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.

翻译：在气候变化时代，气候变量的分布演变不仅限于均值变化。因此，基于集中趋势的聚类算法在用于总结空间和/或时间模式时可能产生误导性结果。我们提出了一种基于分位数的时空序列空间聚类新方法，该方法采用贝叶斯框架，并融入了基于马尔可夫随机场的空间依赖性层。通过一系列模拟实验对方案进行了测试，随后将其应用于地中海海表温度——这是受气候变化影响最早的海域之一。