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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 33:63:6Tgyr1, which are comprised of 19:0 2:6Tgyr1 from anthropogenic (primarily related to agriculture and waste), 13:01:9Tgyr1 from wetlands and 1:7 0:3Tgyr1 from biomass burning sources. In addition, between the 2011-2013 and 2014-2018 periods, Brazil's mean emissions rose by 6:95:3Tgyr1 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 Nino by 3:72:7Tgyr1 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.
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Acknowledgements. This work and its contributors (Rachel L. Tun-nicliffe) were supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil) and the Natural Environment Research Council (NERC) Methane Observations and Yearly Assessments programme (MOYA, NE/N016548/1). Anita L. Ganesan was funded by the NERC Independent Research Fellowship NE/L010992/1. Robert J. Parker and Hartmut Boesch were funded via the UK National Centre for Earth Observation (NCEO grant no. nceo020005).
The operation of the Ragged Point site was funded by the National Aeronautical and Space Administration (NASA, USA) (grants NAG5-12669, NNX07AE89G and NNX11AF17G to MIT; grants NAG5-4023, NNX07AE87G, NNX07AF09G, NNX11AF15G and NNX11AF16G to SIO) under the AGAGE programme and the National Oceanic and Atmospheric Administration (NOAA, USA) (contract RA-133R-15-CN-0008 to the University of Bristol). ATTO data were supported by the Max Planck Society (MPG), the German Federal Ministry of Education and Research (contracts 01LB1001A and 01LK1602A) and the Brazilian Ministério da Ciência, Tecnologia e Inovação (MCTI/FINEP contract 01.11.01248.00) as well as the Amazon State University (UEA) (FAPEAM, LBA/INPA and SDS/CEUC/RDS-Uatumã). We acknowledge the Swiss Federal Office for Meteorology and Climatology (MeteoSwiss) for providing access to ECMWF ERA-Interim reanalysis products for use with the FLEXPART model. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol – http: //www.bristol.ac.uk/acrc/ (last access: 22 October 2010). GOSAT retrievals used the ALICE high-performance computing facility at the University of Leicester.
© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
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