Motivated by real-world data of monthly values of precipitation, minimum, and maximum temperature recorded at 360 monitoring stations covering the Italian territory for 60 years ($12\times 60$ months), in this work we propose a change-point model for multiple multivariate time series, inspired by the hierarchical Dirichlet process. We assume that each station has its change-point structure and, as main novelties, we allow unknown subsets of the parameters in the data likelihood to stay unchanged before and after a change-point, that stations possibly share values of the same parameters and that the unknown number of weather regimes is estimated as a random quantity. Owing to the richness of the formalization, our proposal enables us to identify clusters of spatial units for each parameter, evaluate which parameters are more likely to change simultaneously, and distinguish between abrupt changes and smooth ones. The proposed model provides useful benchmarks to focus monitoring programs regarding ecosystem responses. Results are shown for the whole data, and a detailed description is given for three monitoring stations. Evidence of local behaviors includes highlighting differences in the potential vulnerability to climate change of the Mediterranean ecosystems from the Temperate ones and locating change trends distinguishing between continental plains and mountain ranges.

中文翻译：

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基于多变量生物气候数据的变化点检测的狄利克雷过程模型

灵感来自覆盖意大利领土 60 年的 360 个监测站记录的月降水量、最低和最高温度的真实数据（$12\times 60$ 个月），在这项工作中，我们提出了一个多变量时间序列的变化点模型，灵感来自于分层 Dirichlet 过程。我们假设每个站点都有其变化点结构，并且作为主要创新，我们允许数据似然中的未知参数子集在变化点前后保持不变，站点可能共享相同参数的值，并且未知数量的天气状况被估计为随机量。由于形式化的丰富性，我们的建议使我们能够识别每个参数的空间单元集群，评估哪些参数更有可能同时变化，并区分突然变化和平滑变化。所提出的模型提供了有用的基准来关注有关生态系统响应的监测计划。结果显示为全部数据，并给出了三​​个监测站的详细描述。当地行为的证据包括突出地中海生态系统与温带生态系统对气候变化的潜在脆弱性的差异，以及确定区分大陆平原和山脉的变化趋势。