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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 1
Clim. Past, 6, 85–92, 2010
https://doi.org/10.5194/cp-6-85-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Clim. Past, 6, 85–92, 2010
https://doi.org/10.5194/cp-6-85-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Feb 2010

19 Feb 2010

Limitations of red noise in analysing Dansgaard-Oeschger events

H. Braun1,2, P. Ditlevsen1, J. Kurths3,4, and M. Mudelsee5 H. Braun et al.
  • 1Centre for Ice and Climate, Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, 2100 Copenhagen, Denmark
  • 2Heidelberg Academy of Sciences and Humanities, Im Neuenheimer Feld 229, 69120 Heidelberg, Germany
  • 3Institute of Physics, Humboldt University Berlin, Newtonstraße 15, 12489 Berlin, Germany
  • 4Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412 Potsdam, Germany
  • 5Climate Risk Analysis, Schneiderberg 26, 30167 Hannover, Germany

Abstract. During the last glacial period, climate records from the North Atlantic region exhibit a pronounced spectral component corresponding to a period of about 1470 years, which has attracted much attention. This spectral peak is closely related to the recurrence pattern of Dansgaard-Oeschger (DO) events. In previous studies a red noise random process, more precisely a first-order autoregressive (AR1) process, was used to evaluate the statistical significance of this peak, with a reported significance of more than 99%. Here we use a simple mechanistic two-state model of DO events, which itself was derived from a much more sophisticated ocean-atmosphere model of intermediate complexity, to numerically evaluate the spectral properties of random (i.e., solely noise-driven) events. This way we find that the power spectral density of random DO events differs fundamentally from a simple red noise random process. These results question the applicability of linear spectral analysis for estimating the statistical significance of highly non-linear processes such as DO events. More precisely, to enhance our scientific understanding about the trigger of DO events, we must not consider simple "straw men" as, for example, the AR1 random process, but rather test against realistic alternative descriptions.

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