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

Abstract

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.

The DeepMIP contribution to PMIP4: methodologies for selection, compilation and analysis of latest Paleocene and early Eocene climate proxy data, incorporating version 0.1 of the DeepMIP database

“…If BAYSPAR is used, we recommend that researchers report the parameters used (the prior mean and standard deviation, the search tolerance, and the version of the calibration dataset) so that the results are reproducible. The issue of extrapolation beyond the temperature range of the modern GDGT calibration dataset is highlighted in Eley et al (2019), which also proposes an alternative calibration methodology.…”

“…Whilst some uncertainty may be alleviated by comparison with associated multiproxy data (e.g., Frieling et al, 2017;Tierney et al, 2017), TEX 86 values > 0.8 require extrapolation of the relationship between TEX 86 and temperature beyond the modern calibration dataset or incorporation of limited data from laboratory studies (e.g., Pitcher et al, 2009;Elling et al, 2015). Such high values almost certainly indicate hotter temperatures, but the degree of estimated warming depends on assumptions about the mathematical nature of the temperature-TEX 86 relationship (see below and discussion in Eley et al, 2019). C. J. Hollis et al: The DeepMIP contribution to PMIP4…”

Global mean surface temperature and climate sensitivity of the early Eocene Climatic Optimum (EECO), Paleocene–Eocene Thermal Maximum (PETM), and latest Paleocene

“…The DeepMIP database excludes samples with anomalous GDGT distributions (Hollis et al, 2019). However, a Gaussian pro-cess regression (GPR) model may help to better identify anomalous GDGT distributions in the sedimentary record using a nearest-neighbour distance metric (Eley et al, 2019). This methodology could be employed in future studies to further refine GDGT-based SST datasets, but this methodology is currently under review and is not considered here.…”

A new sea surface temperature proxy based on bacterial 3-hydroxy fatty acids

“…Recent culture studies (Elling et al, 2015) have found that there is not a common linear TEX 86 response to growth temperature in cultivated strains of Thaumarchaeota. Eley et al (2019) recently applied machine-learning tools (e.g., Gaussian Process Emulators) to the isoGDGT global calibration dataset and found a strong, underlying temperature-dependence, but also highlighted how fossil GDGT distribution patterns differed significantly from the modern especially in the Eocene and Cretaceous. Finally, the LDI index, derived from diols produced by microalgae, has been applied to reconstruct SSTs throughout the Quaternary (Naafs et al, 2012;Rodrigo-Gámiz et al, 2014).…”