Large-Scale Atmospheric Phenomena Under the Lens of Ordinal Time-Series Analysis and Information Theory Measures J.I. Deza, G. Tirabassi, M. Barreiro, and C. Masoller 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. Keywords Climate networks • Nonlinear time series analysis • Climate commu- nities • Information transfer 1 Introduction Complex networks constitute the huge revolution in nonlinear science in the twentieth century because it provides a unified framework for the study of a wide range of real-world complex systems, such as the Internet, social networks, transport networks, ecological and metabolic networks, and even the human brain (Albert and Barabasi 2002; Newman 2003; Boccaletti et al. 2006). For understanding and extracting information from observed data, various meth- ods for mapping statistical interdependencies between time series into “functional” J.I. Deza • G. Tirabassi • M. Barreiro Instituto de Física, Facultad de Ciencias, Universidad de la República, Igua 4225, Barcelona, Spain C. Masoller () Departament de Fisica, Universitat Politecnica de Catalunya, Colom 11, Terrassa, 08222, Barcelona, Spain e-mail: cristina.masoller@upc.edu © Springer International Publishing AG 2018 A.A. Tsonis (ed.), Advances in Nonlinear Geosciences, DOI 10.1007/978-3-319-58895-7_4 87