Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Zeitschriftenartikel

Correlation networks from flows. The case of forced and time-dependent advection-diffusion dynamics

Urheber*innen
/persons/resource/tupikina.liubov

Tupikina,  Liubov
Potsdam Institute for Climate Impact Research;

Molkenthin,  N.
External Organizations;

López,  C.
External Organizations;

Hernández-García,  E.
External Organizations;

/persons/resource/Marwan

Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)

7210oa.pdf
(Verlagsversion), 2MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Tupikina, L., Molkenthin, N., López, C., Hernández-García, E., Marwan, N., Kurths, J. (2016): Correlation networks from flows. The case of forced and time-dependent advection-diffusion dynamics. - PloS ONE, 11, 4, e0153703.
https://doi.org/10.1371/journal.pone.0153703


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_20947
Zusammenfassung
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.