Climate regime shift detection with a trans ‐dimensional, sequential Monte Carlo, variational Bayes method

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 Deca dal 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 alte rnatives from the literature and highlight the potential for ours to unlock features hidden in climate data.
Source: Australian and New Zealand Journal of Statistics - Category: Statistics Authors: Tags: Original Article Source Type: research