Parnell, Andrew and Sweeney, James and Doan, Thinh K. and Salter-Townshend, Michael and Allen, Judy R. M. and Huntley, Brian and Haslett, John (2014) Bayesian Inference for Palaeoclimate with time Uncertainty and Stochastic Volatility. Journal of the Royal Statistical Society Series C: Applied Statistics, 64 (1). pp. 115-138. ISSN 0035-9254

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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.

Item Type:	Article
Keywords:	Hierarchical time series; Modular Bayes; Normal–inverse Gaussian process; Palaeoclimate reconstruction; Temporal uncertainty;
Academic Unit:	Faculty of Science and Engineering > Mathematics and Statistics Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	19112
Identification Number:	https://doi.org/10.1111/rssc.12065
Depositing User:	Andrew Parnell
Date Deposited:	29 Oct 2024 12:03
Journal or Publication Title:	Journal of the Royal Statistical Society Series C: Applied Statistics
Publisher:	The Royal Statistical Society
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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