Preprints
https://doi.org/10.5194/acp-2022-423
https://doi.org/10.5194/acp-2022-423
 
26 Jul 2022
26 Jul 2022
Status: this preprint is currently under review for the journal ACP.

Technical Note: Unsupervised classification of ozone profiles in UKESM1

Fouzia Fahrin1,2, Daniel C. Jones3, Yan Wu2, James Keeble4,5, and Alexander T. Archibald4,5 Fouzia Fahrin et al.
  • 1Department of Geological and Atmospheric Sciences, Iowa State University, USA
  • 2Department of Mathematical Sciences, Georgia Southern University, USA
  • 3British Antarctic Survey, NERC, UKRI, Cambridge, UK
  • 4Department of Chemistry, University of Cambridge, Cambridge, UK
  • 5National Centre for Atmospheric Science (NCAS), University of Cambridge, Cambridge, UK

Abstract. The vertical distribution of ozone in the atmosphere, which features complex spatial and temporal variability set by a balance of production, loss, and advection, is relevant for both surface air pollution and for climate via its role in radiative forcing. At present, the way in which regions of coherent ozone structure are defined relies on somewhat arbitrarily drawn boundaries. Here we consider a more general, data-driven method for defining coherent regimes of ozone structure; we apply an unsupervised classification technique called Gaussian Mixture Modelling (GMM), which represents the underlying distribution of ozone profiles as a linear combination of multi-dimensional Gaussian functions. In doing so, GMM identifies coherent groups or sub-populations of the ozone profile distribution. As a proof-of-concept study, we apply GMM to ozone profiles from three subsets of the UKESM1 coupled climate model runs carried out for CMIP6: specifically, a historical decade and two decades from two different future climate projections (i.e. SSP1-2.6, SSP5-8.5). Despite not being given any spatiotemporal information, GMM identifies several spatially coherent regions of ozone structure. Using a combination of statistical guidance and post-hoc judgement, we select a six-class representation of global ozone, consisting of two tropical classes and four mid-to-high latitude classes. The tropical classes feature a relatively high-altitude tropopause, while the higher-latitude classes feature a lower-altitude tropopause and low values of tropospheric ozone, as expected based on broad patterns observed in the atmosphere. Both of the future projections feature lower tropospheric ozone concentrations than the historical benchmark, with signatures of ozone hole recovery. We find that the area occupied by the tropical classes is expanded in both future projections, in consistency with the tropical broadening hypothesis. Our results suggest that GMM may be a useful method for identifying coherent ozone regimes, particularly in the context of model analysis.

Fouzia Fahrin et al.

Status: open (until 06 Sep 2022)

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Fouzia Fahrin et al.

Model code and software

UKESM1 ozone clustering Fouzia Fahrin and Daniel C. Jones https://doi.org/10.5281/zenodo.6837484

Fouzia Fahrin et al.

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Short summary
We use a machine learning technique called Gaussian Mixture Modeling (GMM) to classify vertical ozone profiles into groups based on how the ozone concentration changes with pressure. Even though the GMM algorithm was not provided with spatial information, the classes are geographically coherent. We also detect signatures of tropical broadening in UKESM1 future climate scenarios. GMM may be useful for understanding ozone structures in modeled and observed datasets.
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