Unravelling the community structure of the climate system by using lags and symbolic time-series analysis
Tipo de documentoArtículo
Fecha de publicación2016-07-11
Condiciones de accesoAcceso abierto
Proyecto de la Comisión EuropeaLINC - Learning about Interacting Networks in Climate (EC-FP7-289447)
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.
This work is licensed under a Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/
CitaciónTirabassi, G., Masoller, C. Unravelling the community structure of the climate system by using lags and symbolic time-series analysis. "Scientific reports", 11 Juliol 2016, vol. 6, p. 1-10.
Versión del editorhttp://www.nature.com/articles/srep29804