Complex networks in climate dynamics

By Jonathan F. Donges, Y. Zou, Norbert Marwan, Jurgen Kurths Potsdam Institute for Climate Impact Research, Potsdam, Germany, Institute of Physics, U. Potsdam, D. Physics, Humboldt University and 1 more. 2009

490 citations recorded by the source corpus.

Complex network theory provides a powerful framework to statistically investigate the topology of local and non-local statistical interrelationships, i.e. teleconnections, in the climate system. Climate networks constructed from the same global climatological data set using the linear Pearson correlation coefficient or the nonlinear mutual information as a measure of dynamical similarity between regions, are compared systematically on local, mesoscopic and global topological scales. A high degree of similarity is observed on the local and mesoscopic topological scales for surface air temperature fields taken from AOGCM and reanalysis data sets. We find larger differences on the global scale, particularly in the betweenness centrality field. The global scale view on climate networks obtained using mutual information offers promising new perspectives for detecting network structures based on nonlinear physical processes in the climate system.

Subfields: Economics and Econometrics, Global and Planetary Change, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c1ae57dbc2edc0fb63f12a80657d49bf821335a5

Citations in the corpus

• Complex network approaches to nonlinear time series analysis (2019, unknown): “(3) climate, which focus on the construction and analysis of climate network [46,47,458,459] and coupled climate network analysis [91].”
• A graph‐based approach to find teleconnections in climate data (2013, unknown): “[16]), analyzing hurricane activity (Forgarty et al.”
• Recurrence networks—a novel paradigm for nonlinear time series analysis (2009, unknown): “These measures have been successfully applied for studying real-world networks in various scientific disciplines, including the structural properties of infrastructures [25], biological [26], ecological [27], and climate networks [28, 29], to give some prominent examples.”
• Complex network approach for recurrence analysis of time series (2009, unknown): “ation flow between different components in extended systems, such as social, computer or neural networks [25], food webs, transportation networks, power grids [29], or even in the global climate system [30]. The basis of complex network analysis is the adjacency matrix, representing the links between ”
• Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, unknown): “…that connect nodes defined as spatial grid-cells (Steinhaeuser et al., 2011b; Donges et al., 2013) or climate oscillators (Donges et al., 2009a), often known as “climate networks”, may be useful when representing climate dependencies and develop process understanding (Donges et al., 2009a, b).”
• Machine Learning for the Geosciences: Challenges and Opportunities (2017, unknown): “This includes approaches for examining the structure of the climate system [71], studying hurricane activity [72], and finding communi-”
• Mathematical and physical ideas for climate science (2013, unknown): “…analysis (Vallis, 2006), general circulation of the atmosphere (Schneider , 2006), macroturbulence (Lovejoy and Schertzer , 2013), networks theory (Donges et al., 2009), and many relevant applications of dynamical systems theory to geophysical fluid dynamical problems (Dijkstra, 2013; Kalnay ,…”
• Similarity estimators for irregular and age-uncertain time series (2013, unknown): “Mutual information (MI), an entropybased measure, has been used to investigate nonlinear dependencies of processes from observations (Donges et al., 2009; Runge et al., 2012; Hlinka et al., 2013).”