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A Model-Based Approach to Climate Reconstruction Using Tree-Ring Data
Univ Kentucky, Dept Stat, Lexington, KY 40506 USA.;Univ Otago, Dept Math & Stat, Dunedin, New Zealand..ORCID iD: 0000-0003-1481-2766
Univ Otago, Dept Math & Stat, POB 56, Dunedin, New Zealand..
Columbia Univ, Dept Stat, New York, NY 10027 USA..
Lamont Doherty Earth Observ, Palisades, NY 10964 USA..
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2016 (English)In: Journal of the American Statistical Association, ISSN 0162-1459, E-ISSN 1537-274X, Vol. 111, no 513, 93-106 p.Article in journal (Refereed) Published
Abstract [en]

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 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 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.

Place, publisher, year, edition, pages
2016. Vol. 111, no 513, 93-106 p.
Keyword [en]
Bayesian hierarchical modeling, Dendrochronology, Model uncertainty, Statistical calibration
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:polar:diva-3430DOI: 10.1080/01621459.2015.1110524ISI: 000376031000007OAI: oai:DiVA.org:polar-3430DiVA: diva2:1079093
Available from: 2017-03-07 Created: 2017-03-07 Last updated: 2017-03-07

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