C. Beaulieu, Jie Chen and J. L. Sarmiento Change point analysis as a tool to detect abrupt climate variations
Full article (partial public access?) at Royal Society, doi:10.1098/rsta.2011.0383
Keywords
change point detection, autocorrelation, regime shift, abrupt climate change
Abstract
Recently, there have been an increasing number of studies using change point methods to detect artificial or natural discontinuities and regime shifts in climate. However, a major drawback with most of the currently used change point methods is the lack of flexibility (able to detect one specific type of shift under the assumption that the residuals are independent). As temporal variations in climate are complex, it may be difficult to identify change points with very simple models. Moreover, climate time series are known to exhibit autocorrelation, which corresponds to a model misspecification if not taken into account and can lead to the detection of nonexistent shifts. In this study, we extend a method known as the informational approach for change point detection to take into account the presence of autocorrelation in the model. The usefulness and flexibility of this approach is demonstrated through applications. Furthermore, it is highly desirable to develop techniques that can detect shifts soon after they occur for climate monitoring. To address this, we also perform a simulation study in order to investigate the number of years after which an abrupt shift is detectable. We show that if not detected after approximately ten years, the probabilities of detecting the shift will not increase a lot with more observations. For shifts with a very large magnitude, our simulation study shows that after four years only the probabilities of shift detection reach nearly 100%. This reveals that the approach has potential for climate monitoring.