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https://doi.org/10.5194/acp-2020-964
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-964
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 Sep 2020

25 Sep 2020

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This preprint is currently under review for the journal ACP.

Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations

Yuzhong Zhang1,2,3, Daniel J. Jacob3, Xiao Lu3, Joannes D. Maasakkers4, Tia R. Scarpelli3, Jian-Xiong Sheng5, Lu Shen3, Zhen Qu3, Melissa P. Sulprizio3, Jinfeng Chang6, Anthony A. Bloom7, Shuang Ma7, John Worden7, Robert J. Parker8,9, and Hartmut Boesch8,9 Yuzhong Zhang et al.
  • 1School of Engineering, Westlake University, Hangzhou, Zhejiang, China
  • 2Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
  • 3School of Engineering and Applied Science, Harvard University, MA, USA
  • 4SRON Netherlands Institute for Space Research, Utrecht, the Netherlands
  • 5Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 6Zhejiang University, Hangzhou, Zhejiang, China
  • 7Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 8National Centre for Earth Observation, University of Leicester, UK
  • 9Earth Observation Science, School of Physics and Astronomy, University of Leicester, UK

Abstract. We conduct a global inverse analysis of 2010–2018 GOSAT satellite observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes 2010–2018 anthropogenic methane emissions and their trends on a 4º × 5º grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane) also for individual years. We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. Our inversion successfully reduces the errors against the independent methane observations from the TCCON network and reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil/gas), and too low for Venezuela (oil/gas) and the U.S. (oil/gas). We show that the 2010–2018 increase in global methane emissions is mainly driven by tropical wetlands (Amazon and tropical Africa), boreal wetlands (Eurasia), and tropical livestock (South Asia, Africa, Brazil), with no significant trend in oil/gas emissions. While the rise in tropical livestock emissions is consistent with bottom-up estimates of rapidly growing cattle populations, the rise in wetland emissions needs to be better understood. The sustained acceleration of growth rates in 2016–2018 relative to 2010–2013 is mostly from wetlands, while the peak methane growth rates in 2014–2015 are also contributed by low OH concentrations (2014) and high fire emissions (2015). Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

Yuzhong Zhang et al.

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Short summary
We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical (Amazon, tropical Africa) and boreal wetlands (Eurasia), and tropical livestock (South Asia, Africa, Brazil), with no significant trend in oil/gas emissions. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
We use 2010–2018 satellite observations of atmospheric methane to interpret the factors...
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