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https://doi.org/10.5194/acp-2020-775
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-775
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Sep 2020

17 Sep 2020

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This preprint is currently under review for the journal ACP.

Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations

Xiao Lu1, Daniel J. Jacob1, Yuzhong Zhang1,2,3, Joannes D. Maasakkers4, Melissa P. Sulprizio1, Lu Shen1, Zhen Qu1, Tia R. Scarpelli1, Hannah Nesser1, Robert M. Yantosca1, Jianxiong Sheng5, Arlyn Andrews6, Robert J. Parker7,8, Hartmut Boech7,8, A. Anthony Bloom9, and Shuang Ma9 Xiao Lu et al.
  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
  • 2School of Engineering, Westlake University, Hangzhou, Zhejiang Province, China
  • 3Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China
  • 4SRON Netherlands Institute for Space Research, Utrecht, the Netherlands
  • 5Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 6National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder, CO, USA
  • 7National Centre for Earth Observation, University of Leicester, UK
  • 8Earth Observation Science, Department of Physics and Astronomy, University of Leicester, UK
  • 9Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

Abstract. We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010–2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ datasets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern mid-latitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The GOSAT observations achieve 212 independent pieces of information (DOFS) for quantifying mean 2010–2017 anthropogenic emissions on 1009 global model grid elements, and a DOFS of 122 for 2010–2017 emission trends. Adding the in situ data increases the DOFS by about 20–30 %, to 262 and 161 respectively for mean emissions and trends. Our joint inversion finds that oil/gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, while coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as prior estimate. Oil/gas emissions in the US increase over the 2010–2017 period but decrease in Canada and Europe. Our joint GOSAT+in situ inversion yields a global methane emission of 551 Tg a−1 averaged over 2010–2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86 % of the methane sink).

Xiao Lu et al.

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Xiao Lu et al.

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Short summary
We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
We use an analytical solution to the Bayesian inverse problem to quantitatively compare and...
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