Abstract
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 modeled scenarios with different sequences of variance regimes. The SRSD advantage was particularly apparent in the case of outliers in the series. On the other hand, SRSD has more parameters to adjust than ICSS, which requires more experience from the user in order to select those parameters properly. Therefore, ICSS can serve as a good starting point of a regime shift analysis. 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 sea surface temperature (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.
Highlights
A concept of regime shifts, i.e., abrupt structural changes in climatic time series, has gained popularity in recent decades
The situation is much more advanced in the area of econometrics, but the methods developed there need to be tested on climatic time series, which have their own specifics, such as a relatively short length and smaller magnitudes of shifts
This paper compares two methods: Sequential Regime Shift Detector (SRSD) developed by the author and Integrated Cumulative Sum of Squares (ICSS) developed by Inclan and Tiao (1994)
Summary
A concept of regime shifts, i.e., abrupt structural changes in climatic time series, has gained popularity in recent decades. In variance, as a direct measure of variability, have received less attention, both in documenting those changes and developing of methods for their detection. A comprehensive review of change point detection techniques for climate data by Reeves et al (2007) has no mentioning of any methods for shifts in variance. It is known, that the potential impact of changes in climate variability may be as great or greater than the impact of changes in climate means (Hansen et al, 2012; Katz, 1988). There are indications that an increase in variance may signal impending shifts in ecosystems (Carpenter and Brock, 2006) and regional climates (Wu et al, 2015)
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