Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales

Deza, J. Ignacio; Barreiro, Marcelo; Masoller, Cristina

doi:10.15248/proc.1.235

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…Specifically, combinations with complex network analysis, where concepts like ordinary or conditional mutual information have already been successfully utilized for inferring the most relevant couplings among sets of variables in a climate context [60,[208][209][210][211], have great potentials for unveiling and quantifying possibly unknown dynamical interrelationships, responses and feedbacks. Besides their application in the context of climate networks, the latter concepts have also been successfully utilized for studying the spatio-temporal backbone of seismicity [234], suggesting a much wider range of potential research questions to be addressed with similar approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Selecting the strongest and/or statistically most significant pairwise associations provides a discrete set of spatial interdependencies, which can be further analyzed by network-theoretic concepts. Besides classical linear correlations and concepts referring to different notions of synchronization [202][203][204][205] (which have proven their potentials for bivariate analyses of climate data [206,207] before applications to network construction), application of mutual information based on symbolic dynamics [208,209] or order patterns [210,211] has attracted considerable interest and leads to a multi-scale description of the backbone of relevant spatial interdependencies in the climate system. Recently, the corresponding framework has been successfully generalized to studying directed interrelationships by making use of conditional mutual information [60].…”

Section: Interdependencies and Causality Between Atmospheric Variabilmentioning

confidence: 99%

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Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

Balasis

Donner

Potirakis

et al. 2013

Entropy

104

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This review provides a summary of methods originated in (non-equilibrium) statistical mechanics and information theory, which have recently found successful applications to quantitatively studying complexity in various components of the complex Entropy 2013, 15 4845 system Earth. Specifically, we discuss two classes of methods: (i) entropies of different kinds (e.g., on the one hand classical Shannon and Rényi entropies, as well as non-extensive Tsallis entropy based on symbolic dynamics techniques and, on the other hand, approximate entropy, sample entropy and fuzzy entropy); and (ii) measures of statistical interdependence and causality (e.g., mutual information and generalizations thereof, transfer entropy, momentary information transfer). We review a number of applications and case studies utilizing the above-mentioned methodological approaches for studying contemporary problems in some exemplary fields of the Earth sciences, highlighting the potentials of different techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Interdependencies and Causality Between Atmospheric Variabilmentioning

confidence: 99%

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

Balasis

Donner

Potirakis

et al. 2013

Entropy

104

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“…The links were weighted with cos( i ) to correct the bias induced by the different areas represented by the different grid points, as it is usually done in climate network analysis (Donges et al, 2009;Deza et al, 2013;Tirabassi and Masoller, 2013). It is easy to see that the AWC i is the fraction of the region under study to which a node i is connected to.…”

Section: Air-sea Connectivitymentioning

confidence: 99%

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

2014

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Air-sea interaction in the region of the South Atlantic Convergence Zone (SACZ) is studied using Granger causality (GC) as a measure of directional coupling. Calculation of the area weighted connectivity indicates that the SACZ region is the one with largest mutual air-sea connectivity in the south Atlantic basin during summertime. Focusing on the leading mode of daily coupled variability, GC allows distinguishing four regimes characterized by different coupling: there are years in which the forcing is mainly directed from the atmosphere to the ocean, years in which the ocean forces the atmosphere, years in which the influence is mutual and years in which the coupling is not significant. A composite analysis shows that ocean-driven events have atmospheric anomalies that develop first and are strongest over the ocean, while in events without coupling anomalies develop from the continent where they are strongest and have smaller oceanic extension.

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“…TRMM 3B42V7: satellite-derived data (Huffman et al, 2007). NCEP/NCAR: reanalysis data (Kalnay et al, 1996 2010; Gozolchiani et al, 2008Gozolchiani et al, , 2011Yamasaki et al, 2009;Paluš et al, 2011;Barreiro et al, 2011;Deza et al, 2013Deza et al, , 2014Martin et al, 2013;Tirabassi and Masoller, 2013). While most of these studies are focused on global climate networks of temperature fields and precipitation (Donges et al, 2009a, b;Tsonis and Roebber, 2004;Tsonis et al, 2006;Gozolchiani et al, 2011;Yamasaki et al, 2008Yamasaki et al, , 2009Scarsoglio et al, 2013), others consider smaller, regional networks that focus on a specific climate phenomenon of interest, such as El Niño Gozolchiani et al, 2008), Rossby waves (Wang et al, 2013), continental rainfall in Germany (Rheinwalt et al, 2012), the South American Monsoon System (SAMS) (Boers et al, 2013), and the Indian Summer Monsoon (Malik et al, 2010(Malik et al, , 2011Rehfeld et al, 2012).…”

Section: Climate Network As a Tool For Ism Analysismentioning

confidence: 99%

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

Stolbova

Martin

Bookhagen

et al. 2014

Nonlin. Processes Geophys.

109

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Abstract. This paper employs a complex network approach to determine the topology and evolution of the network of extreme precipitation that governs the organization of extreme rainfall before, during, and after the Indian Summer Monsoon (ISM) season. We construct networks of extreme rainfall events during the ISM (June-September), post-monsoon (October-December), and pre-monsoon (March-May) periods from satellite-derived (Tropical Rainfall Measurement Mission, TRMM) and rain-gauge interpolated (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE) data sets. The structure of the networks is determined by the level of synchronization of extreme rainfall events between different grid cells throughout the Indian subcontinent. Through the analysis of various complex-network metrics, we describe typical repetitive patterns in North Pakistan (NP), the Eastern Ghats (EG), and the Tibetan Plateau (TP). These patterns appear during the pre-monsoon season, evolve during the ISM, and disappear during the post-monsoon season. These are important meteorological features that need further attention and that may be useful in ISM timing and strength prediction.

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Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales

Cited by 5 publications

References 8 publications

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

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