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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 6
Clim. Past, 8, 1869–1879, 2012
https://doi.org/10.5194/cp-8-1869-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Clim. Past, 8, 1869–1879, 2012
https://doi.org/10.5194/cp-8-1869-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Nov 2012

Research article | 22 Nov 2012

Automated ice-core layer-counting with strong univariate signals

J. J. Wheatley1, P. G. Blackwell1, N. J. Abram2, J. R. McConnell3, E. R. Thomas4, and E. W. Wolff4 J. J. Wheatley et al.
  • 1School of Mathematics and Statistics, The University of Sheffield, UK
  • 2Research School of Earth Sciences, Australian National University, Australia
  • 3Desert Research Institute, 2215 Raggio Parkway, Reno, Nevada, 89512-1095, USA
  • 4British Antarctic Survey, Cambridge, UK

Abstract. We present an automated process for determining the annual layer chronology of an ice-core with a strong annual signal, utilising the hydrogen peroxide record from an Antarctic Peninsula ice-core as a test signal on which to count annual cycles and explain the methods. The signal is de-trended and normalised before being split into sections with a deterministic cycle count and those that need more attention. Possible reconstructions for the uncertain sections are determined which could be used as a visual aid for manual counting, and a simple method for assigning probability measures to each reconstruction is discussed. The robustness of this process is explored by applying it to versions of two different chemistry signals from the same stretch of the NGRIP (North Greenland Ice Core Project) ice-core, which shows more variation in annual layer thickness, with and without thinning to mimic poorer quality data. An adapted version of these methods is applied to the more challenging non-sea-salt sulphur signal from the same Antarctic Peninsula core from which the hydrogen peroxide signal was taken. These methods could readily be adapted for use on much longer datasets, thereby reducing manual effort and providing a robust automated layer-counting methodology.

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