The European Physical Journal Special Topics

, Volume 226, Issue 15, pp 3273–3285 | Cite as

Visualizing driving forces of spatially extended systems using the recurrence plot framework

Regular Article
Part of the following topical collections:
  1. Challenges in the Analysis of Complex Systems: Prediction, Causality and Communication

Abstract

The increasing availability of highly resolved spatio-temporal data leads to new opportunities as well as challenges in many scientific disciplines such as climatology, ecology or epidemiology. This allows more detailed insights into the investigated spatially extended systems. However, this development needs advanced techniques of data analysis which go beyond standard linear tools since the more precise consideration often reveals nonlinear phenomena, for example threshold effects. One of these tools is the recurrence plot approach which has been successfully applied to the description of complex systems. Using this technique’s power of visualization, we propose the analysis of the local minima of the underlying distance matrix in order to display driving forces of spatially extended systems. The potential of this novel idea is demonstrated by the analysis of the chlorophyll concentration and the sea surface temperature in the Southern California Bight. We are able not only to confirm the influence of El Niño events on the phytoplankton growth in this region but also to confirm two discussed regime shifts in the California current system. This new finding underlines the power of the proposed approach and promises new insights into other complex systems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.P. Eckmann, S.O. Kamphorst, D. Ruelle, Europhys. Lett. 5, 973 (1987) ADSCrossRefGoogle Scholar
  2. 2.
    N. Marwan, M.C. Romano, M. Thiel, J. Kurths, Phys. Rep. 438, 237 (2007) ADSMathSciNetCrossRefGoogle Scholar
  3. 3.
    N. Marwan, J. Kurths, S. Foerster, Phys. Lett. A 379, 894 (2015) CrossRefGoogle Scholar
  4. 4.
    M. Riedl, N. Marwan, J. Kurths, Chaos: Interdiscip. J. Nonlinear Sci. 25, 123111 (2015) CrossRefGoogle Scholar
  5. 5.
    R.V. Donner, M. Small, J.F. Donges, N. Marwan, Y. Zou, R. Xiang, J. Kurths, Int. J. Bifurc. Chaos 21, 1019 (2011) CrossRefGoogle Scholar
  6. 6.
    M.C. Casdagli, Physica D 108, 12 (1997) ADSMathSciNetCrossRefGoogle Scholar
  7. 7.
    D. Wang, T.C. Gouhier, B.A. Menge, A.R. Ganguly, Nature 518, 390 (2015) ADSCrossRefGoogle Scholar
  8. 8.
    E.L. Venrick, Prog. Oceanogr. 104, 46 (2012) ADSCrossRefGoogle Scholar
  9. 9.
    M. Kahru, R.M. Kudela, M. Manzano-Sarabia, B.G. Mitchell, Deep Sea Res. II: Top. Stud. Oceanogr. 77, 89 (2012) ADSCrossRefGoogle Scholar
  10. 10.
    M. Kahru, R.M. Kudela, C.R. Anderson, B.G. Mitchell, IEEE Geosci. Remote Sens. Lett. 12, 2282 (2015) ADSCrossRefGoogle Scholar
  11. 11.
    W. Härdle, Applied Nonparametric Regression (No). 19 (Cambridge University Press, 1990) Google Scholar
  12. 12.
    B.W. Silverman, in Density Estimation for Statistics and Data Analysis (Chapman & Hall/CRC, London, 1986) pp. 42–43 Google Scholar
  13. 13.
    B. Peterson et al., State Calif. Curr. CalCOFI Rep. 47, 30 (2006) Google Scholar
  14. 14.
    E. Venrick et al., Calif. Curr. 44, 28 (2003) Google Scholar
  15. 15.
    A.W. Leising, CalCOFI Rep. 56, 31 (2015) Google Scholar
  16. 16.
    J. Overland, S. Rodionov, S. Minobe, N. Bond, Prog. Oceanogr. 77, 102 (2008) ADSCrossRefGoogle Scholar
  17. 17.
    R.A. Monserud, R. Leemans, Ecol. Modell. 62, 275 (1992) CrossRefGoogle Scholar
  18. 18.
    M. Nilsson, J.S. Bartunek, J. Nordberg, I. Claesson, in ICIP 2008 (IEEE, 2008), pp. 973–976 Google Scholar
  19. 19.
    K.E. Trenberth, Bull. Am. Meteorol. Soc. 78, 2771 (1997) ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Potsdam Institute for Climate Impact Research (PIK)PotsdamGermany
  2. 2.Humboldt-Universität zu BerlinBerlinGermany

Personalised recommendations