Articles | Volume 26, issue 3
https://doi.org/10.5194/npg-26-251-2019
https://doi.org/10.5194/npg-26-251-2019
Research article
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15 Aug 2019
Research article | Highlight paper |  | 15 Aug 2019

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

Jürgen Kurths, Ankit Agarwal, Roopam Shukla, Norbert Marwan, Maheswaran Rathinasamy, Levke Caesar, Raghavan Krishnan, and Bruno Merz

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Exploring meteorological droughts' spatial patterns across Europe through complex network theory
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Interacting tipping elements increase risk of climate domino effects under global warming
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Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-31,https://doi.org/10.5194/npg-2020-31, 2020
Publication in NPG not foreseen
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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, https://doi.org/10.1038/s41612-017-0003-7, 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, https://doi.org/10.1007/s11269-016-1428-1, 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, https://doi.org/10.5194/npg-24-599-2017, 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, https://doi.org/10.1016/j.jhydrol.2018.06.050, 2018a. 
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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.