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Preprints
https://doi.org/10.5194/acp-2020-915
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-915
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Sep 2020

18 Sep 2020

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This preprint is currently under review for the journal ACP.

2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT satellite observations of atmospheric methane

Joannes D. Maasakkers1,2, Daniel J. Jacob1, Melissa P. Sulprizio1, Tia R. Scarpelli1, Hannah Nesser1, Jianxiong Sheng1,3, Yuzhong Zhang1,4,5,6, Xiao Lu1, A. Anthony Bloom7, Kevin W. Bowman7,8, John R. Worden7, and Robert J. Parker9,10 Joannes D. Maasakkers et al.
  • 1Harvard University, Cambridge, Massachusetts 02138, United States
  • 2SRON Netherlands Institute for Space Research, Utrecht, The Netherlands
  • 3Massachusetts Institute of Technology, Cambridge, MA, United States
  • 4Environmental Defense Fund, Washington, DC, USA
  • 5School of Engineering, Westlake University, Hangzhou, Zhejiang Province, China
  • 6Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China
  • 7Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 8Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA
  • 9Earth Observation Science, School of Physics and Astronomy, University of Leicester, Leicester, UK
  • 10NERC National Centre for Earth Observation, Leicester, UK

Abstract. We use 2010–2015 GOSAT satellite observations of atmospheric methane columns over North America in a high- resolution inversion of methane emissions, including contributions from different sectors and long-term trends. The inversion involves analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5° × 0.625° resolution in concentrated source regions. Analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions. Prior estimates for the inversion include a gridded version of the EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTS model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a-1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a-1). The main discrepancy is for the oil and gas production sectors where we find higher emissions than the GHGI by 35 % and 22 % respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production and we find that these are a factor 2 lower than our estimate. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a-1, on the low end of the prior WetCHARTS inventory uncertainty range (14.2 (3.3–32.4) Tg a-1) and calling for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a-1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil/gas production in the eastern US. We also find that oil/gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

Joannes D. Maasakkers et al.

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Joannes D. Maasakkers et al.

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