A Bayesian spatiotemporal model for reconstructing climate from multiple pollen records

TL;DR

This paper introduces a Bayesian spatiotemporal model that integrates multiple pollen records to produce more accurate and realistic climate reconstructions over the Holocene in northern Europe.

Contribution

It presents a novel Bayesian approach that models shared environmental responses and spatial dependence, improving the quality of climate reconstructions from pollen data.

Findings

Reconstructed temperature histories are consistent with existing methods.

The approach yields smoother and less uncertain climate reconstructions.

Results demonstrate the model's ability to produce more realistic climate histories.

Abstract

Holocene (the last 12,000 years) temperature variation, including the transition out of the last Ice Age to a warmer climate, is reconstructed at multiple locations in southern Finland, Sweden and Estonia based on pollen fossil data from lake sediment cores. A novel Bayesian statistical approach is proposed that allows the reconstructed temperature histories to interact through shared environmental response parameters and spatial dependence. The prior distribution for past temperatures is partially based on numerical climate simulation. The features in the reconstructions are consistent with the quantitative climate reconstructions based on more commonly used reconstruction techniques. The results suggest that the novel spatio-temporal approach can provide quantitative reconstructions that are smoother, less uncertain and generally more realistic than the site-specific individual reconstructions.