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Multi-scale event synchronization analysis for unravelling climate processes

  • 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 acrossThe 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.show moreshow less

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Metadaten
Author:Ankit AgarwalORCiDGND, Norbert MarwanORCiD, Maheswaran RathinasamyORCiD, Bruno MerzORCiDGND, Jürgen KurthsORCiDGND
URN:urn:nbn:de:kobv:517-opus4-418274
DOI:https://doi.org/10.25932/publishup-41827
ISSN:1866-8372
Parent Title (English):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Subtitle (English):a wavelet-based approach
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (661)
Document Type:Postprint
Language:English
Date of first Publication:2019/03/01
Year of Completion:2017
Publishing Institution:Universität Potsdam
Release Date:2019/03/01
Tag:EEG; coherence; desynchronization; interdependences; models; monsoon; networks; phase; precipitation; time
Issue:661
Pagenumber:13
Source:Nonlinear Processes in Geophysics 24 (2017), pp. 599–611 DOI 10.5194/npg-24-599-2017
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer Review:Referiert
Publication Way:Open Access
Grantor:Copernicus
Licence (German):License LogoCreative Commons - Namensnennung, 4.0 International