Articles | Volume 24, issue 4
Nonlin. Processes Geophys., 24, 599–611, 2017
Nonlin. Processes Geophys., 24, 599–611, 2017

Research article 13 Oct 2017

Research article | 13 Oct 2017

Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

Ankit Agarwal1,2,3, Norbert Marwan2, Maheswaran Rathinasamy4, Bruno Merz1,3, and Jürgen Kurths1,2,5 Ankit Agarwal et al.
  • 1University of Potsdam, Institute of Earth and Environmental Science, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam, Germany
  • 2Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam, Germany
  • 3GFZ German Research Centre for Geosciences, Section 5.4: Hydrology, Telegrafenberg, Potsdam, Germany
  • 4Civil engineering department, MVGR college of Engineering, Vizianagaram, India
  • 5Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanova St., Nizhny Novgorod 603950, Russia

Abstract. The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-)processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.

Short summary
Extreme events such as floods and droughts result from synchronization of different natural processes working at multiple timescales. Investigation on an observation timescale will not reveal the inherent underlying dynamics triggering these events. This paper develops a new method based on wavelets and event synchronization to unravel the hidden dynamics responsible for such sudden events. This method is tested with synthetic and real-world cases and the results are promising.