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  A complex network representation of wind flows

Gelbrecht, M., Boers, N., Kurths, J. (2017): A complex network representation of wind flows. - Chaos, 27, 3, 35808.
https://doi.org/10.1063/1.4977699

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

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 Abstract: Climate networks have proven to be a valuable method to investigate spatial connectivity patterns of the climate system. However, so far such networks have mostly been applied to scalar observables. In this study, we propose a new method for constructing networks from atmospheric wind fields on two-dimensional isobaric surfaces. By connecting nodes along a spatial environment based on the local wind flow, we derive a network representation of the low-level circulation that captures its most important characteristics. In our approach, network links are placed according to a suitable statistical null model that takes into account the direction and magnitude of the flow. We compare a simulation-based (numerically costly) and a semi-analytical (numerically cheaper) approach to determine the statistical significance of possible connections, and find that both methods yield qualitatively similar results. As an application, we choose the regional climate system of South America and focus on the monsoon season in austral summer. Monsoon systems are generally characterized by substantial changes in the large-scale wind directions, and therefore provide ideal applications for the proposed wind networks. Based on these networks, we are able to reveal the key features of the low-level circulation of the South American Monsoon System, including the South American Low-Level Jet. Networks of the dry and the wet season are compared with each other and their differences are consistent with the literature on South American climate.

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 Dates: 2017
 Publication Status: Finally published
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1063/1.4977699
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7580
Research topic keyword: Complex Networks
Research topic keyword: Monsoon
Model / method: Nonlinear Data Analysis
Regional keyword: South America
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
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: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 27 (3) Sequence Number: 35808 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808