Comment on acp-2021-955

Abstract

Abstract. We use optimal estimation (OE) to quantify methane fluxes based on total column CH4 data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are 180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.

Authors 13

1. John R. Worden

   Jet Propulsion Laboratory

   Affiliation as printed

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

2. Daniel Cusworth

   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

3. Zhen Qu

   Harvard University

   Affiliation as printed

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

4. Yi Yin

   California Institute of Technology

   Affiliation as printed

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

5. Yuzhong Zhang

   Harvard University · Westlake University

   Affiliation as printed

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

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

6. A. Anthony Bloom

   Jet Propulsion Laboratory

   Affiliation as printed

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

7. Shuang Ma

   Jet Propulsion Laboratory

   Affiliation as printed

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

8. Brendan Keith Aidan Byrne

   Jet Propulsion Laboratory

   Affiliation as printed

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

9. Tia R. Scarpelli

   Harvard University

   Affiliation as printed

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

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

    Space Research Organisation Netherlands

    Affiliation as printed

    SRON Netherlands Institute for Space Research, Utrecht, the Netherlands

11. David Crisp

    Jet Propulsion Laboratory

    Affiliation as printed

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

12. Riley Duren

    University of Arizona

    Affiliation as printed

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

13. Daniel J. Jacob

    Harvard University

    Affiliation as printed

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