Preprints
https://doi.org/10.5194/acp-2021-548
https://doi.org/10.5194/acp-2021-548

  06 Jul 2021

06 Jul 2021

Review status: this preprint is currently under review for the journal ACP.

Atmospheric observations consistent with reported decline in the UK's methane emissions, 2013–2020

Mark Lunt1, Alistair Manning2, Grant Allen3, Tim Arnold4,1, Stéphane Bauguitte5, Hartmut Boesch6, Anita Ganesan7, Aoife Grant8, Carole Helfter9, Eiko Nemitz9, Simon O'Doherty8, Paul Palmer1,10, Joseph Pitt3,a, Chris Rennick4, Daniel Say8, Kieran Stanley11, Ann Stavert12, Dickon Young8, and Matt Rigby8 Mark Lunt et al.
  • 1School of GeoSciences, University of Edinburgh, Edinburgh, UK
  • 2Hadley Centre, UK Met Office, Exeter, UK
  • 3Department of Earth and Environmental Sciences, University of Manchester, Manchester, UK
  • 4National Physical Laboratory, Teddington, UK
  • 5FAAM Airborne Laboratory, National Centre for Atmospheric Sciences, Building 146, College Road, Cranfield, UK
  • 6National Centre for Earth Observation (NCEO), University of Leicester, Leicester, UK
  • 7School of Geographical Sciences, University of Bristol, Bristol, UK
  • 8School of Chemistry, University of Bristol, Bristol, UK
  • 9Centre for Ecology and Hydrology, Penicuik , UK
  • 10NCEO, University of Edinburgh, Edinburgh, UK
  • 11Institute for Atmospheric and Environmental Science, Goethe University Frankfurt, Frankfurt, Germany
  • 12CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia
  • anow at: School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA

Abstract. Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using eight years of continuous methane (CH4) concentration data, this work assesses the United Kingdom's (UK) CH4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 Tg yr−1 and 2.12 ± 0.26 Tg yr−1 between 2013–2020, and an overall trend of −0.05 ± 0.01 Tg yr−2 and −0.06 ± 0.04 Tg yr−2, a 2–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr−1 and trend of −0.03 Tg yr−2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013–2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method, and the atmospheric transport model used. We find the decreasing trend to be due primarily to a reduction of emissions from England, which accounts for 70 % of the UK CH4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion setups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend, and is not necessarily a response to national lockdowns.

Mark Lunt et al.

Status: open (until 20 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Mark Lunt et al.

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Short summary
We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling setup on these findings are explored through a number of sensitivity studies.
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