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  Phase Coherence Between Surrounding Oceans Enhances Precipitation Shortages in Northeast Brazil

Mao, Y., Zou, Y., Alves, L. M., Macau, E. E. N., Taschetto, A. S., Santoso, A., Kurths, J. (2022): Phase Coherence Between Surrounding Oceans Enhances Precipitation Shortages in Northeast Brazil. - Geophysical Research Letters, 49, 9, e2021GL097647.
https://doi.org/10.1029/2021GL097647

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 Creators:
Mao, Y.1, Author
Zou, Y.1, Author
Alves, L. M.1, Author
Macau, E. E. N.1, Author
Taschetto, A. S.1, Author
Santoso, A.1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Understanding the direct and indirect impact of the Pacific and Atlantic Oceans on precipitation in the region of Northeast Brazil (NEB) is crucial for monitoring unprecedented drought events. We propose nonlinear methods of phase coherence and generalized event synchronization analysis to understand the underlying mechanism. In particular, the relationships between sea surface temperature (SST) variability and the standard precipitation index are interpreted as direct interactions, while the relationships between surrounding oceans are interpreted as indirect effects on the precipitation. Our results reveal a dominant role of tropical North Atlantic for precipitation deficit and droughts, particularly in recent decades. Meanwhile, the indirect Pacific-North Atlantic phase synchronizations have significant influence on and reinforcement of the droughts in NEB. Furthermore, we find that the instantaneous angular frequencies of precipitation and SST are drastically changed after very strong El Niño and La Niña events, therefore resulting in a higher probability of phase coherence.

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Language(s): eng - English
 Dates: 2022-04-222022-05-16
 Publication Status: Finally published
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1029/2021GL097647
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
Research topic keyword: Atmosphere
Research topic keyword: Complex Networks
Research topic keyword: Oceans
Model / method: Nonlinear Data Analysis
OATYPE: Green Open Access
 Degree: -

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Title: Geophysical Research Letters
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 49 (9) Sequence Number: e2021GL097647 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals182
Publisher: Wiley
Publisher: American Geophysical Union (AGU)