A Novel Heuristic Method for Detecting Overfit in Unsupervised Classification of Climate Model Data

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1017/eds.2023.40. This is version 5 of this Preprint.

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Emma Joan Douglas Boland, Erin Atkinson , Dan Jones 


Unsupervised classification is becoming an increasingly common method to objectively identify coherent structures within both observed and modelled climate data. However, in most applications using this method, the user must choose the number of classes into which the data are to be sorted in advance. Typically, a combination of statistical methods and expertise is used to choose the appropriate number of classes for a given study, however it may not be possible to identify a single `optimal' number of classes. In this work, we present a heuristic method, the Ensemble Difference Criterion, for unambiguously determining the maximum number of classes supported by model data ensembles. This method requires robustness in the class definition between simulated ensembles of the system of interest. For demonstration, we apply this to the clustering of Southern Ocean potential temperatures in a CMIP6 climate model, and show that the data supports between four and seven classes of a Gaussian Mixture Model.




Analysis, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics


climate modelling, , Unsupervised Classification, Methods, Ocean Data, unsupervised classification, methods, Ocean Data


Published: 2023-03-10 08:04

Last Updated: 2023-10-25 07:25

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