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  A climate network-based index to discriminate different types of El Niño and La Niña

Wiedermann, M., Radebach, A., Donges, J. F., Kurths, J., Donner, R. V. (2016): A climate network-based index to discriminate different types of El Niño and La Niña. - Geophysical Research Letters, 43, 13, 7176-7185.
https://doi.org/10.1002/2016GL069119

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 Creators:
Wiedermann, Marc1, Author              
Radebach, A.2, Author
Donges, Jonathan Friedemann1, Author              
Kurths, Jürgen1, Author              
Donner, Reik V.1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: El Niño exhibits distinct Eastern Pacific (EP) and Central Pacific (CP) types which are commonly, but not always consistently, distinguished from each other by different signatures in equatorial climate variability. Here we propose an index based on evolving climate networks to objectively discriminate between both flavors by utilizing a scalar‐valued measure that quantifies spatial localization and dispersion in global teleconnections of surface air temperature. Our index displays a sharp peak (high localization) during EP events, whereas during CP events (larger dispersion) it remains close to the values observed during normal periods. In contrast to previous classification schemes, our approach specifically accounts for El Niño's global impacts. We confirm recent El Niño classifications for the years 1951 to 2014 and assign types to those cases where former works yielded ambiguous results. Ultimately, we demonstrate that our index provides a similar discrimination of La Niña episodes into two distinct types.

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Language(s):
 Dates: 2016
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/2016GL069119
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
PIKDOMAIN: Earth System Analysis - Research Domain I
eDoc: 7272
Research topic keyword: Atmosphere
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Oceans
Research topic keyword: Tipping Elements
Model / method: Nonlinear Data Analysis
Model / method: Machine Learning
Regional keyword: Global
Organisational keyword: RD4 - Complexity Science
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: FutureLab - Game Theory & Networks of Interacting Agents
Working Group: Whole Earth System Analysis
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Geophysical Research Letters
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 43 (13) Sequence Number: - Start / End Page: 7176 - 7185 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals182