Inverse estimation of sector-level methane emissions using observations of secondary trace gases

  • Alice E Ramsden

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


Methane is a key greenhouse gas with a range of anthropogenic and natural sources. Atmospheric concentrations are currently rising at ever increasing rates and methane is continuing to be an important contributor to global climate change. The causes of this rise in global atmospheric methane concentration are still under debate and the uncertainties involved with attributing methane emissions to their source complicate this process. Improved attribution methods are required to improve our understanding of trends in methane emissions and to target emissions mitigation policies where they will have the greatest impact.
This thesis presents a new inverse modelling method for studying sector-level methane emissions that uses observations of a secondary trace gas and its emission ratio relative to methane to aid with source attribution. The method is tested using observations of ethane to estimate methane emissions from fossil fuel (FF) and non-fossil fuel (non-FF ) sources. This inverse model was used to estimate UK methane emissions using observations from a network of tall tower observation sites and to estimate emissions from two fossil fuel production regions in the US using high frequency aircraft observations. Both studies found that using ethane observations reduces posterior FF methane flux uncertainty by between 15 and 40%, relative to results from a more standard methane-only inverse model.
This novel inverse model was then tested with methane isotope observations as an alternative secondary trace gas. Initial tests using this method were less effective at correctly attributing emissions to their source than the ethane method, and results from these tests suggest that methane isotope observations may not include enough information about regional emissions to provide additional constraint on sector-level methane emissions. To aid with solving this issue, potential alternative methods and future work involving the use of methane isotope observations with this inverse model are presented and discussed.
Date of Award6 Dec 2022
Original languageEnglish
Awarding Institution
  • University of Bristol
SupervisorAnita L Ganesan (Supervisor) & Joanna Isobel House (Supervisor)

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