@Article{BasuLaDlMiScMiGa:2022:EsEmMe,
               author = "Basu, Sourish and Lan, Xin and Dlugokencky, Ed and Michel, Sylvia 
                         and Schwletzke, Stefan and Miller, John B. and Gatti, Luciana 
                         Vanni",
          affiliation = "{} and {} and {} and {} and {} and {} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Estimating Emissions of Methane Consistent with Atmospheric 
                         Measurements of Methane and \δ13C of Methane",
              journal = "Atmospheric Chemistry and Physics",
                 year = "2022",
               volume = "22",
                pages = "15351--15377",
             keywords = "metano, Balan{\c{c}}o Global.",
             abstract = "We have constructed an atmospheric inversion framework based on 
                         TM5-4DVAR to jointly assimilate measurements of methane and 
                         \δ 13C of methane in order to estimate source-specific 
                         methane emissions. Here we present global emission estimates from 
                         this framework for the period 19992016. We assimilate a newly 
                         constructed, multi-agency database of CH4 and \δ 13C 
                         measurements. We find that traditional CH4-only atmospheric 
                         inversions are unlikely to estimate emissions consistent with 
                         atmospheric \δ 13C data, and assimilating \δ 13C data 
                         is necessary to derive emissions consistent with both 
                         measurements. Our framework attributes ca. 85 % of the post-2007 
                         growth in atmospheric methane to microbial sources, with about 
                         half of that coming from the tropics between 23.5\◦ N and 
                         23.5\◦ S. This contradicts the attribution of the recent 
                         growth in the methane budget of the Global Carbon Project (GCP). 
                         We find that the GCP attribution is only consistent with our 
                         top-down estimate in the absence of \δ 13C data. We find 
                         that at global and continental scales, \δ 13C data can 
                         separate microbial from fossil methane emissions much better than 
                         CH4 data alone, and at smaller scales this ability is limited by 
                         the current \δ 13C measurement coverage. Finally, we find 
                         that the largest uncertainty in using \δ 13C data to 
                         separate different methane source types comes from our knowledge 
                         of atmospheric chemistry, specifically the distribution of 
                         tropospheric chlorine and the isotopic discrimination of the 
                         methane sink.",
                  doi = "10.5194/acp-22-15351-2022",
                  url = "http://dx.doi.org/10.5194/acp-22-15351-2022",
                 issn = "1680-7316 and 1680-7324",
                label = "lattes: 6983900937588878 7 BasuLaDlMiScMiGa:2022:EsEmMe",
             language = "en",
           targetfile = "acp-22-15351-2022.pdf",
                  url = "https://acp.copernicus.org/articles/22/15351/2022/acp-22-15351-2022.pdf",
        urlaccessdate = "06 jun. 2024"
}