Abstract
Abstract. The atmospheric methane (CH4) growth rate has varied considerably in recent decades. Unexplained renewed growth after 2006 followed 7 years of stagnation and coincided with an isotopic trend toward CH4 more depleted in 13C, suggesting changes in sources and/or sinks. Using surface observations of both CH4 and the relative change of isotopologue ratio (δ13CH4) to constrain a global 3-D chemical transport model (CTM), we have performed a synthesis inversion for source and sink attribution. Our method extends on previous studies by providing monthly and regional attribution of emissions from six different sectors and changes in atmospheric sinks for the extended 2003–2015 period. Regional evaluation of the model CH4 tracer with independent column observations from the Greenhouse Gases Observing Satellite (GOSAT) shows improved performance when using posterior fluxes (R=0.94–0.96, RMSE =8.3–16.5 ppb), relative to prior fluxes (R=0.60–0.92, RMSE =48.6–64.6 ppb). Further independent validation with data from the Total Carbon Column Observing Network (TCCON) shows a similar improvement in the posterior fluxes (R=0.87, RMSE =18.8 ppb) compared to the prior fluxes (R=0.69, RMSE =55.9 ppb). Based on these improved posterior fluxes, the inversion results suggest the most likely cause of the renewed methane growth is a post-2007 1.8±0.4 % decrease in mean OH, a 12.9±2.7 % increase in energy sector emissions, mainly from Africa–Middle East and southern Asia–Oceania, and a 2.6±1.8 % increase in wetland emissions, mainly from northern Eurasia. The posterior wetland flux increases are in general agreement with bottom-up estimates, but the energy sector growth is greater than estimated by bottom-up methods. The model results are consistent across a range of sensitivity analyses. When forced to assume a constant (annually repeating) OH distribution, the inversion requires a greater increase in energy sector (13.6±2.7 %) and wetland (3.6±1.8 %) emissions and an 11.5±3.8 % decrease in biomass burning emissions. Assuming no prior trend in sources and sinks slightly reduces the posterior growth rate in energy sector and wetland emissions and further increases the magnitude of the negative OH trend. We find that possible tropospheric Cl variations do not influence δ13CH4 and CH4 trends, although we suggest further work on Cl variability is required to fully diagnose this contribution. While the study provides quantitative insight into possible emissions variations which may explain the observed trends, uncertainty in prior source and sink estimates and a paucity of δ13CH4 observations limit the robustness of the posterior estimates.
Highlights
The atmospheric concentration of methane (CH4) has been increasing globally since 2007, following a slowdown in growth from 1999 to 2006 (Dlugokencky et al, 2017)
Inversion results constrained by CH4 and δ13CH4 observations (INV-FULL) show, as expected, improved seasonal and interannual monthly averaged posterior CH4 and δ13CH4 estimates when compared with assimilated surface observations (Fig. 1)
The bias in the prior, relative to both the posterior and observations, grows throughout the simulation period. This results in a large bias at the end of the time period, which is evident in the large root-mean-square error (RMSE) values (Figs. 1 to 4)
Summary
The atmospheric concentration of methane (CH4) has been increasing globally since 2007, following a slowdown in growth from 1999 to 2006 (Dlugokencky et al, 2017). Nisbet et al (2014, 2016) and Schaefer et al (2016) suggested that either increased wetland or agricultural emissions were the likely cause, while Rigby et al (2017) and Turner et al (2017) found the most likely explanation to be a decreased global mean OH concentration The latter two studies emphasised that the problem is not very well constrained by existing data and as a result could not discard the hypothesis that OH is not changing. The synthesis inversion technique uses the forward 3-D CTM to optimise monthly CH4 emissions over relatively large regions and for multiple source sectors This spatial resolution is not present in existing box model inversions. From this we derive possible source and sink changes between 2003 and 2015 which best fit the observations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
