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

• Published in: Atmospheric chemistry and physics Vol. 19; no. 23; pp. 14721 - 14740
• Main Authors: Lunt, Mark F, Palmer, Paul I, Feng, Liang, Taylor, Christopher M, Boesch, Hartmut, Parker, Robert J
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 11.12.2019; Copernicus Publications
• Subjects: Air pollution; Altimetry; Anomalies; Atmospheric chemistry; Bayesian analysis; Budgets; Carbon dioxide; Climate change; Data; Ecosystems; Emissions; Environmental aspects; Gases; Greenhouse effect; Greenhouse gases; Groundwater; Groundwater storage; Growth rate; In situ measurement; Inflow; International marketing; Land surface temperature; Methane; Methane emissions; Northern Hemisphere; Organic chemistry; Physics; Probability theory; Proxy; Satellite data; Satellite observation; Satellites; Seasonal variations; Seasonality; Southern Hemisphere; Surface temperature; Systematic errors; Temperature anomalies; Temporal variability; Temporal variations; Trends; Tropical climate; Uncertainty; Water storage; Wetlands; Africa; Northern Hemisphere
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-19-14721-2019

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 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending 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 7-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, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. 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 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. 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.