Anthropogenic emissions of methane in the United States

Edited by Mark H. Thiemens, University of California, San Diego, La Jolla, CA, and approved October 18, 2013 (received for review August 5, 2013)
November 25, 2013
110 (50) 20018-20022
Letter
Livestock methane emissions in the United States
Alexander N. Hristov, Kristen A. Johnson, Ermias Kebreab
Letter
Reply to Hristov et al.: Linking methane emissions inventories with atmospheric observations
Scot M. Miller, Anna M. Michalak, Steven C. Wofsy

Significance

Successful regulation of greenhouse gas emissions requires knowledge of current methane emission sources. Existing state regulations in California and Massachusetts require ∼15% greenhouse gas emissions reductions from current levels by 2020. However, government estimates for total US methane emissions may be biased by 50%, and estimates of individual source sectors are even more uncertain. This study uses atmospheric methane observations to reduce this level of uncertainty. We find greenhouse gas emissions from agriculture and fossil fuel extraction and processing (i.e., oil and/or natural gas) are likely a factor of two or greater than cited in existing studies. Effective national and state greenhouse gas reduction strategies may be difficult to develop without appropriate estimates of methane emissions from these source sectors.

Abstract

This study quantitatively estimates the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Results show that current inventories from the US Environmental Protection Agency (EPA) and the Emissions Database for Global Atmospheric Research underestimate methane emissions nationally by a factor of ∼1.5 and ∼1.7, respectively. Our study indicates that emissions due to ruminants and manure are up to twice the magnitude of existing inventories. In addition, the discrepancy in methane source estimates is particularly pronounced in the south-central United States, where we find total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. The spatial patterns of our emission fluxes and observed methane–propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13%) in the south-central United States. This result suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global methane inventory. These results cast doubt on the US EPA’s recent decision to downscale its estimate of national natural gas emissions by 25–30%. Overall, we conclude that methane emissions associated with both the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.

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Acknowledgments

For advice and support, we thank Roisin Commane, Elaine Gottlieb, and Matthew Hayek (Harvard University); Robert Harriss (Environmental Defense Fund); Hanqin Tian and Bowen Zhang (Auburn University); Jed Kaplan (Ecole Polytechnique Fédérale de Lausanne); Kimberly Mueller and Christopher Weber (Institute for Defense Analyses Science and Technology Policy Institute); Nadia Oussayef; and Gregory Berger. In addition, we thank the National Aeronautics and Space Administration (NASA) Advanced Supercomputing Division for computing help; P. Lang, K. Sours, and C. Siso for analysis of National Oceanic and Atmospheric Administration (NOAA) flasks; and B. Hall for calibration standards work. This work was supported by the American Meteorological Society Graduate Student Fellowship/Department of Energy (DOE) Atmospheric Radiation Measurement Program, a DOE Computational Science Graduate Fellowship, and the National Science Foundation Graduate Research Fellowship Program. NOAA measurements were funded in part by the Atmospheric Composition and Climate Program and the Carbon Cycle Program of NOAA’s Climate Program Office. Support for this research was provided by NASA Grants NNX08AR47G and NNX11AG47G, NOAA Grants NA09OAR4310122 and NA11OAR4310158, National Science Foundaton (NSF) Grant ATM-0628575, and Environmental Defense Fund Grant 0146-10100 (to Harvard University). Measurements at Walnut Grove were supported in part by a California Energy Commission Public Interest Environmental Research Program grant to Lawrence Berkeley National Laboratory through the US Department of Energy under Contract DE-AC02-05CH11231. DOE flights were supported by the Office of Biological and Environmental Research of the US Department of Energy under Contract DE-AC02-05CH11231 as part of the Atmospheric Radiation Measurement Program (ARM), ARM Aerial Facility, and Terrestrial Ecosystem Science Program. Weather Research and Forecasting–Stochastic Time-Inverted Lagrangian Transport model development at Atmospheric and Environmental Research has been funded by NSF Grant ATM-0836153, NASA, NOAA, and the US intelligence community.

