Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm
| LEADER | caa a22 4500 | ||
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| 001 | 605473617 | ||
| 003 | CHVBK | ||
| 005 | 20210128100343.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150401xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00382-014-2405-0 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00382-014-2405-0 | ||
| 245 | 0 | 0 | |a Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm |h [Elektronische Daten] |c [Athanasia Nikolaou, Pedro Gutiérrez, Antonio Durán, Isabelle Dicaire, Francisco Fernández-Navarro, César Hervás-Martínez] |
| 520 | 3 | |a 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 $$\delta ^{18}\hbox {O}$$ δ 18 O 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 $$\sim $$ ∼ 90% of the algorithm runs for the GISP2 $$\delta ^{18}\hbox {O}$$ δ 18 O dataset and for $$\sim $$ ∼ 100% of the algorithm runs for the NGRIP $$\delta ^{18}\hbox {O}$$ δ 18 O dataset. The proposed approach applied towell-known dynamical systems and paleoclimate datasets provides a novel visualisation tool in the field ofclimate time series analysis. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Warning signals |2 nationallicence | |
| 690 | 7 | |a Time series segmentation |2 nationallicence | |
| 690 | 7 | |a Tipping points |2 nationallicence | |
| 690 | 7 | |a Abrupt climate change |2 nationallicence | |
| 690 | 7 | |a Genetic algorithms |2 nationallicence | |
| 690 | 7 | |a Clustering |2 nationallicence | |
| 700 | 1 | |a Nikolaou |D Athanasia |u Advanced Concepts Team, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, Netherlands |4 aut | |
| 700 | 1 | |a Gutiérrez |D Pedro |u Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain |4 aut | |
| 700 | 1 | |a Durán |D Antonio |u Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain |4 aut | |
| 700 | 1 | |a Dicaire |D Isabelle |u Advanced Concepts Team, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, Netherlands |4 aut | |
| 700 | 1 | |a Fernández-Navarro |D Francisco |u Advanced Concepts Team, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, Netherlands |4 aut | |
| 700 | 1 | |a Hervás-Martínez |D César |u Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain |4 aut | |
| 773 | 0 | |t Climate Dynamics |d Springer Berlin Heidelberg |g 44/7-8(2015-04-01), 1919-1933 |x 0930-7575 |q 44:7-8<1919 |1 2015 |2 44 |o 382 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00382-014-2405-0 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00382-014-2405-0 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Nikolaou |D Athanasia |u Advanced Concepts Team, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, Netherlands |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gutiérrez |D Pedro |u Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Durán |D Antonio |u Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Dicaire |D Isabelle |u Advanced Concepts Team, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, Netherlands |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Fernández-Navarro |D Francisco |u Advanced Concepts Team, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, Netherlands |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hervás-Martínez |D César |u Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Climate Dynamics |d Springer Berlin Heidelberg |g 44/7-8(2015-04-01), 1919-1933 |x 0930-7575 |q 44:7-8<1919 |1 2015 |2 44 |o 382 | ||