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  Percolation analysis of the atmospheric structure

Sun, Y., Meng, J., Yao, Q., Saberi, A. A., Chen, X., Fan, J., Kurths, J. (2021): Percolation analysis of the atmospheric structure. - Physical Review E, 104, 6, 064139.
https://doi.org/10.1103/PhysRevE.104.064139

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 Creators:
Sun, Yu1, Author
Meng, Jun2, Author              
Yao, Qing1, Author
Saberi, Abbas Ali1, Author
Chen, Xiaosong1, Author
Fan, Jingfang2, Author              
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The atmosphere is a thermo-hydrodynamical complex system and provides oxygen to most animal life at the Earth's surface. However, the detection of complexity for the atmosphere remains elusive and debated. Here we develop a percolation-based framework to explore its structure by using the global air temperature field. We find that the percolation threshold is much delayed compared with the prototypical percolation model and the giant cluster eventually emerges explosively. A finite-size-scaling analysis reveals that the observed transition in each atmosphere layer is genuine discontinuous. Furthermore, at the percolation threshold, we uncover that the boundary of the giant cluster is self-affine, with fractal dimension df, and can be utilized to quantify the atmospheric complexity. Specifically, our results indicate that the complexity of the atmosphere decreases superlinearly with height, i.e., the complexity is higher at the surface than at the top layer and vice versa, due to the atmospheric boundary forcings. The proposed methodology may evaluate and improve our understanding regarding the critical phenomena of the complex Earth system and can be used as a benchmark tool to test the performance of Earth system models.

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Language(s): eng - English
 Dates: 2021-12-282021-12
 Publication Status: Finally published
 Pages: 7
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevE.104.064139
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Atmosphere
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Weather
Model / method: Nonlinear Data Analysis
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Green Open Access
 Degree: -

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Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 104 (6) Sequence Number: 064139 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218
Publisher: American Physical Society (APS)