Articles | Volume 23, issue 6
https://doi.org/10.5194/acp-23-3609-2023
https://doi.org/10.5194/acp-23-3609-2023
Technical note
 | 
24 Mar 2023
Technical note |  | 24 Mar 2023

Technical note: Unsupervised classification of ozone profiles in UKESM1

Fouzia Fahrin, Daniel C. Jones, Yan Wu, James Keeble, and Alexander T. Archibald

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Latest update: 21 Jun 2024
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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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