Estimating emissions of methane consistent with atmospneric measurements of methane and δ13C of methane

We have constructed an atmospheric inversion framework based on TM5-4DVAR to jointly assimilate measurements of methane and delta C-13 of methane in order to estimate source-specific methane emissions. Here we present global emission estimates from this framework for the period 1999-2016. We assimilate a newly constructed, multi-agency database of CH4 and delta C-13 measurements. We find that traditional CH4-only atmospheric inversions are unlikely to estimate emissions consistent with atmospheric delta C-13 data, and assimilating delta C-13 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 degrees N and 23.5 degrees 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 delta C-13 data. We find that at global and continental scales, delta C-13 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 delta C-13 measurement coverage. Finally, we find that the largest uncertainty in using delta C-13 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.

Automated summary

This study constructed an atmospheric inversion framework based on TM5-4DVAR to estimate global methane emissions for the period 1999-2016 by assimilating measurements of methane and delta C-13 of methane. It was found that traditional atmospheric inversions using CH4 data alone were unlikely to provide emission estimates consistent with delta C-13 data, highlighting the importance of assimilating delta C-13 data in emission estimations.