poulter B, Zhang Z, Ciais P, Davis SJ, Liu Z, Ott LE (2021) Regional impacts of COVID-19 on carbon dioxide detected worldwide from space. Sci Adv 7:eabf9415. https://doi.org/10.1126/sciadv.abf9415 Wu S, Zhou W, Xiong X, Burr GB, Cheng P, Wang P, Niu Z, Hou Y (2021) The impact of COVID-19 lockdown on atmospheric CO 2 in Xi’an, China. Environ Res 197:111208. https://doi.org/10.1016/j.envres.2021.111208 Yokota T, Yoshida Y, Eguchi N, Ota Y, Tanaka T, Watanabe H, Maksyutov S (2009) Global concentrations of CO 2 and CH 4 retrieved from GOSAT: first preliminary results. SOLA 5:160–163. https//doi.org/10.2151/sola.2009-041 Yoshida Y, Kikuchi N, Morino I, Uchino O, Oshchepkov S, Bril A, Saeki T, Schutgens N, Toon GC, Wunch D, Roehl CM, Wennberg PO, Griffith DWT, Deutscher NM, Warneke T, Notholt J, Robinson J, Sherlock V, Connor B, Rettinger M, Sussmann R, Ahonen P, Heikkinen P, Kyrö E, Mendonca J, Strong K, Hase F, Dohe S, Yokota T (2013) Improvement of the retrieval algorithm for GOSAT SWIR XCO 2 and XCH 4 and their validation using TCCON data. Atmos Meas Tech 6:1533–1547. https://doi.org/10.5194/amt-6-1533-2013 Zhang X, Nakazawa T, Ishizawa M, Aoki S, Nakaoka S, Sugawara S, Maksyutov S, Saeki T, Hayasaka T (2007) Temporal variations of atmospheric carbon dioxide in the southernmost part of Japan. Tellus B 59:645–663 Zheng B, Geng G, Ciais P, Davis SJ, Martin RV, Meng J, Wu N, Chevallier F, Broquet G, Boersma F, van der A R, Lin J, Guan D, Lei Y, He K, Zhang Q (2020) Satellite-based estimates of decline and rebound in China’s COâ‚‚ emissions during COVID-19 pandemic. Sci Adv 6:eabd4998. https//doi.org/10.1126/sciadv.abd4998 Supplementary Files GraphicalAbstractImage.jpg supplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2023 Read the published version in Progress in Earth and Planetary Science → Version 1 posted Editorial decision: Major revision 25 Oct, 2022 Reviewers agreed at journal 06 Sep, 2022 Reviewer # 1 agreed at journal 05 Sep, 2022 Reviewers invited by journal 02 Sep, 2022 Submission checks completed at journal 30 Aug, 2022 Editor invited by journal 30 Aug, 2022 Editor assigned by journal 30 Aug, 2022 First submitted to journal 29 Aug, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2009154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Methodology","associatedPublications":[],"authors":[{"id":133452898,"identity":"e69e9b42-0a7b-4d3b-80dd-6a521b30b681","order_by":0,"name":"Yasunori Tohjima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFACHgaDBAYGOQZmKJ+xgaAGiBZj0rSAQCIhhQhgz957oOBhjl36dnbmwx8YftkwMM8moJuH51yCQeK25NydzWxpEox9aQyMcw4Q0CKRYwDUwpy74TCPGQNjz2EGxhkJRGmpTzc4zP/5AylaDicYHOZhkGD4QYyWM2dAWo4bbjjMZiaR2JDGQ9Av7O09ZoY/t1XLG5w//PjDhz82coaEQgwI2AzgzMQ2Bh7DGQR1MDA/QLD/MDDISxDWMgpGwSgYBSMLAABBZ0DLLHx+VgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2153-6376","institution":"National Institute for Environmental Studies: Kokuritsu Kankyo Kenkyujo","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yasunori","middleName":"","lastName":"Tohjima","suffix":""},{"id":133452899,"identity":"04ec2142-d301-4619-9d1f-be65fe4d7967","order_by":1,"name":"Yosuke Niwa","email":"","orcid":"","institution":"National Institute for Environmental Studies: Kokuritsu Kankyo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yosuke","middleName":"","lastName":"Niwa","suffix":""},{"id":133452900,"identity":"13533832-1085-4399-8060-af5a6336c7b8","order_by":2,"name":"Prabir K. Patra","email":"","orcid":"","institution":"JAMSTEC: Kaiyo Kenkyu Kaihatsu Kiko","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prabir","middleName":"K.","lastName":"Patra","suffix":""},{"id":133452901,"identity":"5441db47-d6ba-4f83-a8ad-7b722dc5750b","order_by":3,"name":"Hitoshi Mukai","email":"","orcid":"","institution":"National Institute for Environmental Studies: Kokuritsu Kankyo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hitoshi","middleName":"","lastName":"Mukai","suffix":""},{"id":133452902,"identity":"ad3bd04f-e923-4f08-a679-5c95d62a019c","order_by":4,"name":"Toshinobu Machida","email":"","orcid":"","institution":"National Institute for Environmental Studies: Kokuritsu Kankyo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Toshinobu","middleName":"","lastName":"Machida","suffix":""},{"id":133452903,"identity":"3be668c9-0847-4f49-87a2-e633245ef9fa","order_by":5,"name":"Motoki Sasakawa","email":"","orcid":"","institution":"National Institute for Environmental Studies: Kokuritsu Kankyo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Motoki","middleName":"","lastName":"Sasakawa","suffix":""},{"id":133452904,"identity":"a854c8aa-df1a-47ad-a15c-40199362b686","order_by":6,"name":"Kazuhiro Tsuboi","email":"","orcid":"","institution":"Kishocho Kisho Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kazuhiro","middleName":"","lastName":"Tsuboi","suffix":""},{"id":133452905,"identity":"0475a335-7c20-41b4-85db-4941f98a3cb2","order_by":7,"name":"Kazuyuki Saito","email":"","orcid":"","institution":"Japan Meteorological Agency","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kazuyuki","middleName":"","lastName":"Saito","suffix":""},{"id":133452906,"identity":"1445d120-a026-471b-bb4e-5edd40795a4f","order_by":8,"name":"Akihiko Ito","email":"","orcid":"","institution":"National Institute for Environmental Studies: Kokuritsu Kankyo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akihiko","middleName":"","lastName":"Ito","suffix":""}],"badges":[],"createdAt":"2022-08-29 09:16:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2009154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2009154/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40645-023-00542-6","type":"published","date":"2023-03-02T19:29:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":26117932,"identity":"b768ec45-b639-406e-80f1-7c0c92453af6","added_by":"auto","created_at":"2022-09-06 14:35:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":363815,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the locations of Hateruma Island (HAT) and Yonaguni Island (YON).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/e4d5e4d7e879b870255c35f9.png"},{"id":26117948,"identity":"5178f68e-9eb4-49f2-b50a-03d6f33542a4","added_by":"auto","created_at":"2022-09-06 14:35:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1369843,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of (a) atmospheric CO\u003csub\u003e2\u003c/sub\u003e and (b) CH\u003csub\u003e4\u003c/sub\u003e hourly mole fractions. The data obtained at YON (gray lines) and HAT (red lines) during the periods from December 15, 2019, to April 15, 2020, and from December 15, 2020, to April 15, 2021, are depicted. The simulated CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e for HAT are plotted as blue lines (see text).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/ec44bec5b5e77231171ec943.jpg"},{"id":26117445,"identity":"f02c6a10-1629-457d-8781-2fdebb3d6016","added_by":"auto","created_at":"2022-09-06 14:30:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1027610,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal changes in the monthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios based on the observations at HAT and YON. The monthly averages at HAT (closed red symbols) and at YON (open black symbols) for January (triangles), February (circles), and March (squares) from 1998 to 2022 are plotted. Solid red and broken black lines represent the average values of the monthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios during a 9-year period (2011-2019) for HAT and YON, respectively. The gray shaded area represents the 95% confidence interval for the average for YON. The broken and solid blue lines represent the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China (right blue Y-axis) taken from GCP (Friedlingstein, et al., 2022) and ODIAC (Oda et al., 2018), respectively. The broken orange line represents the temporal change in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio, which was based on the emissions from China used in the model simulation in this study (Fig. S1) for the three months.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/51a6c0139223b4df91263f42.jpg"},{"id":26117935,"identity":"0be053ee-32b7-4a32-8c1a-5d5d258bb233","added_by":"auto","created_at":"2022-09-06 14:35:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1854917,"visible":true,"origin":"","legend":"\u003cp\u003eConsecutive changes in the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio during January-March in 2020, 2021, and 2022. The changes based on the observations at (a) HAT and (b) YON are depicted. The pink dots with lines and the red squares represent the 30-day moving averages and the monthly averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio, respectively. The gray open circles and vertical bars are averages of the 30-day moving averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio for the preceding 9-years (2011-2019) and their uncertainties, respectively. The light blue shaded areas correspond to the Chinese New Year holidays.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/a7b6e3e1a3e97efe763131e1.jpg"},{"id":26117440,"identity":"8a083e2d-747d-4867-ae10-9bfa0ad4d0be","added_by":"auto","created_at":"2022-09-06 14:30:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":751600,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal change in the monthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios during January-March from 2000 to 2021. The simulated monthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios based on the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e emissions are plotted as purple circles. The observed monthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT are plotted as gray circles for comparison. The purple circles surrounded by the dotted line represent the outliers, which are more than 20 mol mol\u003csup\u003e-1\u003c/sup\u003e larger than the corresponding observed ratios. The color-coded triangles represent the simulated results based on the modified FFCO\u003csub\u003e2\u003c/sub\u003e emissions from 55% to 130% for the meteorological fields of 2020 and 2021.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/61d3da31f8b440127d4841e2.jpg"},{"id":26118828,"identity":"5674c50b-84b8-47c0-a89b-98a727e74e51","added_by":"auto","created_at":"2022-09-06 14:45:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":816920,"visible":true,"origin":"","legend":"\u003cp\u003eA scatter plot between the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios in China and the simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios for HAT. The plots based on the time-dependent and modified FFCO\u003csub\u003e2\u003c/sub\u003e emissions in China are shown as red and blue circles, respectively (see text). The red, blue, and black lines represent linear regression lines for the plots based on the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e, the modified FFCO\u003csub\u003e2\u003c/sub\u003e, and the total FFCO\u003csub\u003e2\u003c/sub\u003e, respectively. The related slopes and y-axis intercepts are shown in the figure. The p-value is for the hypothesis that there is no significant difference between the slopes for the time-dependent fluxes and for the modified fluxes. The gray vertical bars are estimated uncertainties (1σ) for the regression line based on the total FFCO\u003csub\u003e2\u003c/sub\u003e data. Gray crosses are the outliers (see text).