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Discussion papers
https://doi.org/10.5194/acp-2018-1365
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1365
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Jan 2019

Research article | 18 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015

Joannes D. Maasakkers1, Daniel J. Jacob1, Melissa P. Sulprizio1, Tia R. Scarpelli1, Hannah Nesser1, Jian-Xiong Sheng1, Yuzhong Zhang1, Monica Hersher1, A. Anthony Bloom2, Kevin W. Bowman2, John R. Worden2, Greet Janssens-Maenhout3, and Robert J. Parker4,5,6 Joannes D. Maasakkers et al.
  • 1Harvard University, Cambridge, Massachusetts 02138, United States
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 3European Commission Joint Research Centre, Ispra (Va), Italy
  • 4Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
  • 5Leicester Institute for Space and Earth Observation, University of Leicester, Leicester, UK
  • 6NERC National Centre for Earth Observation, UK

Abstract. We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used even when prior error standard deviation distributions are log-normal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil/gas emissions in the EDGAR v4.3.2 inventory, but little error in the US where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil/gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC) and our inversion is generally more consistent with the UNFCCC data. The observed 2010–2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8 ± 0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

Joannes D. Maasakkers et al.
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Joannes D. Maasakkers et al.
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Short summary
We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane emissions and their trends, as well as the concentration and trend of tropospheric OH (hydroxyl radical, methane's main sink). We find overestimates of Chinese coal and Middle East oil/gas emissions in the prior estimate. The 2010–2015 growth in methane is attributed to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH is small in comparison.
We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane...
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