Preprints
https://doi.org/10.5194/egusphere-2022-513
https://doi.org/10.5194/egusphere-2022-513
 
04 Jul 2022
04 Jul 2022
Status: this preprint is open for discussion.

Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion

Angharad C. Stell1, Michael Bertolacci2, Andrew Zammit-Mangion2, Matthew Rigby3, Paul J. Fraser4, Christina M. Harth5, Paul B. Krummel4, Xin Lan6,7, Manfredi Manizza5, Jens Mühle5, Simon O'Doherty3, Ronald G. Prinn8, Ray F. Weiss5, Dickon Young7, and Anita L. Ganesan1 Angharad C. Stell et al.
  • 1School of Geographical Sciences, University of Bristol, Bristol, UK
  • 2School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
  • 3School of Chemistry, University of Bristol, Bristol, UK
  • 4Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Australia
  • 5Scripps Institution of Oceanography, University of California, San Diego, USA
  • 6National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder, USA
  • 7University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, USA
  • 8Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, USA

Abstract. Nitrous oxide is a potent greenhouse gas and ozone depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with non-Gaussian distributions and model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both meteorology and emissions are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0° and 30° N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) TgN yr-1, comprising of 12.0 (11.2–12.8) TgN yr-1 from land and 5.2 (4.5–5.9) TgN yr-1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the Northern Hemisphere extra-tropics that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterising the uncertainties in the system, for example in the prior emission fields, is crucial to be able to derive posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through expert judgment. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.

Angharad C. Stell et al.

Status: open (until 15 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-513', Anonymous Referee #1, 21 Jul 2022 reply
  • RC2: 'Comment on egusphere-2022-513', Anonymous Referee #2, 29 Jul 2022 reply

Angharad C. Stell et al.

Data sets

Data Angharad C. Stell https://doi.org/10.17605/OSF.IO/SN539

Model code and software

Code Angharad C. Stell https://doi.org/10.17605/OSF.IO/SN539

Angharad C. Stell et al.

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Short summary
Nitrous oxide is a potent greenhouse gas and ozone depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0° and 30° N, with the highest emissions recorded in 2020.