\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/1020e72aa360a0d0966e4509.jpg"},{"id":26118254,"identity":"208f3d10-9354-4e3f-8030-13439cbf8731","added_by":"auto","created_at":"2022-09-06 14:40:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1035515,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated FCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission change in China based on the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios observed at HAT and YON. The estimated results for three months (January, February, and March) in 2020, 2021, and 2022 are depicted in the left, middle, and right panels, respectively. The red circles with red lines and pink squares represent the estimates based on the 30-day moving averages and monthly averages, respectively, of the observed ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios. The vertical bars represent the uncertainties. For comparison, the temporal changes in FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China based on the bottom-up estimates of Le Quéré et al. (2020) (orange line) and Liu et al. (2021) (light blue line) are also shown. The gray shaded areas correspond to the Chinese New Year holidays.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/1ebf496786bacd9dcfa90520.jpg"},{"id":44721260,"identity":"32a32f56-4cde-47f6-9daf-7abe06051f5c","added_by":"auto","created_at":"2023-10-16 19:32:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1443904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/53a7356e-eba1-470f-9ae5-55ed05c33844.pdf"},{"id":26118253,"identity":"f14b36ed-43da-45e1-ad5d-dd397e8a3834","added_by":"auto","created_at":"2022-09-06 14:40:41","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1035515,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstractImage.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/0fd46996b08dee3aa59233cd.jpg"},{"id":26117438,"identity":"85acf8de-45fe-43d3-8a78-bd7a3df493ac","added_by":"auto","created_at":"2022-09-06 14:30:41","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":771831,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2009154/v1/acd322765f120eab27b3765a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eNear-real-time estimation of fossil fuel CO\u003csub\u003e2\u003c/sub\u003e emissions from China based on atmospheric observations at Hateruma and Yonaguni Islands, Japan\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTo 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\u0026deg;C, preferably 1.5\u0026deg;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, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 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 (CO\u003csub\u003e2\u003c/sub\u003e) emissions from fossil fuel combustion and cement manufacture (FFCO\u003csub\u003e2\u003c/sub\u003e) 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, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, it is also crucial to develop independent and observational methods for estimation of the emissions for tracking policy implementation.\u003c/p\u003e \u003cp\u003eSystematic observations of atmospheric GHGs, including CO\u003csub\u003e2\u003c/sub\u003e and methane (CH\u003csub\u003e4\u003c/sub\u003e), 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., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tohjima et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), ships (e.g., Terao et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tohjima et al., 2012), aircraft (e.g., Machida et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tsuboi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Umezawa et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and satellites (e.g., Yokota et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eIn 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 NO\u003csub\u003ex\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, and so on. For example, ground-based and satellite-borne observations revealed that atmospheric NO\u003csub\u003e2\u003c/sub\u003e concentrations decreased by 10\u0026thinsp;~\u0026thinsp;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., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Le et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Using such satellite-based column-averaged NO\u003csub\u003e2\u003c/sub\u003e distributions, models, and a variety of bottom-up information, Zheng et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) estimated an 11.5% decrease in China\u0026rsquo;s CO\u003csub\u003e2\u003c/sub\u003e emissions during January-April 2020 compared to the same period in 2019. Meanwhile, estimation studies on changes in CO\u003csub\u003e2\u003c/sub\u003e emissions during the COVID-19 period were conducted based on a variety of activity data: the change in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China estimated by Le Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was \u0026minus;\u0026thinsp;242 (\u0026minus;\u0026thinsp;108 to \u0026minus;\u0026thinsp;394) MtCO\u003csub\u003e2\u003c/sub\u003e during January-April 2020, which corresponds to a \u0026minus;\u0026thinsp;6.9% (\u0026minus;\u0026thinsp;3.1% to \u0026minus;\u0026thinsp;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. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that the changes in the monthly FFCO\u003csub\u003e2\u003c/sub\u003e emissions in 2020 from 2019 were \u0026minus;\u0026thinsp;18.4% in February, \u0026minus;\u0026thinsp;9.2% in March, and +\u0026thinsp;0.6% in April. These results raised the question of whether the direct observations of atmospheric CO\u003csub\u003e2\u003c/sub\u003e were able to detect the signals related to the FFCO\u003csub\u003e2\u003c/sub\u003e emission reduction caused by the COVID-19 outbreak.\u003c/p\u003e \u003cp\u003eShort-lived pollution constituents like NO\u003csub\u003ex\u003c/sub\u003e, whose estimated lifetime over China is less than a day even in winter when NO\u003csub\u003ex\u003c/sub\u003e has the longest lifetime (Shah et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), showed considerable decreases associated with the COVID-19 lockdown in China, as mentioned above. In contrast, the change in the atmospheric CO\u003csub\u003e2\u003c/sub\u003e mole fraction caused by the COVID-19 pandemic is considered to be relatively small in comparison with the atmospheric CO\u003csub\u003e2\u003c/sub\u003e level because of its huge atmospheric burden and a rather long lifetime. The estimated decrease in the annual global FFCO\u003csub\u003e2\u003c/sub\u003e emissions in 2020 was 5\u0026thinsp;~\u0026thinsp;7% relative to that in 2019, which was about 10 PgC (Le Qu\u0026eacute;r\u0026eacute; et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Friedlingstein, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since the estimated change of 0.5\u0026thinsp;~\u0026thinsp;0.7 PgC corresponds to the globally averaged atmospheric CO\u003csub\u003e2\u003c/sub\u003e mole fraction of 0.2\u0026thinsp;~\u0026thinsp;0.3 ppm, it\u0026rsquo;s quite difficult to detect such subtle signals in the atmospheric CO\u003csub\u003e2\u003c/sub\u003e trends after the emitted CO\u003csub\u003e2\u003c/sub\u003e is mixed globally (Lovenduski et al., 2021). Nevertheless, a variety of studies succeeded in detecting signals related to the FFCO\u003csub\u003e2\u003c/sub\u003e reductions in China caused by the COVID-19 lockdown in both local-scale observations (Zeng et al., 2020; Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and regional-scale observations (Tojima et al., 2020; Buchwitz et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Weir et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sim et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) of atmospheric CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eTohjima et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) applied a unique method in their study, which was one of the first studies to observationally detect the regional-scale signals related to FFCO\u003csub\u003e2\u003c/sub\u003e emission decreases caused by the COVID-19 lockdown in China from the synoptic scale variability ratio of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e (ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e) observed on Hateruma Island (HAT, 24.06\u0026deg;N, 123.81\u0026deg;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 ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio roughly reflected the emission ratio of CO\u003csub\u003e2\u003c/sub\u003e to CH\u003csub\u003e4\u003c/sub\u003e from continental East Asia, especially China (Tohjima et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The monthly mean ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio showed a marked decrease in February 2020 when a severe lockdown was implemented almost across China. By using the observed changes in the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios and the simulated relationship between the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio and the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China, whose temporal pattern of the reduction caused by the COVID-19 lockdown was based on the study of Le Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we estimated the FFCO\u003csub\u003e2\u003c/sub\u003e reductions to be 32\u0026thinsp;\u0026plusmn;\u0026thinsp;12% and 19\u0026thinsp;\u0026plusmn;\u0026thinsp;15% for February and March 2020, respectively. More recently, examining the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio on Yonaguni Island (YON, 24.47\u0026deg;N, 123.01\u0026deg;E), located only about 90 km northwest of HAT, we found that the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio also showed a marked decrease in February 2020 after eliminating the local influences (Tohjima et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results convinced us of the reliability of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio as an indicator of the relative emission strength in China.\u003c/p\u003e \u003cp\u003eIn this study, we revisited the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios observed at HAT and YON to develop a near-real-time estimation method for the temporal change in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China and updated the results for 2021 and 2022. In our previous study (Tohjima et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we used prior information about the temporal variation of the FFCO\u003csub\u003e2\u003c/sub\u003e emissions based on a bottom-up estimation by Le Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to evaluate the FFCO\u003csub\u003e2\u003c/sub\u003e emission change in China in 2020. Here we developed a method based on the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio observed at HAT and YON without any prior information about the temporal emission changes. We examined the relationship between the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio and the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e 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 ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios observed at HAT and YON, we estimated the FFCO\u003csub\u003e2\u003c/sub\u003e emission changes in China during January-March (JFM) in 2020 under the assumption of invariable biospheric CO\u003csub\u003e2\u003c/sub\u003e fluxes and all CH\u003csub\u003e4\u003c/sub\u003e emissions and compared them with the previously reported estimations. Finally, we applied the above evaluation method to the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and YON and evaluated the FFCO\u003csub\u003e2\u003c/sub\u003e emission changes in China for JFM in 2021 and 2022.