A Bayesian, spatially-varying calibration model for the TEX86 proxy

Jessica E. Tierney, Martin Patrick Tingley

Research output: Contribution to journalArticle

101 Citations (Scopus)

Abstract

TEX86 is an important proxy for constraining ocean temperatures in the Earth's past. Current calibrations, however, feature structured residuals indicative of a spatially-varying relationship between TEX86 and sea-surface temperatures (SSTs). Here we develop and apply a Bayesian regression approach to the TEX86-SST calibration that explicitly allows for model parameters to smoothly vary as a function of space, and considers uncertainties in the modern SSTs as well as in the TEX86-SST relationship. The spatially-varying model leads to larger uncertainties at locations that are data-poor, while Bayesian inference naturally propagates calibration uncertainty into the uncertainty in the predictions. Applications to both Quaternary and Eocene TEX86 data demonstrate that our approach produces reasonable results, and improves upon previous methods by allowing for probabilistic assessments of past temperatures. The scientific understanding of TEX86 remains imperfect, and the model presented here allows for predictions that implicitly account for the effects of environmental factors other than SSTs that lead to a spatially non-stationary TEX86-SST relationship.

Original languageEnglish (US)
Pages (from-to)83-106
Number of pages24
JournalGeochimica et Cosmochimica Acta
Volume127
DOIs
StatePublished - Feb 15 2014

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sea surface temperature
Calibration
calibration
Temperature
prediction
Eocene
environmental factor
Earth (planet)
Uncertainty
temperature

All Science Journal Classification (ASJC) codes

  • Geochemistry and Petrology

Cite this

Tierney, Jessica E. ; Tingley, Martin Patrick. / A Bayesian, spatially-varying calibration model for the TEX86 proxy. In: Geochimica et Cosmochimica Acta. 2014 ; Vol. 127. pp. 83-106.
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A Bayesian, spatially-varying calibration model for the TEX86 proxy. / Tierney, Jessica E.; Tingley, Martin Patrick.

In: Geochimica et Cosmochimica Acta, Vol. 127, 15.02.2014, p. 83-106.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Bayesian, spatially-varying calibration model for the TEX86 proxy

AU - Tierney, Jessica E.

AU - Tingley, Martin Patrick

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N2 - TEX86 is an important proxy for constraining ocean temperatures in the Earth's past. Current calibrations, however, feature structured residuals indicative of a spatially-varying relationship between TEX86 and sea-surface temperatures (SSTs). Here we develop and apply a Bayesian regression approach to the TEX86-SST calibration that explicitly allows for model parameters to smoothly vary as a function of space, and considers uncertainties in the modern SSTs as well as in the TEX86-SST relationship. The spatially-varying model leads to larger uncertainties at locations that are data-poor, while Bayesian inference naturally propagates calibration uncertainty into the uncertainty in the predictions. Applications to both Quaternary and Eocene TEX86 data demonstrate that our approach produces reasonable results, and improves upon previous methods by allowing for probabilistic assessments of past temperatures. The scientific understanding of TEX86 remains imperfect, and the model presented here allows for predictions that implicitly account for the effects of environmental factors other than SSTs that lead to a spatially non-stationary TEX86-SST relationship.

AB - TEX86 is an important proxy for constraining ocean temperatures in the Earth's past. Current calibrations, however, feature structured residuals indicative of a spatially-varying relationship between TEX86 and sea-surface temperatures (SSTs). Here we develop and apply a Bayesian regression approach to the TEX86-SST calibration that explicitly allows for model parameters to smoothly vary as a function of space, and considers uncertainties in the modern SSTs as well as in the TEX86-SST relationship. The spatially-varying model leads to larger uncertainties at locations that are data-poor, while Bayesian inference naturally propagates calibration uncertainty into the uncertainty in the predictions. Applications to both Quaternary and Eocene TEX86 data demonstrate that our approach produces reasonable results, and improves upon previous methods by allowing for probabilistic assessments of past temperatures. The scientific understanding of TEX86 remains imperfect, and the model presented here allows for predictions that implicitly account for the effects of environmental factors other than SSTs that lead to a spatially non-stationary TEX86-SST relationship.

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