Articles | Volume 9, issue 2
Earth Syst. Sci. Data, 9, 639–656, 2017
https://doi.org/10.5194/essd-9-639-2017
Earth Syst. Sci. Data, 9, 639–656, 2017
https://doi.org/10.5194/essd-9-639-2017

Review article 31 Aug 2017

Review article | 31 Aug 2017

Global Inventory of Gas Geochemistry Data from Fossil Fuel, Microbial and Burning Sources, version 2017

Owen A. Sherwood et al.

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Cited articles

Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C., Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.: Atmospheric CH4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements, J. Geophys. Res.-Atmos., 118, 7350–7369, https://doi.org/10.1002/jgrd.50480, 2013.
Bernard, B. B.: Light hydrocarbons in recent marine sediments: PhD thesis, Texas A&M University, College Station, Texas, USA, 144 pp. 1978.
Bloom, A. A., Palmer, P. I., Fraser, A., Reay, D. S., and Frankenberg, C.: Large-scale controls of methanogenesis inferred from methane and gravity spaceborne data, Science, 327, 322–325, https://doi.org/10.1126/science.1175176, 2010.
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Multiple natural and anthropogenic emissions sources contribute to the global atmospheric methane budget. Methane emissions are constrained, in part, by inverse (top-down) models that incorporate data on the concentration and stable carbon and hydrogen isotopic ratios of methane from different sources. To aid in these modeling efforts, we present a geochemical database comprising over 10 000 discrete samples from fossil and non-fossil fuel sources of methane.