\u003c/p\u003e"},{"header":"2 Methods/experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Atmospheric observations at HAT and YON\u003c/h2\u003e \u003cp\u003eThe National Institute for Environmental Studies (NIES) and the Japan Meteorological Agency (JMA) began monitoring the atmospheric GHGs, including CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e at HAT and YON, respectively, in the 1990s (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The technical details for the measurements of atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e were given elsewhere (e.g., Tohjima et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e and Tohjima et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e for HAT, and Watanabe et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e and Tsutsumi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e for YON). Both islands belong to the western part of the Ryukyu Islands, located between the East China Sea and the western Pacific. Air masses are predominantly transported from the continental region of East Asia during winter and from the Pacific region during summer due to the East Asian monsoon (Wada et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tohjima et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Since HAT and YON are relatively closely located within a distance of about 90 km, almost identical seasonal cycles and trends of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e were observed on both islands (Zhang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, similar synoptic-scale variations with periods of several hours to several days were also observed at both sites, especially during winter, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Enhanced mole fractions of GHGs and related species are often observed when the continental air masses are transported to the islands. However, a previous study revealed that a substantial diurnal variation was superimposed on the synoptic-scale variation of CO\u003csub\u003e2\u003c/sub\u003e at YON, preventing us from extracting the continental emission signals from the variability ratio (Tohjima et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such a diurnal cycle with a deep trough in the daytime was attributed to the local biospheric CO\u003csub\u003e2\u003c/sub\u003e exchange on the island. The rather large local influences at YON were attributed to the differences in the site conditions: the monitoring station with a sampling tower at HAT was built at the eastern tip of the island, whereas that at YON is located inland. We needed a different treatment for the data at YON to suppress the local influences (Tohjima et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data processing procedure\u003c/h2\u003e \u003cp\u003eThe ratio of the synoptic-scale variations of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e was calculated using the same methods as those adopted by previous studies (Tohjima, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Here, we provide a brief explanation of the calculation procedure. The variability ratio, ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e, was computed as a correlation slope of a scatter plot of the consecutive time series of the two species within a certain time window. The slope was computed by a reduced major axis regression (RMA) (Hirsch and Gilroy, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). The standard deviations and the correlation coefficient were also computed at the same time. These calculations were repeated for the whole data set by shifting the time window by one hour. Then, if the standard deviation and the correlation coefficient were lower than certain criteria, those correlation slopes were discarded. Finally, the selected correlation slopes were used to compute the monthly average or the moving averages of ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWe set 0.1 ppm and 0.7 as the criteria for the standard deviation of CO\u003csub\u003e2\u003c/sub\u003e and the correlation coefficient, respectively, as was done in our previous study. As for the time window for the correlation analysis, a duration of 24 hours was used for HAT, while a much longer duration was used for YON. As mentioned in the previous section, the CO\u003csub\u003e2\u003c/sub\u003e diurnal cycle at YON showed a larger decrease in the daytime than that at HAT, which can be attributed to a larger local CO\u003csub\u003e2\u003c/sub\u003e uptake at YON. The larger diurnal cycle, enlarging the absolute value of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio at YON, made it difficult to extract the signals related to the relative emission strengths in the upwind region. However, since such a local influence was effectively eliminated by using a longer time window (84 hours) and only nighttime data in a previous study (20\u0026thinsp;\u0026minus;\u0026thinsp;06 LST), we adopted the same approach as Tohjima et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to calculate the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio at YON in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Model simulation\u003c/h2\u003e \u003cp\u003eTo quantitatively evaluate the relationship between the continental CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions and the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio at HAT and YON, we used an atmospheric transport model of a Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-based transport model (NICAM-TM: Niwa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The NICAM dynamical framework inherently guarantees the conservation of tracer mass in the atmospheric transport process without any numerical mass fixer (Satoh, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), which makes NICAM-TM suitable for studying long-lived species like greenhouse gases (e.g., Niwa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The Japanese 55-year Reanalysis data (JRA-55: Kobayashi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for the period between 2000 and 2021 were used to nudge horizontal winds in the NICAM-TM simulation, and the horizontal resolution of NICAM-TM used was approximately 112 km.\u003c/p\u003e \u003cp\u003eFor the simulation of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e, we used all components of the global surface CO\u003csub\u003e2\u003c/sub\u003e fluxes, which consist of fluxes of FFCO\u003csub\u003e2\u003c/sub\u003e, ocean CO\u003csub\u003e2\u003c/sub\u003e, and land biosphere CO\u003csub\u003e2\u003c/sub\u003e (BioCO\u003csub\u003e2\u003c/sub\u003e). For the FFCO\u003csub\u003e2\u003c/sub\u003e, we used global high-resolution flux maps from the Open-source Data Inventory for Anthropogenic CO\u003csub\u003e2\u003c/sub\u003e of version 2019 (ODIAC2019), which were available for the period from 2000 to 2018 (Oda and Maksyutov, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Oda et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For the ocean CO\u003csub\u003e2\u003c/sub\u003e, we used monthly air-sea flux maps developed by the Japan Meteorological Agency for the period from 2000 to 2018 (Takatani et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Iida et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For the BioCO\u003csub\u003e2\u003c/sub\u003e, we used averaged monthly flux maps based on the inversion for the period of 2006\u0026ndash;2008, conducted with NICAM-TM (Niwa et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As for the global surface CH\u003csub\u003e4\u003c/sub\u003e fluxes, we also used monthly inversion flux maps computed by the NICAM-TM 4D-Var system for the period from 2000 to 2017 (Niwa et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Saunois et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e mole fractions at HAT were simulated during 2000\u0026ndash;2021 by using the corresponding climate dataset from the JRA-55 reanalysis and the above CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e flux data. When there were no flux data for the corresponding years, we repeatedly used the latest flux maps instead: FFCO\u003csub\u003e2\u003c/sub\u003e flux maps for 2018, ocean CO\u003csub\u003e2\u003c/sub\u003e flux maps for 2018, and CH\u003csub\u003e4\u003c/sub\u003e flux maps for 2017. The temporal changes in the monthly emissions of the FFCO\u003csub\u003e2\u003c/sub\u003e, land BioCO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e4\u003c/sub\u003e from China used in the simulation are plotted in supplementary Fig. S1. It should be noted that we do not necessarily need prior information on emissions for the target years 2020, 2021, and 2022 in this study, because only relative emission changes are estimated based on the simulated relationship between the emission ratios and the variability ratios, as described in the following section.\u003c/p\u003e \u003cp\u003eThe time series of the simulated atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e mole fractions at HAT are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The simulations generally well reproduced the observed synoptic-scale variations of both CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e. Using these simulated time series based on the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e fluxes, we examined the relationship between the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio and the CO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China. Additionally, we simulated the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e at HAT in January, February, and March 2020 and 2021 by changing the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China in 2018 to 55, 70, 85, 115, and 130% emissions and examined the relationship between the simulated variability ratio and emission ratio using these modified FFCO\u003csub\u003e2\u003c/sub\u003e emissions. The simulated relationship between the variability ratio and the emission ratio can also be applied to the observations at YON because the distance between HAT and YON (about 90km) is comparable to the horizontal resolution of the atmospheric transport model (112 km) used in this study and the observed data for both sites show almost identical synoptic-scale variations except for diurnal cycles.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results And Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Temporal change in the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and YON\u003c/h2\u003e \u003cp\u003eThe monthly mean values of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio observed at HAT and YON between 1998 and 2022 are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As our previous study (Tohjima et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggested, there are considerable similarities in the temporal change between HAT and YON; the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at both sites show a gradual increase in 2000s and rather stable values after 2011. These trends in the variability ratio are mostly attributed to the changes in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China (Tohjima et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In fact, the pattern of the temporal changes in the annual FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China taken from estimations of the Global Carbon Project (GCP) (Friedlingstein, et al., 2021) and ODIAC (Oda et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) generally agree with that of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we also plotted the average ratios of the estimated FFCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions from China for JFM between 2000 and 2018. Note that these emission ratios were based on the emission estimates used in the model simulation of this study (Fig. S1). Since the interannual variability in the estimated CH\u003csub\u003e4\u003c/sub\u003e emissions is rather suppressed during JFM except for a gradually increasing trend, the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio for China shows a similar temporal pattern to the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies also showed marked decreases in the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and YON in February 2020, when the COVID-19-related nationwide lockdown in China considerably reduced the FFCO\u003csub\u003e2\u003c/sub\u003e emissions (Tohjima et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios for HAT and YON during the preceding 9-year (2011\u0026ndash;2019) period are drawn as red and black broken lines, respectively, with a 95% confidence interval for YON depicted as gray shaded area. The ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at both sites fall below the 95% confidence limit in February 2020. In contrast, the monthly mean ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and YON during JFM in 2021 and 2022 returned to the previous 9-year (2011\u0026ndash;2019) level or higher. This suggests that the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China in early 2021 returned to the same level as or higher than before the COVID-19 lockdown.