Acknowledgements MW and RVD have been supported by the German Federal Ministry for Education and Research via the BMBF Young Investigators Group CoSy-CC2 (grant no. 01LN1306A) and the Belmont Forum/JPI Climate project GOTHAM. JFD is grateful for financial support by the Stordalen Foundation (via the Planetary Boundary Research Network PB.net) and the Earth League's EarthDoc program. JK acknowledges the IRTG 1740 funded by Deutsche Forschungsgemeinschaft and FAPESP. Coupled climate network analysis has been performed using the Python package pyunicorn (Donges et al., 2015b) that is available at https://github.com/pik-copan/pyunicorn.Peer reviewedPostprin

Albert

Boccaletti

Boers

Bretherton

Buldyrev

Czaja

Donges

Donner

Feng

Frankignoul

Gastineau

Ghil

Handorf

Hannachi

Heitzig

Hlinka

Kretschmer

Kushnir

Ludescher

Malik

Molkenthin

Newman

Paluš

Radebach

Ravasz

Rayner

Runge

Serreze

Steinhaeuser

Stolbova

Tantet

Tirabassi

Trenberth

Tsonis

Tupikina

Uppala

van der Mheen

Vespignani

von Storch

Wallace

Wiedermann

Woollings

Wyrtki

Yamasaki

Zemp

International Journal of Climatology

English

arXiv

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

Wiedermann, Marc

Donges, Jonathan F.

Handorf, Dörthe

Kurths, Jürgen

Donner, Reik V.

Electronic Publication Information Center

Hierarchical structures in Northern Hemispheric extratropical winter ocean-atmosphere interactions

Acknowledgements MW and RVD have been supported by the German Federal Ministry for Education and Research via the BMBF Young Investigators Group CoSy-CC2 (grant 18 Marc Wiedermann et al. no. 01LN1306A). JFD thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. JK acknowledges the IRTG 1740 funded by DFG and FAPESP. Coupled climate network analysis has been performed using the Python package pyunicorn (Donges et al, 2015a) that is available at https://github.com/pik-copan/pyunicorn.Peer reviewedPreprin

Hierarchical structures in Northern Hemispheric extratropical winter ocean-atmosphere interactions

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