Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion
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acp-22-12945-2022.pdf
Description
Published version
Size
5.26 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Stell, Angharad C
Bertolacci, Michael
Zammit-Mangion, Andrew
Rigby, Matthew
Fraser, Paul J
Harth, Christina M
Krummel, Paul B
Lan, Xin
Manizza, Manfredi
Mühle, Jens
Date Issued
2022
Journal
Atmospheric Chemistry and Physics
Publisher
Copernicus GmbH
Citation
Stell, Angharad C, Bertolacci, Michael, Zammit-Mangion, Andrew, Rigby, Matthew, Fraser, Paul J et al. 2022. "Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion." Atmospheric Chemistry and Physics, 22 (19).
Version
Final published version
Abstract
Abstract. Nitrous oxide is a potent greenhouse gas (GHG) 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 truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability 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) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N 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 extra-tropical Northern Hemisphere 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 characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving 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 judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.5194/ACP-22-12945-2022
