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dc.contributor.authorNikolaou, Athanasia
dc.contributor.authorGutiérrez, Pedro A.
dc.contributor.authorDurán, Antonio
dc.contributor.authorDicaire, Isabelle
dc.contributor.authorFernández-Navarro, Francisco
dc.contributor.authorHervás-Martínez, César
dc.date.accessioned2017-12-21T12:07:13Z
dc.date.available2017-12-21T12:07:13Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10396/15781
dc.description.abstractThis 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 δ18O 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 ∼90 % of the algorithm runs for the GISP2 δ18O dataset and for ∼100 % of the algorithm runs for the NGRIP δ18O 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 analysises_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.subjectWarning signalses_ES
dc.subjectTime series segmentationes_ES
dc.subjectTipping pointses_ES
dc.subjectAbrupt climate changees_ES
dc.subjectGenetic algorithms Clusteringes_ES
dc.titleDetection of early warning signals in paleoclimate data using a genetic time series segmentation algorithmes_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.relation.publisherversionhttps://dx.doi.org/10.1007/s00382-014-2405-0es_ES
dc.relation.projectIDGobierno de España. TIN2011-22794es_ES
dc.relation.projectIDJunta de Andalucía. P11-TIC-7508es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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