We present an application study which exemplifies a cutting edge statistical approach for detecting climate regime shifts. The algorithm uses Bayesian computational techniques that make time-efficient analysis of large volumes of climate data possible. Output includes probabilistic estimates of the number and duration of regimes, the number and probability distribution of hidden states, and the probability of a regime shift in any year of the time series. Analysis of the Pacific Decadal Oscillation (PDO) index is provided as an example. Two states are detected: one is associated with positive values of the PDO and presents lower interannual variability, while the other corresponds to negative values of the PDO and greater variability. We compare this approach with existing alternatives from the literature and highlight the potential for ours to unlock features hidden in climate data.

中文翻译：

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使用跨维、顺序蒙特卡罗、变分贝叶斯方法进行气候状态变化检测

我们提出了一项应用研究，它举例说明了一种用于检测气候制度变化的前沿统计方法。该算法使用贝叶斯计算技术，可以对大量气候数据进行时间高效的分析。输出包括政权数量和持续时间的概率估计、隐藏状态的数量和概率分布，以及时间序列中任何一年政权转移的概率。以太平洋年代际振荡 (PDO) 指数的分析为例。检测到两种状态：一种与 PDO 的正值相关并呈现较低的年际变异性，而另一种对应于 PDO 的负值和更大的变异性。