\u003c/p\u003e \u003cp\u003eTo take a closer look at the temporal changes in the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio during the JFM in 2020, 2021, and 2022, the 30-day moving averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios for HAT and YON are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For comparison, averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios for the preceding 9 years (2011\u0026ndash;2019) were also drawn together with the standard deviations in the figure. Although the temporal resolution of the plots is low (\u0026plusmn;\u0026thinsp;15 days), the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios show decreases between January and February in 2020, minima in the middle of February, and gradual increases toward the preceding 9-year averages in March 2020. Previous studies suggested that the above temporal patterns of the consecutive ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios were consistent with the estimated change in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China based on a study by Le Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, the consecutive ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios during the JFM in 2021 are larger than the preceding 9-year average and spread. The local minima in the middle of February 2021 might be related to the reduction in economic activity during the Chinese New Year holidays. Note that the period of the Chinese New Year holidays was from January 24 to February 2 in 2020, of which the last three days were extended holidays to fight the spread of COVID-19. The New Year holidays in 2021 were from February 11 to 17, 2021 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results seem to be consistent with the recovery of the economic activity in China from the influence of the COVID-19 pandemic in early 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is noteworthy that the consecutive ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios in 2022, which were higher than the preceding 9-year average in January and decreased in February, reached the preceding 9-year level or lower in March. Since the COVID-19 infection spread again mostly in Shanghai after March, the reduced ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios might reflect the decrease in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions associated with the confinements of the socioeconomic activities in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio\u003c/h2\u003e \u003cp\u003eUsing the simulated time series of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e at HAT for the period from 2000 to 2021 based on the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e emissions from 2000 to 2018, we calculated the monthly averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio from January to March in the same way as the observed data at HAT were computed. The simulated monthly averaged ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios during 2000\u0026ndash;2021 are plotted as purple circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for the control emission cases of CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e, and the observed ratios are also plotted as gray circles for comparison. The simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios roughly trace the observed increasing trend in 2000s and plateau after 2011 except for five points enclosed by dotted lines, which are more than 20 mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e larger than the corresponding observed ratios. These discrepancies were attributed to the fact that the monthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios included some extraordinarily large values with very small CH\u003csub\u003e4\u003c/sub\u003e variability. These erroneous ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios might be attributed to uncertainties in model transport or flux distributions used in the simulation or both. Thus, we rejected these five data as outliers in the subsequent analysis. The scatter plot of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios between the observation and simulation without the above-mentioned five outliers shows a clear positive correlation with a correlation coefficient of 0.74 and a linear regression line slope of 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 (simulation/observation) (supplementary Fig. S2). The regression line was determined by the RMA method and the uncertainties (1σ) of the parameters were evaluated by a bootstrap method, in which the regression calculations were repeatedly applied to the datasets prepared by iterative resampling with replacement (n\u0026thinsp;=\u0026thinsp;10,000). Note that we also used the above approach for the regression analyses of the scatter plots in the following section. Additional sensitivity simulations are also shown for the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio based on the modified FFCO\u003csub\u003e2\u003c/sub\u003e fluxes and the meteorological reanalysis data during 2020\u0026ndash;2021 in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, where the data are plotted as color-coded triangles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relationship between the simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios and the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios in China are shown as scatter plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results based on the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e emissions (1997\u0026ndash;2021) and the modified FFCO\u003csub\u003e2\u003c/sub\u003e emissions (2020\u0026ndash;2021) from China are plotted as red and blue circles, respectively. As is expected, both datasets show positive and consistent correlations and slopes. From the linear regression analysis, we obtained slopes (variability ratio/emission ratio) and y-intercepts of 1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 and 45\u0026thinsp;\u0026plusmn;\u0026thinsp;5 (mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), respectively, for the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e and those of 1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 and 45\u0026thinsp;\u0026plusmn;\u0026thinsp;7 (mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), respectively, for the modified FFCO\u003csub\u003e2\u003c/sub\u003e. There is no significant difference between the two regression lines (p\u0026thinsp;=\u0026thinsp;0.63), suggesting that the contribution of the year-to-year differences in atmospheric transport does not strongly influence the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios. Such characteristics are brought about by the very fact that calculating the variability ratio cancels out the transport influences. Therefore, combining these two datasets, we obtained a single regression line, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e as a black line, with a slope of 1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 and a y-intercept of 46\u0026thinsp;\u0026plusmn;\u0026thinsp;4 (mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The results of the regression analysis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the regression analysis of the scatter plots of the simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio against FFCO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmission ratio or emission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTime-dependent fluxes (A)\u003c/p\u003e \u003cp\u003e(2000\u0026ndash;2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModified fluxes (B)\u003c/p\u003e \u003cp\u003e(2020\u0026ndash;2021)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAll fluxes (A\u0026thinsp;+\u0026thinsp;B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercept\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntercept\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntercept\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(FFCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;BioCO\u003csub\u003e2\u003c/sub\u003e)/CH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e56\u0026thinsp;\u0026plusmn;\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Units of intercepts are given in mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Units of slopes for FFCO\u003csub\u003e2\u003c/sub\u003e emission are given in (mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)/TgC.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e p-values are for the hypothesis that there is no significant difference between the slopes for the time-dependent fluxes and modified fluxes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios also bore linear relationships to the (FFCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;BioCO\u003csub\u003e2\u003c/sub\u003e)/CH\u003csub\u003e4\u003c/sub\u003e emission ratios and FFCO\u003csub\u003e2\u003c/sub\u003e emissions in China, as shown in supplementary Fig. S3. The results based on the above analyses are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The difference of the y-intercepts for the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e from those for the (FFCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;BioCO\u003csub\u003e2\u003c/sub\u003e)/CH\u003csub\u003e4\u003c/sub\u003e corresponds to the influence of the BioCO\u003csub\u003e2\u003c/sub\u003e emissions on the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios. The regression slopes for the emission ratios are close to unity and the y-intercepts for the (FFCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;BioCO\u003csub\u003e2\u003c/sub\u003e)/CH\u003csub\u003e4\u003c/sub\u003e are roughly close to the origin, indicating that the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT straightforwardly reflect the CO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios in China, as was indicated in a previous study (Tohjima et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As for the relationship to the FFCO\u003csub\u003e2\u003c/sub\u003e emissions, there is a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the regression slopes for the time-dependent and the modified emissions, indicating that the different rate of increase in the CH\u003csub\u003e4\u003c/sub\u003e emissions and potentially the spatial heterogeneity in the change rates of the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e emissions contribute to the temporal change in the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios. In the following sections, assuming that the land biospheric CO\u003csub\u003e2\u003c/sub\u003e emissions from China have no interannual variations, we used the linear relationship between the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios and the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio to evaluate the change in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China.