Abstract
Abstract. In the modern oceans, the relative abundances of glycerol dialkyl glycerol tetraether (GDGT) compounds produced by marine archaeal communities show a significant dependence on the local sea surface temperature at the site of deposition. 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 dataset, and second, the problem of making temperature estimates beyond the range of the modern empirical calibrations (> 30 ∘C). 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 dataset. We reduce the root mean square uncertainty on temperature predictions (validated using the modern dataset) from ∼ ±6 ∘C using TEX86-based estimators to ±3.6 ∘C using Gaussian process estimators for temperatures below 30 ∘C. We also provide a new 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.
Highlights
Glycerol dibyphytanyl glycerol tetraethers (GDGTs) are membrane lipids consisting of isoprenoid carbon skeletons ether-bound to glycerol (Schouten et al, 2013)
We hope that this study prompts further investigations that will improve these constraints for the use of GDGTs in deeptime palaeoclimate studies, where they clearly have substantial potential as temperature proxies
Temperature estimates based on fossil GDGT assemblages that are within range of, or similar to, modern GDGT calibration data, do, rest on a strong, underlying temperature dependence observed in the empirical data
Summary
Glycerol dibyphytanyl glycerol tetraethers (GDGTs) are membrane lipids consisting of isoprenoid carbon skeletons ether-bound to glycerol (Schouten et al, 2013). In modern marine core-top sediments, the relative abundance of GDGT compounds with more ring structures increases with the mean annual sea surface temperature (SST) of the overlying waters (Schouten et al, 2002). This trend is most likely driven by the need for increased cell membrane stability and rigidity at higher temperatures (Sinninghe Damsté et al, 2002). The use of empirical GDGT calibrations to infer ancient sea surface temperatures implicitly assumes that the relationships between mean annual SST and all other GDGT-influencing variables are invariant through time This assumption is inescapable until, and unless, a more complete biological mechanistic model of GDGT production emerges. We go on to derive a new machine learning approach “OPTiMAL” (Optimised Palaeothermometry from Tetraethers via MAchine Learning) for reconstructing SSTs from GDGT datasets, which outperforms previous GDGT palaeothermometers and includes robust error estimates that, for the first time, accounts for model uncertainty
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