A Model-Based Approach to Climate Reconstruction Using Tree-Ring Data

TL;DR

This paper introduces a Bayesian model-based method for reconstructing historical climate from tree-ring data, highlighting its flexibility and sensitivity to model assumptions compared to traditional approaches.

Contribution

It presents a novel Bayesian joint modeling framework for climate reconstruction using tree-ring data, emphasizing the impact of model specification on results.

Findings

Model-based approach captures climate variability effectively.

Reconstruction results are sensitive to model assumptions.

Traditional methods may obscure underlying model influences.

Abstract

Quantifying long-term historical climate is fundamental to understanding recent climate change. Most instrumentally recorded climate data are only available for the past 200 years, so proxy observations from natural archives are often considered. We describe a model-based approach to reconstructing climate defined in terms of raw tree-ring measurement data that simultaneously accounts for non-climatic and climatic variability. In this approach we specify a joint model for the tree-ring data and climate variable that we fit using Bayesian inference. We consider a range of prior densities and compare the modeling approach to current methodology using an example case of Scots pine from Tornetrask, Sweden to reconstruct growing season temperature. We describe how current approaches translate into particular model assumptions. We explore how changes to various components in the model-based approach affect the resulting reconstruction. We show that minor changes in model specification can have little effect on model fit but lead to large changes in the predictions. In particular, the periods of relatively warmer and cooler temperatures are robust between models, but the magnitude of the resulting temperatures are highly model dependent. Such sensitivity may not be apparent with traditional approaches because the underlying statistical model is often hidden or poorly described.