\u003c/p\u003e \u003cp\u003eAlthough the JFM chosen for the analysis corresponds to the period when the biotic activities are relatively dormant, there can be a measurable interannual variability in the BioCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions (Fig. S1). From the inversely estimated BioCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions from China after 2011 based on NICAM-TM (see Fig. S1), we obtained averages and standard deviations (1σ) of 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 TgC day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.118\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008 TgCH\u003csub\u003e4\u003c/sub\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the BioCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions, respectively. These standard deviations for the BioCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions correspond to the uncertainties of about\u0026thinsp;\u0026plusmn;\u0026thinsp;14% and \u0026plusmn;\u0026thinsp;7% for the (FFCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;BioCO\u003csub\u003e2\u003c/sub\u003e)/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China for the recent decadal period, respectively. Meanwhile, the CH\u003csub\u003e4\u003c/sub\u003e emissions from China for the three months are mostly derived from anthropogenic sources, including coal mining, landfills, enteric fermentation, and other anthropogenic sources, except for paddy fields (e.g., Ito et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among these anthropogenic sources, coal mining is the largest source in China, contributing about 40% of the total emission during JFM. Since the increase in coal consumption historically enhanced the FFCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e emissions in China, it was inferred that these emissions positively correlated, as was pointed out by Saeki and Patra (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, it should be noted that such a positive correlation of the emissions might, to some extent, attenuate the temporal change in the observed ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and YON. The influence of the correlative change in the CH\u003csub\u003e4\u003c/sub\u003e emissions on the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios in this model simulation can be evaluated from the linear relationships of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios against the FFCO\u003csub\u003e2\u003c/sub\u003e emissions for the two cases listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: (A) the time-dependent FFCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes and (B) the modified FFCO\u003csub\u003e2\u003c/sub\u003e fluxes. For the same change in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from 220 TgC, the change in the simulated ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios for case A is about 8% lower than that for case B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Estimation of the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Conversion of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio to FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio\u003c/h2\u003e \u003cp\u003eUsing the observed monthly averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and YON for the JFM, we evaluated the changes in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China in 2020, 2021, and 2022 from the preceding 9-year (2011\u0026ndash;2019) averages. The monthly averages and the preceding 9-year averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The uncertainties associated with the monthly averages correspond to the standard errors, and those for the 9-year averages correspond to the standard deviations of the monthly averages during the 9-year period. These ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios were translated into the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China by the linear function deduced in the previous section (Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e). Then, we calculated the change rate of the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios from the 9-year averages and the weighted averages for those of HAT and YON. Here we set the reciprocal of the square of the uncertainty associated with each FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio for the weight. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists these estimated changes in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio ranging from \u0026minus;\u0026thinsp;36\u0026ndash;48%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in the observed ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio and the estimated FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate (year/month)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMonthly ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e (mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9-year averaged monthlyΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e (mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eEstimated change in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWeighted Ave.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020/01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020/02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;35\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;36\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;36\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020/03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;18\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;5\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;12\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021/01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021/02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;4\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;2\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021/03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022/01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022/02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;4\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;2\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;3\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022/03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;19\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn our previous study (Tohjima et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), using the same monthly average ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at HAT and the atmospheric model simulation, we estimated the relative changes in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China to be \u0026minus;\u0026thinsp;32\u0026thinsp;\u0026plusmn;\u0026thinsp;12% and \u0026minus;\u0026thinsp;19\u0026thinsp;\u0026plusmn;\u0026thinsp;15% for February and March 2020, respectively, under the assumption of invariable CH\u003csub\u003e4\u003c/sub\u003e emissions. The slight differences in the FFCO\u003csub\u003e2\u003c/sub\u003e emission changes in this study are attributed to the different approach of the previous study, in which the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China were reduced in proportion to the bottom-up estimate based on the economic activity data of Le Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also estimated the consecutive changes in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio based on the 30-day moving averages of the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio at HAT and YON. As was done for the monthly averages, the consecutive variability ratios were converted to emission ratios, and the rate of change in the emission ratios for the preceding 9-year averages was computed. The estimated rates of change in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio for HAT and YON are depicted in supplementary Fig. S4, and their weighted averages with the propagated uncertainties (1σ) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Hereinafter, we discuss the weighted averages of the estimated FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios as for the FFCO\u003csub\u003e2\u003c/sub\u003e emission change in China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission change in 2020\u003c/h2\u003e \u003cp\u003eThe estimated monthly change in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios for 2020 compared with the preceding 9-year averages were 17\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, \u0026minus;\u0026thinsp;36\u0026thinsp;\u0026plusmn;\u0026thinsp;7%, and \u0026minus;\u0026thinsp;12\u0026thinsp;\u0026plusmn;\u0026thinsp;8% for January, February, and March, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The average change for the three months is \u0026minus;\u0026thinsp;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9% compared with the preceding 9-year average. The value is consistent with previous estimates based on bottom-up approaches: \u0026minus;10.1% (\u0026minus;\u0026thinsp;4.6% to \u0026minus;\u0026thinsp;16.5%) by Le Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and \u0026minus;\u0026thinsp;13% by Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for JFM. Note that these bottom-up values were reported as the changes from the emissions in the previous year (2019).\u003c/p\u003e \u003cp\u003eAs was discussed in Tohjima et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the marked decrease in February corresponded to the period of the nationwide lockdown in China and the slight recovery in March corresponded to the transition period to normal conditions. Such a temporal change is more clearly shown in the plot of the consecutive estimation of the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For comparison, the temporal changes in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China based on bottom-up estimates (Le Qu\u0026eacute;r\u0026eacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e and Liu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) are also depicted in the figure. Both bottom-up estimates begin to decrease in late January, reach a minimum in the middle of February, then gradually return to the normal emission (0%), although the estimate of Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) shows a sharp maximum at the beginning of February. The sharp maximum corresponds to the estimated FFCO\u003csub\u003e2\u003c/sub\u003e emission minimum of the previous year (2019), which was attributed by Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to the reduction in economic activity in China during the Chinese New Year holidays from February 4 to 10 in 2019. Except for the sharp maximum, our estimation agrees well with the bottom-up estimations. These results seem to support the reliability of our simple estimation approach based on the atmospheric ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission change in 2021\u003c/h2\u003e \u003cp\u003eThe estimated FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios for 2021 were equal to or larger than the preceding 9-year average; the average changes are 18\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, \u0026minus;\u0026thinsp;2\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, and 29\u0026thinsp;\u0026plusmn;\u0026thinsp;12% for January, February, and March, respectively, and the average change for JFM is 15\u0026thinsp;\u0026plusmn;\u0026thinsp;10%. The relatively lower ratio for February than those for January and March may be attributed to the temporal decrease in FFCO\u003csub\u003e2\u003c/sub\u003e emissions during the Chinese New Year holidays. From the extended estimates of the bottom-up study of Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.carbonmonitor.org.cn\u003c/span\u003e\u003cspan address=\"https://www.carbonmonitor.