On Bayesian Modelling of the Uncertainties in Palaeoclimate Reconstruction

TL;DR

This paper presents a Bayesian state space model for palaeoclimate reconstruction using pollen data, incorporating a novel Levy process to model climate volatility and an efficient MCMC algorithm for inference.

Contribution

It introduces a new multivariate non-linear non-Gaussian model with a Levy process for climate change and a minimal-assumption inference method that integrates out the state variable.

Findings

Efficient MCMC algorithm demonstrated on real pollen data.

Model captures large climate jumps with temporal consistency.

Method provides probabilistic climate reconstructions with uncertainty quantification.

Abstract

We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate non-linear non-Gaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving climate change over time. The link from climate to proxy data is defined by a pre-calibrated forward model, as developed in Salter-Townshend and Haslett (2012) and Sweeney (2012). Climatic change is represented by a temporally-uncertain Normal-Inverse Gaussian Levy process, being able to capture large jumps in multivariate climate whilst remaining temporally consistent. The pre-calibrated nature of the forward model allows us to cut feedback between the observation and evolution equations and thus integrate out the state variable entirely whilst making minimal simplifying assumptions. A key part of this approach is the creation of mixtures of marginal data posteriors representing the information obtained about climate from each individual time point. Our approach allows for an extremely efficient MCMC algorithm, which we demonstrate with a pollen core from Sluggan Bog, County Antrim, Northern Ireland.