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  Hierarchical structures in Northern Hemispheric extratropical winter ocean–atmosphere interactions

Wiedermann, M., Donges, J. F., Handorf, D., Kurths, J., Donner, R. V. (2017): Hierarchical structures in Northern Hemispheric extratropical winter ocean–atmosphere interactions. - International Journal of Climatology, 37, 10, 3821-3836.
https://doi.org/10.1002/joc.4956

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

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 Abstract: In recent years extensive studies on the Earth's climate system have been carried out by means of advanced complex network statistics. The great majority of these studies, however, have been focusing on investigating correlation structures within single climatic fields directly on or parallel to the Earth's surface. Here, we develop a novel approach of node weighted coupled network measures to study correlations between ocean and atmosphere in the Northern Hemisphere extratropics and construct 18 coupled climate networks, each consisting of two subnetworks. In all cases, one subnetwork represents monthly sea‐surface temperature (SST ) anomalies, while the other is based on the monthly geopotential height (HGT ) of isobaric surfaces at different pressure levels covering the troposphere as well as the lower stratosphere. The weighted cross‐degree density proves to be consistent with the leading coupled pattern obtained from maximum covariance analysis. Network measures of higher order allow for a further analysis of the correlation structure between the two fields and consistently indicate that in the Northern Hemisphere extratropics the ocean is correlated with the atmosphere in a hierarchical fashion such that large areas of the ocean surface correlate with multiple statistically dissimilar regions in the atmosphere. Ultimately we show that this observed hierarchy is linked to large‐scale atmospheric variability patterns, such as the Pacific North American pattern, forcing the ocean on monthly time scales.

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 Dates: 2017
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/joc.4956
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
PIKDOMAIN: Earth System Analysis - Research Domain I
eDoc: 7500
Research topic keyword: Atmosphere
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Oceans
Model / method: Nonlinear Data Analysis
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: International Journal of Climatology
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 37 (10) Sequence Number: - Start / End Page: 3821 - 3836 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals218