Title Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm
Authors Nikolaou, Athanasia, Antonio Gutierrez, Pedro, DURAN ROSAL, ANTONIO MANUEL, Dicaire, Isabelle, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervas-Martinez, Cesar, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, DURAN ROSAL, ANTONIO MANUEL
External publication No
Means Clim. Dyn.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 4.70800
SJR Impact 3.69700
Area International
Publication date 01/04/2015
ISI 000351458300013
Scopus Id 2-s2.0-84925514342
DOI 10.1007/s00382-014-2405-0
Abstract This paper proposes a time series segmentation algorithm combining a clustering technique and a genetic algorithm to automatically find segments sharing common statistical characteristics in paleoclimate time series. The segments are transformed into a six-dimensional space composed of six statistical measures, most of which have been previously considered in the detection of warning signals of critical transitions. Experimental results show that the proposed approach applied to paleoclimate data could effectively analyse Dansgaard-Oeschger (DO) events and uncover commonalities and differences in their statistical and possibly their dynamical characterisation. In particular, warning signals were robustly detected in the GISP2 and NGRIP delta O-18 ice core data for several DO events (e.g. DO 1, 4, 8 and 12) in the form of an order of magnitude increase in variance, autocorrelation and mean square distance from a linear approximation (i.e. the mean square error). The increase in mean square error, suggesting nonlinear behaviour, has been found to correspond with an increase in variance prior to several DO events for similar to 90 % of the algorithm runs for the GISP2 delta O-18 dataset and for similar to 100 % of the algorithm runs for the NGRIP delta O-18 dataset. The proposed approach applied to well-known dynamical systems and paleoclimate datasets provides a novel visualisation tool in the field of climate time series analysis.
Keywords Warning signals; Time series segmentation; Tipping points; Abrupt climate change; Genetic algorithms; Clustering
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