Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm
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 statistic...
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ftunivloyola:oai:repositorio.uloyola.es:20.500.12412/1144 2024-09-15T18:12:02+00:00 Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm Athanasia, Nikolau Gutiérrez Peña, Pedro Antonio Durán Rosal, Antonio Manuel Dicaire, Isabelle Fernández Navarro, Francisco Hervás Martínez, César 2015 https://hdl.handle.net/20.500.12412/1144 eng eng Nikolaou, A., Gutiérrez, P.A., Durán, A. et al. Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm. Clim Dyn 44, 1919–1933 (2015). https://doi.org/10.1007/s00382-014-2405-0 0930-7575 http://hdl.handle.net/20.500.12412/1144 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess Warning Signals Time series segmentation Tipping points Abrupt climate change Genetic algorithms Clustering article 2015 ftunivloyola https://doi.org/20.500.12412/114410.1007/s00382-014-2405-0 2024-07-14T23:33:33Z 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 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 analysis. Es la versión enviada del artículo. Se puede consultar la versión final en https://doi.org/10.1007/s00382-014-2405-0 Article in Journal/Newspaper ice core NGRIP Institutional repository of the Universidad Loyola Andalucía Climate Dynamics 44 7-8 1919 1933 |
institution |
Open Polar |
collection |
Institutional repository of the Universidad Loyola Andalucía |
op_collection_id |
ftunivloyola |
language |
English |
topic |
Warning Signals Time series segmentation Tipping points Abrupt climate change Genetic algorithms Clustering |
spellingShingle |
Warning Signals Time series segmentation Tipping points Abrupt climate change Genetic algorithms Clustering Athanasia, Nikolau Gutiérrez Peña, Pedro Antonio Durán Rosal, Antonio Manuel Dicaire, Isabelle Fernández Navarro, Francisco Hervás Martínez, César Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
topic_facet |
Warning Signals Time series segmentation Tipping points Abrupt climate change Genetic algorithms Clustering |
description |
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 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 analysis. Es la versión enviada del artículo. Se puede consultar la versión final en https://doi.org/10.1007/s00382-014-2405-0 |
format |
Article in Journal/Newspaper |
author |
Athanasia, Nikolau Gutiérrez Peña, Pedro Antonio Durán Rosal, Antonio Manuel Dicaire, Isabelle Fernández Navarro, Francisco Hervás Martínez, César |
author_facet |
Athanasia, Nikolau Gutiérrez Peña, Pedro Antonio Durán Rosal, Antonio Manuel Dicaire, Isabelle Fernández Navarro, Francisco Hervás Martínez, César |
author_sort |
Athanasia, Nikolau |
title |
Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
title_short |
Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
title_full |
Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
title_fullStr |
Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
title_full_unstemmed |
Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
title_sort |
detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |
publishDate |
2015 |
url |
https://hdl.handle.net/20.500.12412/1144 |
genre |
ice core NGRIP |
genre_facet |
ice core NGRIP |
op_relation |
Nikolaou, A., Gutiérrez, P.A., Durán, A. et al. Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm. Clim Dyn 44, 1919–1933 (2015). https://doi.org/10.1007/s00382-014-2405-0 0930-7575 http://hdl.handle.net/20.500.12412/1144 |
op_rights |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess |
op_doi |
https://doi.org/20.500.12412/114410.1007/s00382-014-2405-0 |
container_title |
Climate Dynamics |
container_volume |
44 |
container_issue |
7-8 |
container_start_page |
1919 |
op_container_end_page |
1933 |
_version_ |
1810449621638447104 |