COVID-19 lockdown NO&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt; emission reductions can explain most of the coincident increase in global atmospheric methane

Stevenson, David S.; Derwent, Richard G.; Wild, Oliver; Collins, William

doi:10.5194/acp-2021-604

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Laughner et al (2021) found in a box model analysis that a 3% reduction in global mean OH concentration in 2020 could account for only half of the observed methane increase, which is consistent with our results that OH changes cannot explain most of the 2020 methane surge. Stevenson et al (2021) argue that most of the methane increase from 2019 to 2020 is due to a 15% reduction in global NO x emissions, based on model sensitivities of methane to NO x , but they did not consider the offsetting impact of reductions in CO emissions (Fry et al 2012). In addition, a 15% reduction applied to global annual NO x emissions in 2020 is likely excessive.…”

Section: Resultsmentioning

confidence: 99%

Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations

Jacob

Zhang

et al. 2022

Environ. Res. Lett.

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Atmospheric methane mixing ratio rose by 15 ppbv between 2019 and 2020, the fastest growth rate on record. We conduct a global inverse analysis of 2019-2020 GOSAT satellite observations of atmospheric methane to analyze the combination of sources and sinks driving this surge. The imbalance between sources and sinks of atmospheric methane increased by 31 Tg a-1 from 2019 to 2020, representing a 36 Tg a-1 forcing (direct changes in methane emissions and OH concentrations) on the methane budget away from steady state. 86% of the forcing in the base inversion is from increasing emissions (82  18% in the 9-member inversion ensemble), and only 14% is from decrease in tropospheric OH. Half of the increase in emissions is from Africa (15 Tg a-1) and appears to be driven by wetland inundation. There is also a large relative increase in emissions from Canada and Alaska (4.8 Tg a-1, 24%) that could be driven by temperature sensitivity of boreal wetland emissions.

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Section: Resultsmentioning

confidence: 99%

Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations

Jacob

Zhang

et al. 2022

Environ. Res. Lett.

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“…The overall impact of the global slowdown on CH4 emissions is found to be small and the increased atmospheric growth is the result of a continued increasing trend in CH4 emissions and possibly related to changes in atmospheric chemistry in response to the slowdown (e.g. Stevenson et al, 2021). The reason for the observed variability in emissions is unclear, it is possible a reduction in energy demand resulted in increased venting of natural gas or a change in working practice led to an increase in fugitive emissions which subsequently fell below previous levels after several months of reduced demand.…”

Section: Discussionmentioning

confidence: 99%

Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion

McNorton

Bousserez

Agustı́-Panareda

et al. 2022

Preprint

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Abstract. Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +14.7 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (~80 km), short-window (24-hour) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (63.1 Tg yr−1) and posterior (59.8 Tg yr−1) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated form our global system agree well with previous basin-wide regional studies and point source specific studies. Emission events (leaks/blowouts) >10 t hr−1 were detected, but without accurate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for 2020 were 5.7 Tg yr−1 (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest 2020 increases were seen from China (+2.6 Tg yr−1, 4.3 %), with smaller increases from India (+0.8 Tg yr−1, 2.2 %) and Indonesia (+0.3 Tg yr−1, 2.6 %). Results show the rise in emissions, and subsequent atmospheric growth, would have occurred with or without the COVID-19 slowdown. During the onset of the global slowdown (March–April, 2020) energy sector CH4 emissions from China increased; however, during later months (May–June, 2020) emissions decreased below expected pre-slowdown levels. The accumulated impact of the slowdown on CH4 emissions from March–June 2020 is found to be small. Changes in atmospheric chemistry, not investigated here, may have contributed to the observed growth in 2020. Future work aims to develop the global IFS inversion system and to extend the 4D-Var window-length using a hybrid ensemble-variational method.

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“…Figure 15b shows the difference of annual enhancement between years during the three months from January to April for the sampling regions. It can be found from Figure 15b that the annual enhancement in 2020 is lower than that in 2019 for the regions except the Shanxi Province (S5) and Taklimakan Deserts (S6), which is likely related with the reduction of anthropogenic emissions caused by the lockdown during this period from January to April in 2020 due to the coronavirus disease 2019 (COVID-19) [87,88]. The NO 2 concentration in Shanxi Province during this lockdown period showed the similar change to XCH 4 [89], which is likely because the needs of heating in winter did not decrease; thus, there was no significant reduction of emissions from the fossil fuel production and use.…”

Section: Temporal Variations Of Xch4 For Various Surface Emissionsmentioning

confidence: 90%

Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations

Lei

Song

et al. 2022

Remote Sensing

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Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.

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COVID-19 lockdown NO<sub>x</sub> emission reductions can explain most of the coincident increase in global atmospheric methane

Cited by 5 publications

References 14 publications

Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations

Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations

Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion

Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations

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