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.

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.