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| Titel |
Characterizing the evolution of climate networks |
| VerfasserIn |
L. Tupikina, K. Rehfeld, N. Molkenthin, V. Stolbova, N. Marwan, J. Kurths |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 3 ; Nr. 21, no. 3 (2014-06-25), S.705-711 |
| Datensatznummer |
250120924
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| Publikation (Nr.) |
 copernicus.org/npg-21-705-2014.pdf |
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| Zusammenfassung |
Complex network theory has been successfully applied to understand the
structural and functional topology of many dynamical systems from nature,
society and technology. Many properties of these systems change over time,
and, consequently, networks reconstructed from them will, too. However,
although static and temporally changing networks have been studied
extensively, methods to quantify their robustness as they evolve in time are
lacking. In this paper we develop a theory to investigate how networks are
changing within time based on the quantitative analysis of dissimilarities in
the network structure.
Our main result is the common component evolution function (CCEF) which
characterizes network development over time. To test our approach we apply it
to several model systems, Erdős–Rényi networks, analytically derived
flow-based networks, and transient simulations from the START model for which
we control the change of single parameters over time. Then we construct
annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon
domain for the time period of 1970–2011 CE and use the CCEF to characterize
the temporal evolution in this region. While this real-world CCEF displays a
high degree of network persistence over large time lags, there are distinct
time periods when common links break down. This phasing of these events
coincides with years of strong El Niño/Southern Oscillation phenomena,
confirming previous studies. The proposed method can be applied for any type
of evolving network where the link but not the node set is changing, and may
be particularly useful to characterize nonstationary evolving systems using
complex networks. |
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