Bayesian Palaeoclimate Inference from Pollen in Southern Italy

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 observation equation linking climate to proxy data is defined by a pre-calibrated forward model, created via a multivariate latent Gaussian process fitted via integrated nested Laplace approximation (INLA). The data for this forward model are taken from a calibration set of modern climate-pollen relationships. The evolution equation representing climate change is driven by a Normal-Inverse Gaussian Lévy 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 nuisance parameters 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 Lake Monticchio in south-central Italy