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Discussion papers
https://doi.org/10.5194/acp-2019-477
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-477
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Jun 2019

Submitted as: research article | 26 Jun 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

Mark F. Lunt1, Paul I. Palmer1,2, Liang Feng1,2, Christopher M. Taylor3,4, Hartmut Boesch5,6, and Robert J. Parker5,6 Mark F. Lunt et al.
  • 1School of GeoSciences, University of Edinburgh, Edinburgh, UK
  • 2National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
  • 3Centre for Ecology and Hydrology, Wallingford, UK
  • 4National Centre for Earth Observation, Wallingford, UK
  • 5Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
  • 6National Centre for Earth Observation, University of Leicester, Leicester, UK

Abstract. Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5 × 0.625) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010–2016 range from 75 (72–78) Tg yr−1 to 80 (78–83) Tg yr−1, dependent on the proxy XCH4 data used, with larger differences in northern hemisphere Africa than southern hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our seven-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, dependent on the CO2 data product used in the proxy retrieval. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data we find this increase in CH4 emission is consistent with an increase in wetland extent due to increased inflow from the White Nile. We find a strong seasonality in emissions across northern hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

Mark F. Lunt et al.
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Short summary
Using data from the GOSAT satellite between 2010-2016 and a Bayesian inversion approach, we estimate monthly emissions of methane from tropical Africa. We find an increase in methane emissions during this period, driven in part by rising emissions from South Sudan. Using ancillary data we attribute this short-term emissions rise to an increase in the extent of the Sudd wetlands driven by increased outflow from the East African lakes.
Using data from the GOSAT satellite between 2010-2016 and a Bayesian inversion approach, we...
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