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Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility

Parnell, A.C.; Sweeney, J.; Doan, T.K.; Salter-Townshend, M.; Allen, J.R.M.; Huntley, B.; Haslett, J.

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Authors

A.C. Parnell

J. Sweeney

T.K. Doan

M. Salter-Townshend

B. Huntley

J. Haslett



Abstract

We propose and fit a Bayesian model to infer palaeoclimate over several thousand years. The data that we use arise as ancient pollen counts taken from sediment cores together with radiocarbon dates which provide (uncertain) ages. When combined with a modern pollen–climate data set, we can calibrate ancient pollen into ancient climate. We use a normal–inverse Gaussian process prior to model the stochastic volatility of palaeoclimate over time, and we present a novel modularized Markov chain Monte Chain algorithm to enable fast computation. We illustrate our approach with a case-study from Sluggan Moss, Northern Ireland, and provide an R package, Bclim, for use at other sites.

Citation

Parnell, A., Sweeney, J., Doan, T., Salter-Townshend, M., Allen, J., Huntley, B., & Haslett, J. (2015). Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility. Journal of the Royal Statistical Society: Series C, 64(1), 115-138. https://doi.org/10.1111/rssc.12065

Journal Article Type Article
Publication Date Jan 1, 2015
Deposit Date Jan 26, 2015
Publicly Available Date Feb 6, 2015
Journal Journal of the Royal Statistical Society: Series C
Print ISSN 0035-9254
Electronic ISSN 1467-9876
Publisher Royal Statistical Society
Peer Reviewed Peer Reviewed
Volume 64
Issue 1
Pages 115-138
DOI https://doi.org/10.1111/rssc.12065
Keywords Hierarchical time series, Modular Bayes, Normal–inverse Gaussian process, Palaeoclimate reconstruction, Temporal uncertainty.

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Accepted Journal Article (861 Kb)
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Copyright Statement
This is the accepted version of the following article: Parnell, A. C., Sweeney, J., Doan, T. K., Salter-Townshend, M., Allen, J. R. M., Huntley, B. and Haslett, J. (2015), Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility. Journal of the Royal Statistical Society. Series C, Applied Statistics, 64 (1): 115-138, which has been published in final form at http://dx.doi.org/10.1111/rssc.12065. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.





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