Climate regime shift detection with a trans‐dimensional, sequential Monte Carlo, variational Bayes method
| Autor: | Clare A. McGrory, Daniel Ahfock, Ricardo T. Lemos |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
05 social sciences Bayesian probability Probabilistic logic Climate change 01 natural sciences 010104 statistics & probability Bayes' theorem 0502 economics and business Probability distribution Regime shift Statistical physics 0101 mathematics Statistics Probability and Uncertainty Particle filter Physics::Atmospheric and Oceanic Physics Pacific decadal oscillation 050205 econometrics Mathematics |
| Zdroj: | Australian & New Zealand Journal of Statistics. 61:175-188 |
| ISSN: | 1467-842X 1369-1473 |
| DOI: | 10.1111/anzs.12265 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|