Finding a consensus on credible features among several paleoclimate reconstructions

TL;DR

This paper introduces a Bayesian method to merge multiple noisy, irregularly sampled paleoclimate reconstructions into a consensus time series, effectively capturing credible features across different time scales.

Contribution

It presents a novel Bayesian inference approach for combining diverse paleoclimate data, accounting for uncertainties and temporal errors, to identify shared climate features.

Findings

Successfully applied to Holocene temperature reconstructions from Finnish Lapland.

Effectively captures credible climate features across multiple time scales.

Method adaptable to other noisy, irregular time series contexts.

Abstract

We propose a method to merge several paleoclimate time series into one that exhibits a consensus on the features of the individual times series. The paleoclimate time series can be noisy, nonuniformly sampled and the dates at which the paleoclimate is reconstructed can have errors. Bayesian inference is used to model the various sources of uncertainty and smoothing of the posterior distribution of the consensus is used to capture its credible features in different time scales. The technique is demonstrated by analyzing a collection of six Holocene temperature reconstructions from Finnish Lapland based on various biological proxies. Although the paper focuses on paleoclimate time series, the proposed method can be applied in other contexts where one seeks to infer features that are jointly supported by an ensemble of irregularly sampled noisy time series.