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we obtained monthly changes relative to 2019 of 16%, \u0026minus;\u0026thinsp;1%, and 14% for January, February, and March, respectively, and the average change for JFM was 10%, which are again consistent with the estimations of this study. The consecutive variations in 2021 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Compared with the bottom-up estimate of Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the temporal variability of our estimation is rather suppressed, especially during the period related to the Chinese New Year holidays. The difference is partially explained by the low time resolution (\u0026plusmn;\u0026thinsp;15 days) of our estimation. In addition, our estimated FFCO\u003csub\u003e2\u003c/sub\u003e emissions for March 2021 are slightly larger than the bottom-up estimates of Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These enhanced estimates were supported by the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios observed at both HAT and YON (see Fig. S4). Note that the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at YON were more than double those at HAT. Possibly, influences from local emissions were not sufficiently eliminated by the previously determined treatment (see Section 2.2), enhancing the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios at YON in March 2021. Our observational result, although still having a large uncertainty, might suggest that the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China considerably rebounded in early 2021 despite the global effort to reduce GHGs emissions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission change in 2022\u003c/h2\u003e \u003cp\u003eThe estimated changes in the monthly FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios for 2022 are 20\u0026thinsp;\u0026plusmn;\u0026thinsp;9%, \u0026minus;\u0026thinsp;3\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, and \u0026minus;\u0026thinsp;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9% for January, February, and March, respectively, and the average change for JFM is 2\u0026thinsp;\u0026plusmn;\u0026thinsp;9%. The monthly FFCO\u003csub\u003e2\u003c/sub\u003e emission changes relative to 2019 taken from the extended estimates of the bottom-up study of Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) were 15%, 3%, and 8% for January, February, and March, respectively, and the average change for JFM was 9%, which are again consistent with the estimations of this study except for March. The consecutive variations in our emission estimate for 2022 shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e persistently decrease in February even after the Chinese New Year holidays and maintain the low level in March, whereas the bottom-up result of Liu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) shows a gradual increase after the Chinese New Year holidays. The emissions from Shanghai strongly affected the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios observed at HAT and YON because of the relatively short distance. Therefore, our estimated changes based on atmospheric observations might reflect the FFCO\u003csub\u003e2\u003c/sub\u003e emission decreases due to the spread of COVID-19 in Shanghai after March.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eWe developed a near-real-time estimation method for the change in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China based on the synoptic-scale variability ratio of atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e (ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio) in January, February, and March (JFM) on two remote islands in Japan, HAT and YON. From simulation results based on an atmospheric transport model (NICAM-TM) with all components of realistic CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes, we found a linear relationship between the monthly averaged ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios and the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios in China. This simulated linear relationship was used to translate the observed ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio into FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios under the assumption of no interannual BioCO\u003csub\u003e2\u003c/sub\u003e change during JFM. The change in the estimated FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio can be interpreted as the change in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions by assuming no interannual CH\u003csub\u003e4\u003c/sub\u003e emission change during JFM. Because this method is simple compared to an inverse method, near-real-time monitoring is feasible.\u003c/p\u003e \u003cp\u003eUsing the developed method, we estimated the change in the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios for 2020, 2021, and 2022 with respect to the average emission ratios for the preceding 9-year period (2011\u0026ndash;2019), during which relatively stable ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios were observed at both HAT and YON. The resulting changes in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions for January, February, and March were 17\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, \u0026minus;\u0026thinsp;36\u0026thinsp;\u0026plusmn;\u0026thinsp;7%, and \u0026minus;\u0026thinsp;12\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, respectively, in 2020 (\u0026minus;\u0026thinsp;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9% for JFM overall), 18\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, \u0026minus;\u0026thinsp;2\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, and 29\u0026thinsp;\u0026plusmn;\u0026thinsp;12%, respectively, in 2021 (15\u0026thinsp;\u0026plusmn;\u0026thinsp;10% for JFM overall), and 20\u0026thinsp;\u0026plusmn;\u0026thinsp;9%, \u0026minus;\u0026thinsp;3\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, and \u0026minus;\u0026thinsp;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9%, respectively, in 2022 (2\u0026thinsp;\u0026plusmn;\u0026thinsp;9% for JFM overall). The estimations for 2020 of not only the average change but also the temporal pattern of the FFCO\u003csub\u003e2\u003c/sub\u003e emission change agreed well with the reported estimations based on bottom-up studies (Le Qu\u0026eacute;r\u0026eacute; et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e and Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, our estimations for 2021 strongly suggest that FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China rebounded with the recovery of the socioeconomic activities after the COVID lockdown in China. However, our estimated FFCO\u003csub\u003e2\u003c/sub\u003e change showed a slight decrease in March 2022, suggesting that the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China were still affected by the infection status of the COVID-19 in China.\u003c/p\u003e \u003cp\u003eThis early estimation method proposed in this study only gives us quick but rough estimations because of a variety of assumptions. Especially the assumption that the BioCO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e have no interannual variations should be validated in future studies to refine the estimated FFCO\u003csub\u003e2\u003c/sub\u003e change based on more comprehensive analyses. Nevertheless, we consider this estimation method useful for the verification of the GHG emission mitigation strategy in China or elsewhere with strategically positioned measurement sites.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHAT: Hateruma Island; YON: Yonaguni Island; NIES: National Institute for Environmental Studies; JMA:\u0026nbsp;Japan Meteorological Agency; RMA:\u0026nbsp;Reduced major axis regression; NICAM-TM: Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-based transport model; ODIAC: Open-source Data Inventory for Anthropogenic CO\u003csub\u003e2\u003c/sub\u003e. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAvailability of data and material\u003c/h3\u003e\n\u003cp\u003eThe time series of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e mole fractions at HAT are available through the NIES database, Global Environmental Database (GED) (https://db.cger.nies.go.jp/ged/en/index.html).\u003c/p\u003e\n\u003cp\u003eThe time series of the atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e mole fractions at YON are available through the website of the World Data Centre for Greenhouse Gases (WDCGG). (https://xml.kishou.go.jp/)\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study was supported by funds provided by\u0026nbsp;the Environment Research and Technology Development Fund (JPMEERF21S20800) and the Global Environmental Research Coordinate System from the Ministry of the Environment, Japan (grant no. E1451).\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eYT conceived and designed the study, YT, HM, TM, MS, KT, and KS conducted the measurements, YN carried out the model simulation, and YT, YN, and PKP developed the analysis strategy. All authors participated in the discussions and preparation of the manuscript.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; information\u003c/h3\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eEarth System Division, National Institute for Environmental Studies, Tsukuba, Ibaraki 305-8506, Japan: Yasunori Tohjima, Hitoshi Mukai, Toshinobu Machida, Motoki Sasakawa, Akihiko Ito\u003c/p\u003e\n\u003cp\u003eJapan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa 236-0001, Japan: Prabir K. Patra\u003c/p\u003e\n\u003cp\u003eMeteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan: Kazuhiro Tsuboi\u003c/p\u003e\n\u003cp\u003eJapan Meteorological Agency, Minato-ku, Tokyo 105-8431, Japan: Kazuyuki Saito\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe are grateful to the staff members of the Global Environment Forum and the Center for Global Environmental Research, as well as the local staff members for their continued support in conducting the in-situ measurements of CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e at HAT. Great thanks are also given to\u0026nbsp;many staff members of the Japan Meteorological Agency for their work in the long-term observations of atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e at YON. The NICAM-TM simulations were performed using the NIES supercomputer system (NEC SX-Aurora).