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Climate as complex networks

The field of Complex Networks has emerged as an important area of science to generate novel insights into nature of complex systems. The application of the theory to Climate Science is a young and emerging field. , , , To identify and analyze patterns in global climate, scientists model the climate data as Complex Networks.

Unlike most of the real-world networks in which nodes and edges are well defined, nodes in climate networks are identified with the spatial grid points of underlying global climate data set, which is defined arbitrarily and can be represented at various resolutions. Two nodes are connected by an edge depending on the degree of statistical dependence between corresponding pairs of time-series taken from climate data, on the basis of similarity shared in climatic variability., The climate network approach enables novel insights into the dynamics of the climate system over many spatial scales., ,

Depending upon the choice of nodes and/or edges, climate networks may take many different forms, shapes, sizes and complexities. Tsonis et al. introduced the field of complex networks to climate. In their model, the nodes for the network were constituted by a single variable (500 hPa) from NCEP/NCAR Reanalysis datasets. In order to estimate the edges between nodes, correlation coefficient at zero time lag between all possible pairs of nodes was estimated. A pair of nodes was considered to be connected, if their correlation coefficient is above a threshold of 0.5.

The team of Havlin introduced the weighted links method which considers (i) the time delay of the link, (ii) the maximum of the cross-correlation at the time delay and (iii) the level of noise in the cross-correlation function. , , , , ,

Steinhaeuser and team introduced the novel technique of multivariate networks in climate by constructing networks from several climate variables separately and capture their interaction in multivariate predictive model. It was demonstrated in their studies that in context of climate, extracting predictors based on cluster attributes yield informative precursors to improve predictive skills.



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