Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints

Abstract. We investigate the causes of the renewed growth of atmospheric methane (CH4) amount fractions after 2007 by using variational inverse modeling with a three-dimensional chemistry-transport model. Together with CH4 amount fraction data, we use the additional information provided by observations of CH4 isotopic compositions (13C : 12C and D : H) to better differentiate between the emission categories compared to the differentiation achieved by assimilating CH4 amount fractions alone. Our system allows us to optimize either the CH4 emissions only or both the emissions and the source isotopic signatures (δsource(13C,CH4) and δsource(D,CH4)) of five emission categories. Consequently, we also assess, for the first time, the influence of applying random errors to both emissions and source signatures in an inversion framework. As the computational cost of a single inversion is high at present, the methodology applied to prescribe source signature uncertainties is simple, so it can serve as a basis for future work. Here, we investigate the post-2007 increase in atmospheric CH4 using the differences between 2002–2007 and 2007–2014. When random uncertainties in source isotopic signatures are accounted for, our results suggest that the post-2007 increase (here defined using the two periods 2002–2007 and 2007–2014) in atmospheric CH4 was caused by increases in emissions from (1) fossil sources (51 % of the net increase in emissions) and (2) agriculture and waste sources (49 %), which were slightly compensated for by a small decrease in biofuel- and biomass-burning emissions. The conclusions are very similar when assimilating CH4 amount fractions alone, suggesting either that random uncertainties in source signatures are too large at present to impose any additional constraint on the inversion problem or that we overestimate these uncertainties in our setups. On the other hand, if the source isotopic signatures are considered to be perfectly known (i.e., ignoring their uncertainties), the relative contributions of the different emission categories are significantly changed. Compared to the inversion where random uncertainties are accounted for, fossil emissions and biofuel- and biomass-burning emissions are increased by 24 % and 41 %, respectively, on average over 2002–2014. Wetland emissions and agricultural and waste emissions are decreased by 14 % and 7 %, respectively. Also, in this case, our results suggest that the increase in CH4 amount fractions after 2007 (despite a large decrease in biofuel- and biomass-burning emissions) was caused by increases in emissions from (1) fossil fuels (46 %), (2) agriculture and waste (37 %), and (3) wetlands (17 %). Additionally, some other sensitivity tests have been performed. While the prescribed interannual variability in OH can have a large impact on the results, assimilating δ(D,CH4) observations in addition to the other constraints has only a minor influence. Using all the information derived from these tests, the net increase in emissions is still primarily attributed to fossil sources (50 ± 3 %) and agriculture and waste sources (47 ± 5 %). Although our methods have room for improvement, these results illustrate the full capacity of our inversion framework, which can be used to consistently account for random uncertainties in both emissions and source signatures.