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBauwens M, Compernolle S, Stavrakou T, Muller J-F, van Gent J, Eskes H, Levelt PF, van der A R, Veefkind JP, Vlietinck J, Yu H, Zehner C (2020) Impact of coronavirus outbreak on NO\u003csub\u003e2\u003c/sub\u003e pollution assessed using TROPOMI and OMI observations. Geophys Res Lett 47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020GL087978\u003c/span\u003e\u003cspan address=\"10.1029/2020GL087978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2020GL087978\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuchwitz M, Reuter M, No\u0026euml;l S, Bramstedt K, Schneising O, Hilker M, Andrade BF, Bovensmann H, Burrows JP, Di Noia A, Boesch H, Wu L, Landgraf J, Aben I, Retscher C, O\u0026rsquo;Dell CW, Crisp D (2021) Can a regional-scale reduction of atmospheirc CO\u003csub\u003e2\u003c/sub\u003e during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO\u003csub\u003e2\u003c/sub\u003e retrievals. Atmos Meas Tech 14:2141\u0026ndash;2166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/amt-14-2141-2021\u003c/span\u003e\u003cspan address=\"10.5194/amt-14-2141-2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChevallier F, Zheng B, Broquet G, Ciais P, Liu Z, Davis SJ, Deng Z, Wang Y, Br\u0026eacute;on F-M, O\u0026rsquo;Dell CW (2020) Local anomalies in the column-averaged dry air mole fractions of carbon dioxide across the globe during the first months of the coronavirus recession. Geophys Res Lett 47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020GL090244\u003c/span\u003e\u003cspan address=\"10.1029/2020GL090244\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2020GL090244\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedlingstein P, Jones MW, O'Sullivan M, Andrew RM, Bakker DCE, Hauck J, Le Qu\u0026eacute;r\u0026eacute; C, Peters GP, Peters W, Pongratz J, Sitch S, Canadell JG, Ciais P, Jackson RB, Alin SR, Anthoni P, Bates NR, Becker M, Bellouin N, Bopp L, Chau TTTC, Chevallier F, Chini LP, Cronin M, Currie KI, Decharme B, Djeutchouang LM, Dou X, Evans W, Feely RA et al (2022) Glocal carbon budget 2021. Earth Syst Sci Data 14:1917\u0026ndash;2005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-14-1917-2022\u003c/span\u003e\u003cspan address=\"10.5194/essd-14-1917-2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilfillan D, Marland G (2021) CDIAC-FF: global and national CO. emissions from fossil fuel combustion and cement manufacture: 1751\u0026ndash;2017 Earth Syst Sci Data 13:1667\u0026ndash;1680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-13-1667-2021\u003c/span\u003e\u003cspan address=\"10.5194/essd-13-1667-2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirsch RM, Gilroy EJ (1984) METHODS OF FITTING A STRAIGHT LINE TO DATA: EXAMPLES IN WATER RESOURCES. J Am Water Resour Assoc 20:705\u0026ndash;711\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida Y, Kojima A, Takatani Y, Nakano T, Sugimoto H, Midorikawa T, Ishii M (2015) Trends in pCO. and sea-air CO2 flux over the global open oceans for the last two decades J Oceanogr 71:637\u0026ndash;661. DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10872-015-0306-4\u003c/span\u003e\u003cspan address=\"10.1007/s10872-015-0306-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida Y, Takatani Y, Kojima A, Ishii M (2021) Global trends of ocean CO\u003csub\u003e2\u003c/sub\u003e sink and ocean acidification: An obsevation-based reconstruction of surface ocean inorganic carbon variables. J Oceanogr 77:323\u0026ndash;358. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10872-020-00571-5\u003c/span\u003e\u003cspan address=\"10.1007/s10872-020-00571-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto A, Tohjima Y, Saito T, Umezawa T, Hajima T, Hirata R, Saito M, Terao Y (2019) Methane budget of East Asia, 1990\u0026ndash;2015: A bottom-up evaluation. Sci Total Env 676:40\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2019.04.263\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2019.04.263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The JRA-55 reanalysis: General specifications and basic characteristics. J Meteorol Soc Japan Ser II 93:5\u0026ndash;48. DOI:10.215/jmsj.2015-001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe T, Wang Y, Liu L, Yang J, Yung YL, Li G, Seinfeld JH (2020) Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 369(6504):702\u0026ndash;706. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.abb7431\u003c/span\u003e\u003cspan address=\"10.1126/science.abb7431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Qu\u0026eacute;r\u0026eacute; C, Jackson RB, Jones MW, Smith AJP, Abernethy S, Andrew RM, De-Gol AJ, Willis DR, Shan Y, Canadell JG, Friedlingstein P, Creutzig F, Peters GP (2020) Temporary reduction in daily global CO\u003csub\u003e2\u003c/sub\u003e emissions during the COVID-19 forced confinement. Nat Clim Chang 10:647\u0026ndash;653. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-020-0797-x\u003c/span\u003e\u003cspan address=\"10.1038/s41558-020-0797-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Qu\u0026eacute;r\u0026eacute; C, Peters GP, Friedlingstein P, Andrew RM, Canadell JG, Davis SJ, Jackson RB, Jones MW (2021) Fossil CO\u003csub\u003e2\u003c/sub\u003e emissions in the post-COVID-19 era. Nat Clim Chang 11:197\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41558-021-01001-0\u003c/span\u003e\u003cspan address=\"10.1038/s41558-021-01001-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu D, Sun W, Zeng N, Han P, Yao B, Liu Z, Wang P, Zheng K, Mei H, Cai Q (2021) Observed decreases in on-road CO\u003csub\u003e2\u003c/sub\u003e concentratrions in Beijing during COVID-19 restrictions. Atmos Chem Phys 21:4599\u0026ndash;4614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/acp-21-4599-2021\u003c/span\u003e\u003cspan address=\"10.5194/acp-21-4599-2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu F, Page A, Strode SA, Yoshida Y, Choi S, Zheng B, Lamsal LN, Li C, Krotkov NA, Eskes H, van der Veefkind AR, Levelt P, Hauser PF, Joiner OP J (2020) Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19. Sci Adv 6:eabc2992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.abc2992\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abc2992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachida T, Matsueda H, Sawa Y, Nakagawa Y, Hirotani K, Kondo N, Goto K, Nakazawa T, Ishikawa K, Ogawa T (2008) Worldwide measurements of atmospheric CO\u003csub\u003e2\u003c/sub\u003e and other trace gas species using commercial airlines. J Atmos Ocean Technol 25:1744\u0026ndash;1754. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2008JTECHA1082.1\u003c/span\u003e\u003cspan address=\"10.1175/2008JTECHA1082.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiwa Y, Tomita H, Satoh M, Imasu R (2011) A three-dimensional icosahedral grid advection scheme preserving monotonicity and consistency with continuity for atmospheric tracer transport. J Meteorol Soc Japan 89:255\u0026ndash;268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2151/jmsj.2011-306\u003c/span\u003e\u003cspan address=\"10.2151/jmsj.2011-306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiwa Y, Machida T, Sawa Y, Matsueda H, Schuck TJ, Brenninkmeijer CAM, Imasu R, Satoh M (2012) Imposing strong constraints on tropical terrestrial CO\u003csub\u003e2\u003c/sub\u003e fluxes using passenger aircraft based measurements. J Geophys Res 117:D11303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2012JD017474\u003c/span\u003e\u003cspan address=\"10.1029/2012JD017474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiwa Y, Tomita H, Satoh M, Imasu R, Sawa Y, Tsuboi K, Matsueda H, Machida T, Sasakawa M, Belan B, Saigusa N (2017a) A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 1. Off-line forward and adjoint transport models. Geosci Model Dev 10:1157\u0026ndash;1174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/gmd-10-1157-2017\u003c/span\u003e\u003cspan address=\"10.5194/gmd-10-1157-2017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiwa Y, Fujii Y, Sawa Y, Iida Y, Ito A, Satoh M, Imasu R, Tsuboi K, Matsueda H, Saigusa N (2017b) A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 2. Optimization scheme and identical twin experiment of atmospheric CO\u003csub\u003e2\u003c/sub\u003e inversion. Geosci Model Dev 10:2201\u0026ndash;2219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/gmd-10-2201-2017\u003c/span\u003e\u003cspan address=\"10.5194/gmd-10-2201-2017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOda T, Maksyutov S (2011) A very high-resolution (1 km\u0026times;1 km) global fossil fuel CO\u003csub\u003e2\u003c/sub\u003e emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos Chem Phys 11:543\u0026ndash;556\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOda T, Maksyutov S, Andres RJ (2018) The open-source data inventory for anthropogenic CO\u003csub\u003e2\u003c/sub\u003e, version 2016 (ODIAC2016): A global monthly fossil fuel CO\u003csub\u003e2\u003c/sub\u003e gridded emissions data product for tracer transport simulations and surface flux inversions. Earth Syst Sci Data 10:87\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-10-87-2018\u003c/span\u003e\u003cspan address=\"10.5194/essd-10-87-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuter M, Buchwitz M, Hilboll A, Richter R, Schneising O, Hilker M, Heymann J, Bovensmann H, Burrows JP (2014) Decreasing emissions of NOx relative to CO\u003csub\u003e2\u003c/sub\u003e in East Asia inferred from satellite observations. Nat Geosci 7:792\u0026ndash;795. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dor.org/10.1038/NGEO2257\u003c/span\u003e\u003cspan address=\"https://dor.10.1038/NGEO2257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeki T, Patra PK (2017) Implications of overestimated anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissoins on East Asian and global land CO\u003csub\u003e2\u003c/sub\u003e flux inversion. Geosci Lett 4:9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dor.org/10.1184/s40562-017-0074-7\u003c/span\u003e\u003cspan address=\"https://dor.10.1184/s40562-017-0074-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatoh M (2002) Conservative scheme for the compressible nonhydrostatic models with the horizontally explicit and vertically implicit time integration scheme. Mon Wea Rev 130:1227\u0026ndash;1245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunois M, Stavert AR, Poulter B, Bousquet P, Canadell JG, Jackson RB, Raymond PA, Dlugokencky EJ, Houweling S, Patra PK, Ciais P, Arora VK, Bastviken D, Bergamaschi P, Blake DR, Brailsford G, Bruhwiler L, Carlson KM, Carrol M, Castaldi S, Chandra N, Crevoisier C, Crill PM, Covey K, Curry CL, Etiope G, Frankenberg C, Gedney N, Hegglin MI, H\u0026ouml;glund-Isaksson L et al (2020) The Global Methane Budget 2000\u0026ndash;2017. Earth Syst Sci Data 12:1561\u0026ndash;1623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-12-1561-2020\u003c/span\u003e\u003cspan address=\"10.5194/essd-12-1561-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah V, Jacob DJ, Li K, Silvern RF, Zhai S, Liu M, Lin J, Zhang Q (2020) Effect of changing NO\u003csub\u003ex\u003c/sub\u003e lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO\u003csub\u003e2\u003c/sub\u003e columns over China. Atmos Chem Phys 20:1483\u0026ndash;1495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/acp-20-1483-2020\u003c/span\u003e\u003cspan address=\"10.5194/acp-20-1483-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim S, Lee H, Oh E, Kim S, Ciais P, Piao S, Lin JC, Mallia DV, Lee S, Kim Y-H, Park H, Yun J, Jeong S (2022) Short-term reduction of regional enhancement of atmospheric CO\u003csub\u003e2\u003c/sub\u003e in China during the first COVID-19 pandemic period. Environ Res Lett 17:024036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/ac507d\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ac507d\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakatani Y, Enyo K, Iida Y, Kojima A, Nakano T, Sasano D, Kosugi N, Midorikawa T, Suzuki T, Ishii M (2014) Relationships between total alkalinity in surface water and sea surface dynamic height in the Pacific Ocean. J Geophys Res 119:2806\u0026ndash;2814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2013JC009739\u003c/span\u003e\u003cspan address=\"10.1002/2013JC009739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerao Y, Mukai H, Nojiri Y, Machida T, Tohjima Y, Saeki T, Maksyutov S (2011) Interannual variability and trends in atmospheric methane over the western Pacific from 1994 to 2010. J Geophys Res 116:D14303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2010JD015467\u003c/span\u003e\u003cspan address=\"10.1029/2010JD015467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohjima Y, Machida T, Utiyama M, Katsumoto M, Fujinuma Y, Maksyutov S (2002) Analysis and presentation of in situ atmospheric methane measurements from Cape Ochi-ishi and Hateruma Island. J Geophys Res 107:D12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2001JD001003\u003c/span\u003e\u003cspan address=\"10.1029/2001JD001003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohjima Y, Mukai H, Hashimoto S, Patra PK (2010) Increasing synoptic scale variability in atmospheric CO\u003csub\u003e2\u003c/sub\u003e at Hateruma Island associated with increasing East-Asian emissions. Atmos Chem Phys 10:453\u0026ndash;462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/acp-10-453-2010\u003c/span\u003e\u003cspan address=\"10.5194/acp-10-453-2010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohjima Y, Kubo M, Minejima C, Mukai H, Tanimoto