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  Arctic weather variability and connectivity

Meng, J., Fan, J., Bhatt, U. S., Kurths, J. (2023): Arctic weather variability and connectivity. - Nature Communications, 14, 6574.
https://doi.org/10.1038/s41467-023-42351-x

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Meng_2024_s41467-023-42351-x.pdf (Publisher version), 11MB
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 Creators:
Meng, Jun1, Author
Fan, Jingfang2, Author              
Bhatt, Uma S.1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The Arctic’s rapid sea ice decline may influence global weather patterns, making the understanding of Arctic weather variability (WV) vital for accurate weather forecasting and analyzing extreme weather events. Quantifying this WV and its impacts under human-induced climate change remains a challenge. Here we develop a complexity-based approach and discover a strong statistical correlation between intraseasonal WV in the Arctic and the Arctic Oscillation. Our findings highlight an increased variability in daily Arctic sea ice, attributed to its decline accelerated by global warming. This weather instability can influence broader regional patterns via atmospheric teleconnections, elevating risks to human activities and weather forecast predictability. Our analyses reveal these teleconnections and a positive feedback loop between Arctic and global weather instabilities, offering insights into how Arctic changes affect global weather. This framework bridges complexity science, Arctic WV, and its widespread implications.

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Language(s): eng - English
 Dates: 2023-10-182023-10-18
 Publication Status: Finally published
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-023-42351-x
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Atmosphere
Research topic keyword: Climate impacts
Working Group: Network- and machine-learning-based prediction of extreme events
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
OATYPE: Gold Open Access
 Degree: -

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Title: Nature Communications
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 14 Sequence Number: 6574 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354
Publisher: Nature