Interactions, information transfer and complex structures in the dynamics of changing climate
Abstract
Methods of complex networks and graph theory, tools from information theory and synchronization of nonlinear systems will be used in development of mathematical methods and computer algorithms for analysis of high-dimensional, nonlinear time series. Spectral and Monte-Carlo Markov-Chain approaches will be used for identification of hierarchical structures and clusters in climate networks constructed from spatio-temporal fields of meteorological variables. Information flows among important clusters will be quantified using information-theoretic functionals. Structures, clusters and information flows in network representation will be confronted with known modes of climate variability and with teleconnection patterns – global communication in the Earth atmosphere. Stability of the climate system and its sensitivity to external influences (greenhouse gases, solar and geomagnetic activity) will be evaluated within the complex network paradigm. New theoretical and computational approach will help to understand natural influences and anthropogenic forcing of global change.
Within the project, Institute of Computer Science collaborates with the Potsdam Institute for Climate Impact Research.
The project is supported by Czech Science Foundation project No. P103/11/J068.
Publications
Articles
- Paluš, M., Hartman, D., Hlinka, J., and Vejmelka, M.: Discerning connectivity from dynamics in climate networks, Nonlin. Processes Geophys., 18, pp. 751-763, 2011.
Posters
- The eff ect of nonlinearity in computing graph theoretic characteristics of complex networks
- Relation of structure and dynamics in complex systems: consequences for graph-theoretical analysis
- Inferring coupling structure from dynamics in complex systems: consequences for graph-theoretical analysis
- Sensitivity of centrality measures to estimation of adjacency structure: a study of mutual information estimators