H, Ganshin A, Maksyutov S, Katsumata K, Machida T, Kita K (2014) Temporal changes in the emissions of CH\u003csub\u003e4\u003c/sub\u003e and CO from China estimated from CH\u003csub\u003e4\u003c/sub\u003e / CO\u003csub\u003e2\u003c/sub\u003e and CO / CO\u003csub\u003e2\u003c/sub\u003e correlations observed at Hateruma Island. Atmos Chem Phys 14:1663\u0026ndash;1677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/acp-14-1663-2014\u003c/span\u003e\u003cspan address=\"10.5194/acp-14-1663-2014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohjima Y, Patra PK, Niwa Y, Mukai H, Sasakawa M, Machida T (2020) Detection of fossil-fuel CO\u003csub\u003e2\u003c/sub\u003e plummet in China due to COVID-19 by observation at Hateruma. Sci Rep 10:18688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-75763-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-75763-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohjima Y, Niwa Y, Tsuboi K, Saito K (2022) Did atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e observation at Yonagunijima detect fossil-fuel CO\u003csub\u003e2\u003c/sub\u003e reduction due to COVID-19 lockdown? J Meteor Soc Japan 100(2):437\u0026ndash;444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps//doi.org/10.2151/jmsj.2022-021\u003c/span\u003e\u003cspan address=\"10.2151/jmsj.2022-021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsuboi K, Matsueda H, Sawa Y, Niwa Y, Nakamura M, Kuboike D, Saito K, Ohmori H, Iwatsubo S, Nishi H, Hanamiya Y, Tsuji K, Baba Y (2013) Evaluation of a new JMA aircraft flask sampling system and laboratory trace gas analysis system. Atoms Meas Tech 6:1257\u0026ndash;1270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps//doi.org/10.5194/amt-6-1257-2013\u003c/span\u003e\u003cspan address=\"10.5194/amt-6-1257-2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsutsumi Y, Mori K, Ikegami M, Tashiro T, Tsuboi K (2006) Long-term trends of greenhouse gases in regional and background events observed during 1998\u0026ndash;2004 at Yonagunijima located to the east of the Asian continent. Atmos Env 40:5868\u0026ndash;5879\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmezawa T, Matsueda H, Oda T, Higuchi K, Sawa Y, Machida T, Niwa Y, Maksyutov S (2020) Statistical characterization of urban CO\u003csub\u003e2\u003c/sub\u003e emission signals observed by commercial airliner measurements. Sci Rep 10:7963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-64769-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-64769-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme (2021) Emissions Gap Report 2021: The heat is on \u0026ndash; A world of climate promises not yet delivered. Nairobi, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unep.org/emissions-gap-report-2021\u003c/span\u003e\u003cspan address=\"https://www.unep.org/emissions-gap-report-2021\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed August 2, 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWada A, Matsueda H, Murayama S, Taguchi S, Hirao S, Yamazawa H, Moriizumi J, Tsuboi K, Niwa Y, Sawa Y (2013) Quantification of emission estimates of CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and CO for East Asia derived from atmospheric radon-222 measurements over the western North Pacific. Tellus B 65:18037. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3402/tellusb.v65i0.18037\u003c/span\u003e\u003cspan address=\"10.3402/tellusb.v65i0.18037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe F, Uchino O, Joo Y, Aono M, Higashijima K, Hirano Y, Tsuboi K, Suda K (2000) Interannual variation of growth rate of atmospheric carbon dioxide concentration observed at the JMA\u0026rsquo;s three monitoring stations: large increase in concentration of atmospheric carbon dioxide in 1998. J Meteorol Soc Jpn 78::673\u0026ndash;682\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir B, Crisp D, O\u0026rsquo;Dell CW, Basu S, Chatterjee A, Kolassa J, Oda T, Pawson S, Poulter B, Zhang Z, Ciais P, Davis SJ, Liu Z, Ott LE (2021) Regional impacts of COVID-19 on carbon dioxide detected worldwide from space. Sci Adv 7:eabf9415. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.abf9415\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abf9415\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, Zhou W, Xiong X, Burr GB, Cheng P, Wang P, Niu Z, Hou Y (2021) The impact of COVID-19 lockdown on atmospheric CO\u003csub\u003e2\u003c/sub\u003e in Xi\u0026rsquo;an, China. Environ Res 197:111208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2021.111208\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2021.111208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokota T, Yoshida Y, Eguchi N, Ota Y, Tanaka T, Watanabe H, Maksyutov S (2009) Global concentrations of CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e retrieved from GOSAT: first preliminary results. SOLA 5:160\u0026ndash;163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps//doi.org/10.2151/sola.2009-041\u003c/span\u003e\u003cspan address=\"10.2151/sola.2009-041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshida Y, Kikuchi N, Morino I, Uchino O, Oshchepkov S, Bril A, Saeki T, Schutgens N, Toon GC, Wunch D, Roehl CM, Wennberg PO, Griffith DWT, Deutscher NM, Warneke T, Notholt J, Robinson J, Sherlock V, Connor B, Rettinger M, Sussmann R, Ahonen P, Heikkinen P, Kyr\u0026ouml; E, Mendonca J, Strong K, Hase F, Dohe S, Yokota T (2013) Improvement of the retrieval algorithm for GOSAT SWIR XCO\u003csub\u003e2\u003c/sub\u003e and XCH\u003csub\u003e4\u003c/sub\u003e and their validation using TCCON data. Atmos Meas Tech 6:1533\u0026ndash;1547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/amt-6-1533-2013\u003c/span\u003e\u003cspan address=\"10.5194/amt-6-1533-2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Nakazawa T, Ishizawa M, Aoki S, Nakaoka S, Sugawara S, Maksyutov S, Saeki T, Hayasaka T (2007) Temporal variations of atmospheric carbon dioxide in the southernmost part of Japan. Tellus B 59:645\u0026ndash;663\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng B, Geng G, Ciais P, Davis SJ, Martin RV, Meng J, Wu N, Chevallier F, Broquet G, Boersma F, van der A R, Lin J, Guan D, Lei Y, He K, Zhang Q (2020) Satellite-based estimates of decline and rebound in China\u0026rsquo;s CO\u0026acirc;\u0026#130;\u0026#130; emissions during COVID-19 pandemic. Sci Adv 6:eabd4998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps//doi.org/10.1126/sciadv.abd4998\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.abd4998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"progress-in-earth-and-planetary-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"peps","sideBox":"Learn more about [Progress in Earth and Planetary Science](http://progearthplanetsci.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/peps/default.aspx","title":"Progress in Earth and Planetary Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fossil fuel CO2 emissions, Synoptic-scale variations, Atmospheric CO2, Atmospheric CH4, COVID-19 lockdown, East Asian Monsoon ","lastPublishedDoi":"10.21203/rs.3.rs-2009154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2009154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe developed a near-real-time estimation method for temporal changes in fossil fuel CO\u003csub\u003e2\u003c/sub\u003e (FFCO\u003csub\u003e2\u003c/sub\u003e) emissions from China for three months (January, February, March, (JFM)) based on atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e observations on Hateruma Island (HAT, 24.06\u0026deg;N, 123.81\u0026deg;E) and Yonaguni Island (YON, 24.47\u0026deg;N, 123.01\u0026deg;E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e (ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e fluxes, we found that the ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratio was linearly related to the FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios into FFCO\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e4\u003c/sub\u003e emission ratios in China. The change rates of the emission ratios were calculated relative to those for the preceding 9-year period (2011\u0026ndash;2019), during which relatively stable ΔCO\u003csub\u003e2\u003c/sub\u003e/ΔCH\u003csub\u003e4\u003c/sub\u003e ratios were observed. These changes in the emission ratios can be read as FFCO\u003csub\u003e2\u003c/sub\u003e emission changes under the assumption of no interannual variations in CH\u003csub\u003e4\u003c/sub\u003e emissions and biospheric CO\u003csub\u003e2\u003c/sub\u003e fluxes for JFM. The resulting average changes in the FFCO\u003csub\u003e2\u003c/sub\u003e emissions in January, February, and March 2020 were 17\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, \u0026minus;\u0026thinsp;36\u0026thinsp;\u0026plusmn;\u0026thinsp;7%, and \u0026minus;\u0026thinsp;12\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, respectively, (\u0026minus;\u0026thinsp;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9% for JFM overall) relative to 2011\u0026ndash;2019. These results were generally consistent with previous estimates. The emission changes for the two most recent JFM were 18\u0026thinsp;\u0026plusmn;\u0026thinsp;8%, \u0026minus;\u0026thinsp;2\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, 29\u0026thinsp;\u0026plusmn;\u0026thinsp;12%, respectively, in 2021 (15\u0026thinsp;\u0026plusmn;\u0026thinsp;10% for JFM overall) and 20\u0026thinsp;\u0026plusmn;\u0026thinsp;9%, \u0026minus;\u0026thinsp;3\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, \u0026minus;\u0026thinsp;10\u0026thinsp;\u0026plusmn;\u0026thinsp;9%, respectively, in 2022 (2\u0026thinsp;\u0026plusmn;\u0026thinsp;9% for JFM overall). These results suggest that the FFCO\u003csub\u003e2\u003c/sub\u003e emissions from China rebounded to the normal level or set a new high in early 2021 after the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai.\u003c/p\u003e","manuscriptTitle":"Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations at Hateruma and Yonaguni Islands, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-06 14:30:38","doi":"10.21203/rs.3.rs-2009154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-26T03:20:46+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-09-06T23:38:36+00:00","index":0,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-09-06T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-02T08:08:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-30T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-08-30T23:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-30T04:40:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Progress in Earth and Planetary Science","date":"2022-08-29T05:14:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"progress-in-earth-and-planetary-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"peps","sideBox":"Learn more about [Progress in Earth and Planetary Science](http://progearthplanetsci.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/peps/default.aspx","title":"Progress in Earth and Planetary Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a52ac00b-6873-4f3a-a000-f91d742b9031","owner":[],"postedDate":"September 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T19:30:58+00:00","versionOfRecord":{"articleIdentity":"rs-2009154","link":"https://doi.org/10.1186/s40645-023-00542-6","journal":{"identity":"progress-in-earth-and-planetary-science","isVorOnly":false,"title":"Progress in Earth and Planetary Science"},"publishedOn":"2023-03-02 19:29:14","publishedOnDateReadable":"March 2nd, 2023"},"versionCreatedAt":"2022-09-06 14:30:38","video":"","vorDoi":"10.1186/s40645-023-00542-6","vorDoiUrl":"https://doi.org/10.1186/s40645-023-00542-6","workflowStages":[]},"version":"v1","identity":"rs-2009154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2009154","identity":"rs-2009154","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}