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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 5
Nonlin. Processes Geophys., 18, 545–562, 2011
https://doi.org/10.5194/npg-18-545-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Recent advances in data analysis and modeling of nonlinear...

Nonlin. Processes Geophys., 18, 545–562, 2011
https://doi.org/10.5194/npg-18-545-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 05 Sep 2011

Research article | 05 Sep 2011

Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis

J. F. Donges1,2, R. V. Donner1,3, K. Rehfeld1,2, N. Marwan1, M. H. Trauth4, and J. Kurths1,2 J. F. Donges et al.
  • 1Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412 Potsdam, Germany
  • 2Department of Physics, Humboldt University Berlin, Newtonstr. 15, 12489 Berlin, Germany
  • 3Institute for Transport and Economics, Dresden University of Technology, Würzburger Str. 35, 01187 Dresden, Germany
  • 4Department of Geosciences, University of Potsdam, Karl-Liebknecht-Str. 24, 14476 Potsdam, Germany

Abstract. The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks – a recently developed approach – are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods.

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