Skip to content

English

Climate networks constructed by using information-theoretic measures and ordinal tima-series analysis

Juan Ignacio Deza, Presentation date: February 26, 2015

Author: J. Ignacio Deza
Title: Climate networks constructed by using information-theoretic measures and ordinal time-series analysis
Directors: Cristina Masoller and Marcelo Barreiro Parrillo (Universidad de la República, Montevideo, Uruguay)
Presentation date: February 26, 2015
Link to text: http://www.tdx.cat/handle/10803/286281

Abstract: This Thesis is devoted to the construction of global climate networks (CNs) built from time series -surface air temperature anomalies (SAT)- using nonlinear analysis. Several information theory measures have been used including mutual information (MI) and conditional mutual information (CMI). The ultimate goal of the study is to improve the present understanding of climatic variability by means of networks, focusing on the different spatial and time-scales of climate phenomena. An introduction to the main components of this interdisciplinary work are offered in the first three chapters. Climate variability and patterns are introduced Chapter 1, network theory in Chapter 2, and nonlinear time series analysis -especially information theoretic methodology- in Chapter 3. In Chapter 4, the statistical similarity of SAT anomalies in different regions of the world is assessed using MI. These climate networks are constructed from time series of monthly averaged SAT anomalies, and from their symbolic ordinal representation, which allows an analysis of these interdependencies on different time scales. This analysis allows identifying topological changes in the networks when using ordinal patterns (OPs) of different time intervals. Intra-seasonal (of a few months), inter-seasonal (covering a year) and inter-annual (several years) timescales are considered. The nature of the interdependencies is then explored in Chapter 5 by using SAT data from an ensemble of atmospheric general circulation model (AGCM) runs, all of them forced by the same historical sea surface temperature (SST). It is possible to separate atmospheric variability into a forced component, and another one intrinsic to the atmosphere. In this way, it is possible to obtain climate networks for both types of variability and characterize them. Furthermore, an analysis using OP allows to construct CNs for several time scales, and evaluate the connectivity of each different network. This selecting both time scale and variability type allows to obtain a further insight into the study of SAT anomalies. The connectivity of the constructed CNs allows to assess the influence of two main climate phenomena: ENSO and the North Atlantic Oscillation (NAO). In Chapter 6, a natural extension of the network construction methodology is implemented in order to infer the direction of the links. A directionality index (DI) is used. DI can be defined as the difference of the CMI between two time series x(t) and y(t), calculated in two ways: i) considering the information about x(t) contained in t time units in the past of y(t), and ii) considering the information about y(t) contained in t time units in the past of x(t). DI is used to quantify the direction of information flow among the series, indicating the direction of the links of the network. Two SAT datasets -one monthly-averaged and another daily-averaged- are used. The links of the obtained networks are interpreted in terms of known atmospheric tropical and extra-tropical variability phenomena. Specific and relevant geographical regions are selected, the net direction of propagation of the atmospheric patterns is analyzed, and the direction of the inferred links is tested using surrogate data. These patterns are also found to be acting on various time scales, such as synoptic atmospheric waves in the extra-tropics or longer time scale events in the tropics. The final Chapter 7 presents the main conclusions, and a discussion of future work.