Abstract
Complex network theory provides an elegant and powerful framework to statistically investigate different types of systems such as society, brain or the structure of local and long-range dynamical interrelationships in the climate system. Network links in climate networks typically imply information, mass or energy exchange. However, the specific connection between oceanic or atmospheric flows and the climate network’s structure is still unclear. We propose a theoretical approach for verifying relations between the correlation matrix and the climate network measures, generalizing previous studies and overcoming the restriction to stationary flows. Our methods are developed for correlations of a scalar quantity (temperature, for example) which satisfies an advection-diffusion dynamics in the presence of forcing and dissipation. Our approach reveals that correlation networks are not sensitive to steady sources and sinks and the profound impact of the signal decay rate on the network topology. We illustrate our results with calculations of degree and clustering for a meandering flow resembling a geophysical ocean jet.
Highlights
The network approach has become an essential tool in the study of complex systems [1,2,3], where networks are reconstructed from time series in order to uncover underlying dynamics [4,5,6,7,8]
In the framework of the linear advection-diffusion equation (ADE) dynamics we are using here, a time-independent spatial forcing Fð~xÞ has no influence on the covariance matrix, as it is constructed from anomalies with respect to the mean
Correlation networks become independent from the forcing terms present in the linear ADE Eq (1)
Summary
The network approach has become an essential tool in the study of complex systems [1,2,3], where networks are reconstructed from time series in order to uncover underlying dynamics [4,5,6,7,8]. I.e. those in which geographical nodes are linked when there is similar climatic dynamics on them (as measured by correlations, mutual information, etc.), have been thoroughly investigated in the last years in [9,10,11,12,13,14]. In the same context of geophysical systems, flow networks have been introduced [15,16,17,18]. They are networks in which geographical nodes are linked when there is fluid transport from one location to another. Since correlations between different regions of a flow or geophysical system should be greatly influenced by the mass transport among them, it is natural to search for the relationship between these two types
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
