Spatial quantile clustering of climate data

In the era of climate change, the distribution of climate variables evolves with changesnot limited to the mean value. Consequently, clustering algorithms based on centraltendency could produce misleading results when used to summarize spatial and/ortemporal patterns. We present a novel approach to spatial clustering of time seriesbased on quantiles using a Bayesian framework that incorporates a spatial dependencelayer 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 firstseas to be affected by the effects of climate change.