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

Emissions of methane (<span classCombining double low line"inline-formula">CH4</span>) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global <span classCombining double low line"inline-formula">CH4</span> 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 <span classCombining double low line"inline-formula">CH4</span> columns can help to narrow down some of the uncertainties in the tropical <span classCombining double low line"inline-formula">CH4</span> emission budget. We use proxy column retrievals of atmospheric <span classCombining double low line"inline-formula">CH4</span> (<span classCombining double low line"inline-formula">XCH4</span>) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (<span classCombining double low line"inline-formula"><math xmlnsCombining double low line"http://www.w3.org/1998/Math/MathML" idCombining double low line"M7" displayCombining double low line"inline" overflowCombining double low line"scroll" dspmathCombining double low line"mathml"><mrow><mn mathvariantCombining double low line"normal">0.5</mn><msup><mi/><mo>ĝ</mo></msup><mspace widthCombining double low line"0.125em" linebreakCombining double low line"nobreak"/><mo>×</mo><mspace linebreakCombining double low line"nobreak" widthCombining double low line"0.125em"/><mn mathvariantCombining double low line"normal">0.625</mn><msup><mi/><mo>ĝ</mo></msup></mrow></math><span><svg:svg xmlns:svgCombining double low line"http://www.w3.org/2000/svg" widthCombining double low line"67pt" heightCombining double low line"11pt" classCombining double low line"svg-formula" dspmathCombining double low line"mathimg" md5hashCombining double low line"94db5c4ea3c5edbcf2cb8e6a20a0a27f"><svg:image xmlns:xlinkCombining double low line"http://www.w3.org/1999/xlink" xlink:hrefCombining double low line"acp-19-14721-2019-ie00001.svg" widthCombining double low line"67pt" heightCombining double low line"11pt" srcCombining double low line"acp-19-14721-2019-ie00001.png"/></svg:svg></span></span>) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of <span classCombining double low line"inline-formula">XCH4</span> 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 (<span classCombining double low line"inline-formula">CO2</span>) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different <span classCombining double low line"inline-formula">XCH4</span> data products that are determined using different model <span classCombining double low line"inline-formula">CO2</span> fields. We infer monthly emissions from GOSAT <span classCombining double low line"inline-formula">XCH4</span> 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)&thinsp;Tg&thinsp;yr<span classCombining double low line"inline-formula">-1</span>, depending on the proxy <span classCombining double low line"inline-formula">XCH4</span> data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African <span classCombining double low line"inline-formula">CH4</span> emissions for our 7-year study period, with values of 1.5 (1.1-1.9)&thinsp;Tg&thinsp;yr<span classCombining double low line"inline-formula">-1</span> or 2.1 (1.7-2.5)&thinsp;Tg&thinsp;yr<span classCombining double low line"inline-formula">-1</span>, depending on the <span classCombining double low line"inline-formula">CO2</span> 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&thinsp;Tg&thinsp;yr<span classCombining double low line"inline-formula">-1</span> between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in <span classCombining double low line"inline-formula">CH4</span> 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 <span classCombining double low line"inline-formula">CH4</span> 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.