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  Phase coherence between precipitation in South America and Rossby waves

Gelbrecht, M., Boers, N., Kurths, J. (2018): Phase coherence between precipitation in South America and Rossby waves. - Science Advances, 4, eaau3191.
https://doi.org/10.1126/sciadv.aau3191

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 Creators:
Gelbrecht, Maximilian1, Author              
Boers, Niklas1, Author              
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The dominant mode of intraseasonal precipitation variability during the South American monsoon is the so-called precipitation dipole between the South Atlantic convergence zone (SACZ) and southeastern South America (SESA). It affects highly populated areas that are of substantial importance for the regional food supplies. Previous studies using principal components analysis or complex networks were able to describe and characterize this variability pattern, but crucial questions regarding the responsible physical mechanism remain open. Here, we use phase synchronization techniques to study the relation between precipitation in the SACZ and SESA on the one hand and southern hemisphere Rossby wave trains on the other hand. In combination with a conceptual model, this approach demonstrates that the dipolar precipitation pattern is caused by the southern hemisphere Rossby waves. Our results thus show that Rossby waves are the main driver of the monsoon season variability in South America, a finding that has important implications for synoptic-scale weather forecasts.

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 Dates: 2018
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1126/sciadv.aau3191
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8406
Research topic keyword: Atmosphere
Research topic keyword: Extremes
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Monsoon
Model / method: Nonlinear Data Analysis
Regional keyword: South America
Organisational keyword: RD4 - Complexity Science
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: Science Advances
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 4 Sequence Number: eaau3191 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/161027