A Bayesian framework for deriving sector-based methane emissions from top-down fluxes

Communications Earth & Environment, vol. 2

Abstract

Abstract Atmospheric methane observations are used to test methane emission inventories as the sum of emissions should correspond to observed methane concentrations. Typically, concentrations are inversely projected to a net flux through an atmospheric chemistry-transport model. Current methods to partition net fluxes to underlying sector-based emissions often scale fluxes based on the relative weight of sectors in a prior inventory. However, this approach imposes correlation between emission sectors which may not exist. Here we present a Bayesian optimal estimation method that projects inverse methane fluxes directly to emission sectors while accounting uncertainty structure and spatial resolution of prior fluxes and emissions. We apply this method to satellite-derived fluxes over the U.S. and at higher resolution over the Permian Basin to demonstrate that we can characterize a sector-based emission budget. This approach provides more robust comparisons between different top-down estimates, critical for assessing the efficacy of policies intended to reduce emissions.

Authors 12

1. Daniel Cusworth corresponding

   Jet Propulsion Laboratory · University of Arizona

   Affiliation as printed

   Arizona Institutes for Resilience, University of Arizona, Tucson, AZ, USA

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

2. A. Anthony Bloom

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

3. Shuang Ma

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

4. Charles E. Miller

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

5. K. W. Bowman

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

6. Yi Yin

   California Institute of Technology

   Affiliation as printed

   Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA

7. Joannes D. Maasakkers Aachen SRON Netherlands Institute for Space Research

   Space Research Organisation Netherlands

   Affiliation as printed

   SRON Netherlands Institute for Space Research, Utrecht, Netherlands

8. Yuzhong Zhang

   Westlake University

   Affiliation as printed

   Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China

   Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang Province, China

9. Tia R. Scarpelli

   Harvard University

   Affiliation as printed

   Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

10. Zhen Qu

    Harvard University

    Affiliation as printed

    School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

11. Daniel J. Jacob

    Harvard University

    Affiliation as printed

    Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

    School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

12. John R. Worden

    Jet Propulsion Laboratory

    Affiliation as printed

    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA