A comparison of two methods for detecting abrupt changes in the variance of climatic time series

TL;DR

This study compares two methods, ICSS and SRSD, for detecting abrupt variance changes in climatic time series, finding SRSD generally more effective, especially with short series and outliers, and revealing increased climate variability.

Contribution

The paper provides a comprehensive comparison of ICSS and SRSD methods on synthetic and real climatic data, highlighting SRSD's superior performance in various scenarios.

Findings

SRSD outperforms ICSS in most scenarios, especially with outliers.

Both methods agree on change points in longer series, with some exceptions.

Climatic series show a trend toward increased variance in recent decades.

Abstract

Two methods for detecting abrupt shifts in the variance, Integrated Cumulative Sum of Squares (ICSS) and Sequential Regime Shift Detector (SRSD), have been compared on both synthetic and observed time series. In Monte Carlo experiments, SRSD outperformed ICSS in the overwhelming majority of the modelled scenarios with different sequences of variance regimes. The SRSD advantage was particularly apparent in the case of outliers in the series. When tested on climatic time series, in most cases both methods detected the same change points in the longer series (252-787 monthly values). The only exception was the Arctic Ocean SST series, when ICSS found one extra change point that appeared to be spurious. As for the shorter time series (66-136 yearly values), ICSS failed to detect any change points even when the variance doubled or tripled from one regime to another. For these time series, SRSD is recommended. Interestingly, all the climatic time series tested, from the Arctic to the Tropics, had one thing in common: the last shift detected in each of these series was toward a high-variance regime. This is consistent with other findings of increased climate variability in recent decades.