Large-scale atmospheric phenomena under the lens of ordinal time-series analysis and information theory measures

Deza, J. I.; Tirabassi, G.; Barreiro, M.; Masoller, C.

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.

• Online Publication Date: Oct 11, 2017
• Publication Date: Jan 1, 2018
• Deposit Date: Mar 19, 2019
• Peer Reviewed: Peer Reviewed
• Pages: 87-99
• Book Title: Advances in Nonlinear Geosciences
• ISBN: 9783319588940
• DOI: https://doi.org/10.1007/978-3-319-58895-7_4
• Keywords: climate networks, nonlinear time series analysis, climate communities, information transfer
• Public URL: https://uwe-repository.worktribe.com/output/872718
• Publisher URL: http://doi.org/10.1007/978-3-319-58895-7_4
• Related Public URLs: https://www.springer.com/gp/book/9783319588940
• Additional Information: Corporate Creators : Springer International Publishing
• Contract Date: Mar 19, 2019