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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/cp-2019-60
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-2019-60
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 06 Jun 2019

Submitted as: research article | 06 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Climate of the Past (CP).

OPTiMAL: A new machine learning approach for GDGT-based palaeothermometry

Yvette L. Eley1, William Thompson2, Sarah E. Greene1, Ilya Mandel3,4, Kirsty Edgar1, James A. Bendle1, and Tom Dunkley Jones1 Yvette L. Eley et al.
  • 1School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, UK
  • 2School of Mathematics, University of Birmingham, Edgbaston, B15 2TT, UK
  • 3Institute of Gravitational Wave Astronomy, School of Physics and Astronomy, University of Birmingham, Edgbaston, B15 2TT, UK
  • 4Monash Centre for Astrophysics, School of Physics and Astronomy, Monash University, Clayton, Victoria 3800, Australia

Abstract. In the modern oceans, the relative abundances of Glycerol dialkyl glycerol tetraether (GDGTs) compounds produced by marine archaeal communities show a significant dependence on the local sea surface temperature at the site of formation. When preserved in ancient marine sediments, the measured abundances of these fossil lipid biomarkers thus have the potential to provide a geological record of long-term variability in planetary surface temperatures. Several empirical calibrations have been made between observed GDGT relative abundances in late Holocene core top sediments and modern upper ocean temperatures. These calibrations form the basis of the widely used TEX86 palaeothermometer. There are, however, two outstanding problems with this approach, first the appropriate assignment of uncertainty to estimates of ancient sea surface temperatures based on the relationship of the ancient GDGT assemblage to the modern calibration data set; and second, the problem of making temperature estimates beyond the range of the modern empirical calibrations (> 30 ºC). Here we apply modern machine-learning tools, including Gaussian Process Emulators and forward modelling, to develop a new mathematical approach we call OPTiMAL (Optimised Palaeothermometry from Tetraethers via MAchine Learning) to improve temperature estimation and the representation of uncertainty based on the relationship between ancient GDGT assemblage data and the structure of the modern calibration data set. We reduce the root mean square uncertainty on temperature predictions (validated using the modern data set) from ~ ±6 ºC using TEX86 based estimators to ±3.6 ºC using Gaussian Process estimators for temperatures below 30 ºC. We also provide a new but simple quantitative measure of the distance between an ancient GDGT assemblage and the nearest neighbour within the modern calibration dataset, as a test for significant non-analogue behaviour. Finally, we advocate against the use of temperature estimates beyond the range of the modern empirical calibration dataset, given the absence – to date – of a robust predictive biological model or extensive and reproducible mesocosm experimental data in this elevated temperature range.

Yvette L. Eley et al.
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Yvette L. Eley et al.
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