Literature DB >> 23818627

Improved El Nino forecasting by cooperativity detection.

Josef Ludescher1, Avi Gozolchiani, Mikhail I Bogachev, Armin Bunde, Shlomo Havlin, Hans Joachim Schellnhuber.   

Abstract

Although anomalous episodic warming of the eastern equatorial Pacific, dubbed El Niño by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead. A significant extension of the prewarning time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a unique avenue toward El Niño prediction based on network methods, inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode--linking the El Niño basin (equatorial Pacific corridor) and the rest of the ocean--builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data available since 1950 and yields hit rates above 0.5, whereas false-alarm rates are below 0.1.

Entities:  

Keywords:  ENSO; climate; cross-correlations; dynamic networks; spring barrier

Mesh:

Year:  2013        PMID: 23818627      PMCID: PMC3718177          DOI: 10.1073/pnas.1309353110

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  12 in total

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Authors:  R Sari Kovats; Menno J Bouma; Shakoor Hajat; Eve Worrall; Andy Haines
Journal:  Lancet       Date:  2003-11-01       Impact factor: 79.321

2.  Emergence of El Niño as an autonomous component in the climate network.

Authors:  A Gozolchiani; S Havlin; K Yamasaki
Journal:  Phys Rev Lett       Date:  2011-09-30       Impact factor: 9.161

3.  Extreme climate of the global troposphere and stratosphere in 1940-42 related to El Niño.

Authors:  S Brönnimann; J Luterbacher; J Staehelin; T M Svendby; G Hansen; T Svenøe
Journal:  Nature       Date:  2004-10-21       Impact factor: 49.962

4.  Intense hurricane activity over the past 5,000 years controlled by El Niño and the West African monsoon.

Authors:  Jeffrey P Donnelly; Jonathan D Woodruff
Journal:  Nature       Date:  2007-05-24       Impact factor: 49.962

5.  Tipping elements in the Earth's climate system.

Authors:  Timothy M Lenton; Hermann Held; Elmar Kriegler; Jim W Hall; Wolfgang Lucht; Stefan Rahmstorf; Hans Joachim Schellnhuber
Journal:  Proc Natl Acad Sci U S A       Date:  2008-02-07       Impact factor: 11.205

6.  El Nino/Southern Oscillation response to global warming.

Authors:  M Latif; N S Keenlyside
Journal:  Proc Natl Acad Sci U S A       Date:  2008-12-05       Impact factor: 11.205

7.  Topology and predictability of El Niño and La Niña networks.

Authors:  Anastasios A Tsonis; Kyle L Swanson
Journal:  Phys Rev Lett       Date:  2008-06-05       Impact factor: 9.161

8.  Climate networks around the globe are significantly affected by El Niño.

Authors:  K Yamasaki; A Gozolchiani; S Havlin
Journal:  Phys Rev Lett       Date:  2008-06-05       Impact factor: 9.161

9.  El Niño in a changing climate.

Authors:  Sang-Wook Yeh; Jong-Seong Kug; Boris Dewitte; Min-Ho Kwon; Ben P Kirtman; Fei-Fei Jin
Journal:  Nature       Date:  2009-09-24       Impact factor: 49.962

10.  Predicting stochastic systems by noise sampling, and application to the El Niño-Southern Oscillation.

Authors:  Mickaël David Chekroun; Dmitri Kondrashov; Michael Ghil
Journal:  Proc Natl Acad Sci U S A       Date:  2011-07-05       Impact factor: 11.205

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  15 in total

1.  Climate network percolation reveals the expansion and weakening of the tropical component under global warming.

Authors:  Jingfang Fan; Jun Meng; Yosef Ashkenazy; Shlomo Havlin; Hans Joachim Schellnhuber
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-26       Impact factor: 11.205

2.  Very early warning of next El Niño.

Authors:  Josef Ludescher; Avi Gozolchiani; Mikhail I Bogachev; Armin Bunde; Shlomo Havlin; Hans Joachim Schellnhuber
Journal:  Proc Natl Acad Sci U S A       Date:  2014-02-10       Impact factor: 11.205

3.  Network analysis reveals strongly localized impacts of El Niño.

Authors:  Jingfang Fan; Jun Meng; Yosef Ashkenazy; Shlomo Havlin; Hans Joachim Schellnhuber
Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-03       Impact factor: 11.205

4.  Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier.

Authors:  Jun Meng; Jingfang Fan; Josef Ludescher; Ankit Agarwal; Xiaosong Chen; Armin Bunde; Jürgen Kurths; Hans Joachim Schellnhuber
Journal:  Proc Natl Acad Sci U S A       Date:  2019-12-24       Impact factor: 11.205

5.  Network-based forecasting of climate phenomena.

Authors:  Josef Ludescher; Maria Martin; Niklas Boers; Armin Bunde; Catrin Ciemer; Jingfang Fan; Shlomo Havlin; Marlene Kretschmer; Jürgen Kurths; Jakob Runge; Veronika Stolbova; Elena Surovyatkina; Hans Joachim Schellnhuber
Journal:  Proc Natl Acad Sci U S A       Date:  2021-11-23       Impact factor: 11.205

6.  Detrended partial-cross-correlation analysis: a new method for analyzing correlations in complex system.

Authors:  Naiming Yuan; Zuntao Fu; Huan Zhang; Lin Piao; Elena Xoplaki; Juerg Luterbacher
Journal:  Sci Rep       Date:  2015-01-30       Impact factor: 4.379

7.  Local difference measures between complex networks for dynamical system model evaluation.

Authors:  Stefan Lange; Jonathan F Donges; Jan Volkholz; Jürgen Kurths
Journal:  PLoS One       Date:  2015-04-09       Impact factor: 3.240

8.  Percolation-based precursors of transitions in extended systems.

Authors:  Víctor Rodríguez-Méndez; Víctor M Eguíluz; Emilio Hernández-García; José J Ramasco
Journal:  Sci Rep       Date:  2016-07-14       Impact factor: 4.379

9.  Percolation Phase Transition of Surface Air Temperature Networks: A new test bed for El Niño/La Niña simulations.

Authors:  Lijuan Hua; Zhenghui Lu; Naiming Yuan; Lin Chen; Yongqiang Yu; Lu Wang
Journal:  Sci Rep       Date:  2017-08-16       Impact factor: 4.379

10.  A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables.

Authors:  Naiming Yuan; Elena Xoplaki; Congwen Zhu; Juerg Luterbacher
Journal:  Sci Rep       Date:  2016-06-13       Impact factor: 4.379

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