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Pullback Attractor Crisis in a Delay Differential ENSO Model Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Large-Scale Atmospheric Phenomena Under the Lens of Ordinal Time-Series Analysis and Information Theory Measures

Authors : J. I. Deza, G. Tirabassi, M. Barreiro, C. Masoller

Published in: Advances in Nonlinear Geosciences

Publisher: Springer International Publishing

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Abstract

This review presents a synthesis of our work done in the framework of the European project Learning about Interacting Networks in Climate (LINC, climatelinc.eu). We have applied tools of information theory and ordinal time series analysis to investigate large scale atmospheric phenomena from climatological datasets. Specifically, we considered monthly and daily Surface Air Temperature (SAT) time series (NCEP reanalysis) and used the climate network approach to represent statistical similarities and interdependencies between SAT time series in different geographical regions. Ordinal analysis uncovers how the structure of the climate network changes in different time scales (intra-season, intra-annual, and longer). We have also analyzed the directionally of the links of the network, and we have proposed novel approaches for uncovering communities formed by geographical regions with similar SAT properties.

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previous chapter Stochastic Parameterization of Subgrid-Scale Processes: A Review of Recent Physically Based Approaches
next chapter Supermodeling: Synchronization of Alternative Dynamical Models of a Single Objective Process
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Metadata
Title
Large-Scale Atmospheric Phenomena Under the Lens of Ordinal Time-Series Analysis and Information Theory Measures
Authors
J. I. Deza
G. Tirabassi
M. Barreiro
C. Masoller
Copyright Year
2018
Book
Advances in Nonlinear Geosciences

Print ISBN: 978-3-319-58894-0

Electronic ISBN: 978-3-319-58895-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-58895-7_4