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We developed a near-real-time estimation method for temporal changes in fossil fuel CO2 (FFCO2) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO2 and CH4 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 CO2 and CH4 (ΔCO2/ΔCH4) 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 CO2 and CH4 fluxes, we found that the ΔCO2/ΔCH4 ratio was linearly related to the FFCO2/CH4 emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO2/ΔCH4 ratios into FFCO2/CH4 emission ratios in China. The change rates of the emission ratios for 2020–2022 were calculated relative to those for the preceding 9-year period (2011–2019), during which relatively stable ΔCO2/ΔCH4 ratios were observed. These changes in the emission ratios can be read as FFCO2 emission changes under the assumption of no interannual variations in CH4 emissions and biospheric CO2 fluxes for JFM. The resulting average changes in the FFCO2 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 January, February, and March were 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). These results suggest that the FFCO2 emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during 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. [ABSTRACT FROM AUTHOR]

To understand the changes in CH 4 emissions in East Asia, mainly in China, we analyzed the synoptic scale variability (SSV) of CO 2 and CH 4 mole fractions at Anmyeondo (AMY; 36.53°N, 126.32°E, 46 m above sea level) in South Korea using near-surface in-situ observations and an atmospheric chemical transport model (ACTM) to investigate the role of prescribed surface fluxes on SSV. The SSV of the tracers at AMY was mainly caused by emission variations from eastern and northeastern China during the winters in 2010–2020, as indicated by the simulated concentration footprints at AMY observed using the WRF-STILT model. The estimated SSV of ΔCH 4 /ΔCO 2 mole fraction ratios from the observations was 4.2–6.2 ppb ppm−1, with a mean of 5.7 ± 0.93 ppb ppm−1 during the winters in 2010–2020. We also calculated the CH 4 /CO 2 emission ratio from China's total annual fossil-fuel emissions, excluding seasonal sources estimated from the Emission Database for Global Atmospheric Research. We found that the ratio was overestimated by ∼98.1% compared to observations, which was likely driven either due to the overestimation of CH 4 or underestimation of CO 2. We discerned that the model-simulated SSV of the ΔCH 4 /ΔCO 2 ratios generally reproduced the observations when the CH 4 FixCoal simulation case was used. This implies that CH 4 from coal was a major contributor to the observed variations in the winter period ΔCH 4 /ΔCO 2 ratios. However, the model did not reproduce the observed ratio in 2012—this discrepancy requires further study. The trend of ΔCH 4 /ΔCO 2 was a slight decrease over time, which was attributed to the earlier onset of CH 4 emissions relative to fossil-fuel CO 2 , predominantly due to the reduction of CH 4 emissions from coal. In 2013, a relatively low tracer ratio was noted, which was caused by the large contribution of CO 2 from biomass burning. Overall, the observed AMY ΔCH 4 /ΔCO 2 ratios provide an efficient approach for validating the existing estimates of East Asian sectoral emissions of CH 4. • Synoptic scale variability (SSV) of CH 4 and CO 2 at Anmyeondo, South Korea was mainly caused by emission variations from eastern and northeastern China. • Simulated wintertime SSV of CH 4 a from coal was well matched with observations at Anmyeondo during 2010–2020. • EDGAR v6 China anthropogenic CH 4 /CO 2 emission ratio was overestimated against observation based SSV ΔCH 4 /ΔCO 2 mole fractions ratios. • Decline trend of wintertime SSV ΔCH 4 /ΔCO 2 mole fractions ratios Anmyeondo was attributed to a slower growth rate of China anthropogenic CH 4 emission compared to CO 2. • Observed low ΔCH 4 /ΔCO 2 ratio in 2013 was most likely caused by large contribution CO 2 from biomass burning. [ABSTRACT FROM AUTHOR]

Observation-based verification of regional/national methane (CH 4 ) emission trends is crucial for transparent monitoring and mitigation strategy planning. Although surface observations track the global and sub-hemispheric emission trends well, their sparse spatial coverage limits our ability to assess regional trends. Dense satellite observations complement surface observations, offering a valuable means to validate emission trends, especially in regions where emissions changes are substantial but debated. The uncertainty surrounding the rate of increase in fugitive coal mine emissions in China and emissions from unconventional oil and natural gas (ONG) exploration in the United States underscores the need for rigorous validation. Here, we examine the time evolution of total column dry-air mole fractions of CH 4 (XCH 4 ) during 2010-2020 by comparing observations from the GOSAT satellite with simulations from an atmospheric chemistry-transport model (ACTM). This study analyzes emissions and XCH 4 trends in global totals and regions of India, China, the USA, and the global tropics. Our results suggest that GAINSv4 emission inventory overestimates the emission increase rate for the unconventional ONG sector of USA by about 3 times, while EDGARv6 inventory overestimates coal mine emissions in China. Emission increases in China and India agree with those estimated by GAINSv4. Analysis of spatially integrated XCH 4 statistics (mean, 1-σ standard deviation) reveals a slight systematic underestimation of total emissions in China and bias (in both directions) in different parts of USA. Our results suggest that long-term satellite observations and ACTM simulations can effectively validate emission inventories for CH 4 emissions and emission trends regionally.

The ongoing development of the Global Carbon Project (GCP) global methane (CH 4 ) budget shows a continuation of increasing CH 4 emissions and CH 4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH 4 emission estimates demonstrates robust temporal trends with CH 4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH 4  yr -1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH 4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH 4 burden and the greatest opportunities for greenhouse gas abatement., (© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO 2 ) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO 2 growth rate. Using the variabilities in CO 2 (ΔCO 2 ) and methane (ΔCH 4 ) observed at Hateruma Island, Japan during 1997-2020, we show a traceable CO 2 emission reduction in China during February-March 2020. The monitoring station at Hateruma Island observes the outflow of Chinese emissions during winter and spring. A systematic increase in the ΔCO 2 /ΔCH 4 ratio, governed by synoptic wind variability, well corroborated the increase in China's fossil-fuel CO 2 (FFCO 2 ) emissions during 1997-2019. However, the ΔCO 2 /ΔCH 4 ratios showed significant decreases of 29 ± 11 and 16 ± 11 mol mol -1 in February and March 2020, respectively, relative to the 2011-2019 average of 131 ± 11 mol mol -1 . By projecting these observed ΔCO 2 /ΔCH 4 ratios on transport model simulations, we estimated reductions of 32 ± 12% and 19 ± 15% in the FFCO 2 emissions in China for February and March 2020, respectively, compared to the expected emissions. Our data are consistent with the abrupt decrease in the economic activity in February, a slight recovery in March, and return to normal in April, which was calculated based on the COVID-19 lockdowns and mobility restriction datasets.