  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.
