Nonlinear Time Series Analysis in the Geosciences pp 327-353

Part of the Lecture Notes in Earth Sciences book series (LNEARTH, volume 112) | Cite as

Detecting Oscillations Hidden in Noise: Common Cycles in Atmospheric, Geomagnetic and Solar Data

  • Milan Paluš
  • Dagmar Novotná

Abstract

In this chapter we present a nonlinear enhancement of a linear method, the singular system analysis (SSA), which can identify potentially predictable or relatively regular processes, such as cycles and oscillations, in a background of colored noise. The first step in the distinction of a signal from noise is a linear transformation of the data provided by the SSA. In the second step, the dynamics of the SSA modes is quantified in a general, non-linear way, so that dynamical modes are identified which are more regular, or better predictable than linearly filtered noise. A number of oscillatory modes are identified in data reflecting solar and geomagnetic activity and climate variability, some of them sharing common periods.

Keywords

signal detection statistical testing Monte Carlo SSA sunspots geomagnetic activity NAO air temperature solar-terrestrial relations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Milan Paluš
    • 1
  • Dagmar Novotná
    • 2
  1. 1.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicCzech Republic
  2. 2.Institute of Atmospheric Physics, Academy of Sciences of the Czech RepublicCzech Republic

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