Articles | Volume 23, issue 1
Research article
29 Feb 2016
Research article |  | 29 Feb 2016

A sequential Bayesian approach for the estimation of the age–depth relationship of the Dome Fuji ice core

Shin'ya Nakano, Kazue Suzuki, Kenji Kawamura, Frédéric Parrenin, and Tomoyuki Higuchi

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Cited articles

Andrieu, C., Doucet, A., and Holenstein, R.: Particle Markov chain Monte Carlo methods, J. Roy. Statist. Soc. B, 72, 269–342, 2010.
Doucet, A., de Freitas, N., and Gordon, N. (Eds.): Sequential Monte Carlo methods in practice, Springer-Verlag, New York, 2001.
Dreyfus, G. B., Parrenin, F., Lemieux-Dudon, B., Durand, G., Masson-Delmotte, V., Jouzel, J., Barnola, J.-M., Panno, L., Spahni, R., Tisserand, A., Siegenthaler, U., and Leuenberger, M.: Anomalous flow below 2700 m in the EPICA Dome C ice core detected using d18O of atmospheric oxygen measurements, Clim. Past, 3, 341–353,, 2007.
Freitag, J., Kipfstuhl, S., and Laepple, T.: Core-scale radioscopic imaging: a new method reveals density–calcium link in Antarctic firn, J. Glaciology, 59, 1009–1014,, 2013.
Gordon, N. J., Salmond, D. J., and Smith, A. F. M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F, 140, 107–113, 1993.
Short summary
This paper proposes a technique for dating an ice core. The proposed technique employs a hybrid method combining the sequential Monte Carlo method and the Markov chain Monte Carlo method, which is referred to as the particle Markov chain Monte Carlo method. The sequential Monte Carlo method, which is also known as the particle filter, is widely used for nonlinear time-series analysis. This paper demonstrates the usefulness of the approach in time-series analysis for dating an ice core.