Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 251–266, 2019

Special issue: Centennial issue on nonlinear geophysics: accomplishments...

Nonlin. Processes Geophys., 26, 251–266, 2019

Research article 15 Aug 2019

Research article | 15 Aug 2019

Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

Jürgen Kurths et al.

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Recurrence analysis of extreme event like data
Abhirup Banerjee, Bedartha Goswami, Yoshito Hirata, Deniz Eroglu, Bruno Merz, Jürgen Kurths, and Norbert Marwan
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Revised manuscript under review for NPG
Influence of extreme events modeled by Lévy flight on global thermohaline circulation stability
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Interacting tipping elements increase risk of climate domino effects under global warming
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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Cited articles

Abid, M. A., Almazroui, M., Kucharski, F., O'Brien, E., and Yousef, A. E.: ENSO relationship to summer rainfall variability and its potential predictability over Arabian Peninsula region, npj Climate and Atmospheric Science, 1, 20171,, 2018. 
Agarwal, A.: Unraveling spatio-temporal climatic patterns via multi-scale complex networks, Universität Potsdam, 2019. 
Agarwal, A., Maheswaran, R., Kurths, J., and Khosa, R.: Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization – a Case Study in the Western United States, Water Resour. Manag., 30, 4399–4413,, 2016. 
Agarwal, A., Marwan, N., Rathinasamy, M., Merz, B., and Kurths, J.: Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach, Nonlin. Processes Geophys., 24, 599–611,, 2017. 
Agarwal, A., Marwan, N., Maheswaran, R., Merz, B., and Kurths, J.: Quantifying the roles of single stations within homogeneous regions using complex network analysis, J. Hydrol., 563, 802–810,, 2018a. 
Short summary
We examined the spatial diversity of Indian rainfall teleconnection at different timescales, first by identifying homogeneous communities and later by computing non-linear linkages between the identified communities (spatial regions) and dominant climatic patterns, represented by climatic indices such as El Nino–Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic Multi-Decadal Oscillation.