Supporting Information

Supporting Information (PDF)
Supporting Information

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 110 | No. 50
December 10, 2013
PubMed: 24277804

Classifications

Submission history

Published online: November 25, 2013
Published in issue: December 10, 2013

Keywords

  1. climate change policy
  2. geostatistical inverse modeling

Acknowledgments

For advice and support, we thank Roisin Commane, Elaine Gottlieb, and Matthew Hayek (Harvard University); Robert Harriss (Environmental Defense Fund); Hanqin Tian and Bowen Zhang (Auburn University); Jed Kaplan (Ecole Polytechnique Fédérale de Lausanne); Kimberly Mueller and Christopher Weber (Institute for Defense Analyses Science and Technology Policy Institute); Nadia Oussayef; and Gregory Berger. In addition, we thank the National Aeronautics and Space Administration (NASA) Advanced Supercomputing Division for computing help; P. Lang, K. Sours, and C. Siso for analysis of National Oceanic and Atmospheric Administration (NOAA) flasks; and B. Hall for calibration standards work. This work was supported by the American Meteorological Society Graduate Student Fellowship/Department of Energy (DOE) Atmospheric Radiation Measurement Program, a DOE Computational Science Graduate Fellowship, and the National Science Foundation Graduate Research Fellowship Program. NOAA measurements were funded in part by the Atmospheric Composition and Climate Program and the Carbon Cycle Program of NOAA’s Climate Program Office. Support for this research was provided by NASA Grants NNX08AR47G and NNX11AG47G, NOAA Grants NA09OAR4310122 and NA11OAR4310158, National Science Foundaton (NSF) Grant ATM-0628575, and Environmental Defense Fund Grant 0146-10100 (to Harvard University). Measurements at Walnut Grove were supported in part by a California Energy Commission Public Interest Environmental Research Program grant to Lawrence Berkeley National Laboratory through the US Department of Energy under Contract DE-AC02-05CH11231. DOE flights were supported by the Office of Biological and Environmental Research of the US Department of Energy under Contract DE-AC02-05CH11231 as part of the Atmospheric Radiation Measurement Program (ARM), ARM Aerial Facility, and Terrestrial Ecosystem Science Program. Weather Research and Forecasting–Stochastic Time-Inverted Lagrangian Transport model development at Atmospheric and Environmental Research has been funded by NSF Grant ATM-0836153, NASA, NOAA, and the US intelligence community.

Notes

This article is a PNAS Direct Submission.

Authors

Affiliations

Scot M. Miller1 [email protected]
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138;
Steven C. Wofsy
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138;
Anna M. Michalak
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305;
Eric A. Kort
Department of Atmospheric, Ocean, and Space Sciences, University of Michigan, Ann Arbor, MI 48109;
Arlyn E. Andrews
Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305;
Sebastien C. Biraud
Earth Sciences Division, and
Edward J. Dlugokencky
Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305;
Janusz Eluszkiewicz
Atmospheric and Environmental Research, Lexington, MA 02421;
Marc L. Fischer
Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720;
Greet Janssens-Maenhout
Institute for Environment and Sustainability, European Commission Joint Research Centre, 21027 Ispra, Italy; and
Ben R. Miller
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309
John B. Miller
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309
Stephen A. Montzka
Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305;
Thomas Nehrkorn
Atmospheric and Environmental Research, Lexington, MA 02421;
Colm Sweeney
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309

Notes

1
To whom correspondence should be addressed. E-mail: [email protected].
Author contributions: S.M.M., S.C.W., and A.M.M. designed research; S.M.M., A.E.A., S.C.B., E.J.D., J.E., M.L.F., G.J.-M., B.R.M., J.B.M., S.A.M., T.N., and C.S. performed research; S.M.M. analyzed data; S.M.M., S.C.W., A.M.M., and E.A.K. wrote the paper; A.E.A., S.C.B., E.J.D., M.L.F., B.R.M., J.B.M., S.A.M., and C.S. collected atmospheric methane data; and J.E. and T.N. developed meteorological simulations using the Weather Research and Forecasting model.

Competing Interests

The authors declare no conflict of interest.

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    Anthropogenic emissions of methane in the United States
    Proceedings of the National Academy of Sciences
    • Vol. 110
    • No. 50
    • pp. 19971-20345

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