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Spatial quantile clustering of climate data

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Abstract

In the era of climate change, the distribution of climate variables evolves with changes not limited to the mean value. Consequently, clustering algorithms based on central tendency could produce misleading results when used to summarize spatial and/or temporal patterns. We present a novel approach to spatial clustering of time series based on quantiles using a Bayesian framework that incorporates a spatial dependence layer based on a Markov random field. A series of simulations tested the proposal, then applied to the sea surface temperature of the Mediterranean Sea, one of the first seas to be affected by the effects of climate change.

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Acknowledgements

The authors would like to thank Noémie Le Carrer for retrieving the dataset.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Carlo Gaetan, Paolo Girardi and Victor Muthama Musau have contributed equally to this work.

Authors and Affiliations

  1. Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari, Via Torino 155, 30172, Venezia-Mestre, Italy

    Carlo Gaetan & Paolo Girardi

  2. Department of Pure and Applied Sciences, Kirinyaga University, C74, Kutus- Kerugoya Rd, 10300, Kerugoya, Kenya

    Victor Muthama Musau

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  1. Carlo Gaetan
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  2. Paolo Girardi
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Correspondence to Paolo Girardi.

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Gaetan, C., Girardi, P. & Musau, V.M. Spatial quantile clustering of climate data. Adv Data Anal Classif (2024). https://doi.org/10.1007/s11634-024-00580-y

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