A Model-Based Approach to Climate Reconstruction Using Tree-Ring Data. Issue 513 (2nd January 2016)

• Record Type: Journal Article
• Authors: Schofield, Matthew R.; Barker, Richard J.; Gelman, Andrew; Cook, Edward R.; Briffa, Keith R.
• Is Part Of: Journal of the American Statistical Association. Volume 111:Issue 513(2016)
• Page Start: 93
• Page End: 106
• Publication Date: 2016-01-02
• Subjects: Bayesian hierarchical modeling; Dendrochronology; Model uncertainty; Statistical calibration; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2015.1110524
• Languages: English
• ISSNs: 0162-1459

Abstract

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 nonclimatic 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 Torneträsk, 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 is highly model dependent. Such sensitivity may not be apparent with traditional approaches because the underlying statistical model is often hidden or poorly described. Supplementary materials for this article are available online.