Estimates of sub-national methane emissions from inversion modelling

Abstract. Methane is a strong contributor to global climate change, yet our current understanding and quantification of its sources and their variability is incomplete. There is a growing need for comparisons between emission estimates produced using bottom-up inventory approaches and top-down inversion techniques based on atmospheric measurements, especially at higher spatial resolutions. To meet this need, this study presents using an inversion approach based on the Inversion Technique for Emissions Modelling (InTEM) framework and measurements from four sites in East Anglia, United Kingdom. Atmospheric methane concentrations were recorded at 1–2 minute time-steps at each location within the region of interest. These observations, coupled with the UK Met Office's Lagrangian particle dispersion model, NAME (Numerical Atmospheric dispersion Modelling Environment), were used within InTEM2014 to produce methane emission estimates for a 1-year period (June 2013–May 2014) in this eastern region of the UK (~ 100 × 150 km) at high spatial resolution (up to 4 × 4 km). InTEM2014 was able to produce realistic emissions estimates for East Anglia, and highlighted potential areas of difference from the UK National Atmospheric Emissions Inventory (NAEI). As this study was part of the UK Greenhouse gAs Uk and Global Emissions (GAUGE) project, observations were included within a national inversion using all eleven measurement sites across the UK to directly compare emission estimates for the East Anglia Region. Results show similar methane estimates for the East Anglia region. Methane emissions from Norfolk and Suffolk show good agreement with the estimates in NAEI, with differences of ~ 5 %. Larger differences are found for Cambridgeshire where our estimate is 22.5 % lower than that of NAEI. The addition of the EA sites within the national inversion system enabled finer spatial resolution and a decrease in the associated uncertainty for that area. Further development of our approach to include a more robust analysis of the methane concentration in the air entering this region and the uncertainty associated with the resulting emissions would strengthen this inverse method. Nonetheless, our results show there is value in high spatial resolution measurement networks and the resulting inversion emission estimates.