Quantifying sources of Brazil's methane emissions between 2010 and 2018 from satellite data

Rachel L Tunnicliffe*, Anita L Ganesan, Robert J Parker, Hartmut Boesch, Nicola Gedney, Benjamin Poulter, Zhen Zhang, Jost Lavric, David Walter, Matthew L Rigby, Stephan Henne, T D S Young, Simon O'Doherty

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

Brazil's CH4 emissions over the period 2010–2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010–2018 are 33.6±3.6Tgyr−1, which are comprised of 19.0±2.6Tgyr−1 from anthropogenic (primarily related to agriculture and waste), 13.0±1.9Tgyr−1 from wetlands and 1.7±0.3Tgyr−1 from biomass burning sources. In addition, between the 2011–2013 and 2014–2018 periods, Brazil's mean emissions rose by 6.9±5.3Tgyr−1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015–2016 El Niño by 3.7±2.7Tgyr−1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.
Original languageEnglish
Pages (from-to)13041–13067
Number of pages27
JournalAtmospheric Chemistry and Physics
Volume20
Issue number21
DOIs
Publication statusPublished - 7 Nov 2020

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