Climate tipping points: Detection and analysis of patterns using an ordinal regression approach. Proyecto Ariadna.

Financial Entity: European Space Agency (ESA)
Principal Investigators: César Hervás Martínez, Pedro Antonio Gutiérrez Peña

 This project aimed at gaining knowledge about the typical statistical behaviour of paleoclimate data series before the occurrence of a tipping point to allow the detection of warning signs for upcoming abrupt climate transitions. Tools developped in the field of machine learning and soft computing were applied to Greenland ice core data to provide insights on the dynamical system under study. 

In this project we proposed a time series segmentation algorithm combining a clustering technique and a genetic algorithm to automatically find segments sharing common patterns from paleoclimate time series. The segments were transformed into a six-dimensional space composed of six statistical measures most of which have been previously considered in the detection of critical transitions.

Experimental results showed that the proposed approach applied to paleoclimate data could effectively assess warning signals of Dansgaard-Oeschger (DO) events and uncover commonalities and differences in their statistical and dynamical behaviour. In particular, strong warning signals were detected in the GISP2 and NGRIP δ18O ice core data for several DO events (e.g. DO 1, 4, 8 & 12) in the form of increasing variance, autocorrelation and 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 paleoclimate data provides a novel visualisation tool in the field of climate time series analysis of critical transitions.