Unravelling the community structure of the climate system by using lags and symbolic time-series analysis

Abstract

Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.

Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data

“…The map of the communities obtained, which are the regions which have a synchronous seasonal cycle, Fig. 1 13, has also several features in common with the map presented in Fig. 2 here.…”

“…The mutual lags between SAT time series in different regions were then used by Tirabassi and Masoller13 to infer climate communities , defined as regions that share similar properties of SAT time series. The map of the communities obtained, which are the regions which have a synchronous seasonal cycle, Fig.…”

“…Within this approach, a climate network is defined over a regular grid of geographical locations (nodes), and the links between pairs of nodes are defined by performing bi-variate analysis of the time series of climate variables recorded at the nodes. Different measures have been used to infer the links (cross-correlation, the mutual information, the conditional mutual entropy, Granger causality5678910111213), and similar, or different, network structures have been unveiled, depending on the measure employed and the significance confidence level used.…”

“…Climatic similarities can be due to physical processes that act in a similar way in distant regions, and produce similar effects. Such climatic similarities might be reflected as network links, depending on the similarity measure used to construct the network13. On the other hand, regions with different climate characteristics may as well be linked by genuine long-range interactions.…”

Advancing climate science with knowledge-discovery through data mining

“…Applications to climate science have received growing attention since 2004, 25 when graph theory was applied to the investigation of global geopotential height. Network analysis has been since applied to studies of numerous climate modes, [26][27][28][29][30][31] of atmospheric and oceanic circulation drivers, [32][33][34][35] of precipitation in different time periods, [36][37][38] and of Rossby wave dynamics. 39 Generally networks are constructed as undirected, binary graphs.…”