This is a Preprint and has not been peer reviewed. This is version 5 of this Preprint.
This is a Preprint and has not been peer reviewed. This is version 5 of this Preprint.
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.
https://doi.org/10.31223/X5B66V
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
CC BY Attribution 4.0 International
Conflict of interest statement:
None
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