Comment on egusphere-2023-946

Abstract

Abstract. We quantify 2019 methane emissions in the contiguous U.S. (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the U.S. Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a-1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find livestock emissions of 10.4 (10.0–10.7) Tg a-1, oil and gas of 10.4 (10.1–10.7) Tg a-1, coal of 1.5 (1.2–1.9) Tg a-1, landfills of 6.9 (6.4–7.5) Tg a-1, wastewater of 0.6 (0.5–0.7), and other anthropogenic sources of 1.1 (1.0–1.2) Tg a-1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 73 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI’s new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 34 % larger than the 2022 GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a-1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.

Authors 15

1. Hannah O. Nesser corresponding

   Harvard University

   Affiliation as printed

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

2. Daniel J. Jacob

   Harvard University

   Affiliation as printed

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

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

   Space Research Organisation Netherlands

   Affiliation as printed

   SRON Netherlands Institute for Space Research, Leiden, the Netherlands

4. Alba Lorente Aachen SRON Netherlands Institute for Space Research

   Space Research Organisation Netherlands

   Affiliation as printed

   SRON Netherlands Institute for Space Research, Leiden, the Netherlands

5. Zichong Chen

   Harvard University

   Affiliation as printed

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

6. Xiao Hui Lu

   Sun Yat-sen University

   Affiliation as printed

   School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong Province, China

7. Lu Shen

   Peking University

   Affiliation as printed

   Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

8. Zhen Qu

   North Carolina State University

   Affiliation as printed

   Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA

9. Melissa Payer Sulprizio

   Harvard University

   Affiliation as printed

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

10. Margaux Winter

    Harvard University

    Affiliation as printed

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

11. Shuang Ma

    Jet Propulsion Laboratory

    Affiliation as printed

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

12. A. Anthony Bloom

    Jet Propulsion Laboratory

    Affiliation as printed

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

13. John R. Worden

    Jet Propulsion Laboratory

    Affiliation as printed

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

14. Robert N. Stavins

    Harvard University

    Affiliation as printed

    Harvard Kennedy School, Cambridge, MA, USA

15. Cynthia A. Randles

    ExxonMobil (United States)

    Affiliation as printed

    ExxonMobil Technology and Engineering Company, Annandale, NJ, USA

    now at: United Nations Environment Program International Methane Emissions Observatory, Paris, France