Quantitative estimates of past temperature changes are a cornerstone of paleoclimatology. For a number of marine sediment-based proxies, the accuracy and precision of past temperature reconstructions depends on a spatial calibration of modern surface sediment measurements to overlying water temperatures. Here, we present a database of 1095 surface sediment measurements of TEX86, a temperature proxy based on the relative cyclization of marine archaeal glycerol dialkyl glycerol tetraether (GDGT) lipids. The dataset is archived in a machine-readable format with geospatial information, fractional abundances of lipids (if available), and metadata. We use this new database to update surface and subsurface temperature calibration models for TEX86 and demonstrate the applicability of the TEX86 proxy to past temperature prediction. The TEX86 database confirms that surface sediment GDGT distribution has a strong relationship to temperature, which accounts for over 70% of the variance in the data. Future efforts, made possible by the data presented here, will seek to identify variables with secondary relationships to GDGT distributions, such as archaeal community composition.
Background & Summary
The reconstruction of past changes in ocean temperatures allows us to understand the behavior of the Earth’s climate system, including internal and external drivers of oceanic variability, climate sensitivity, and ocean-atmosphere interactions. A number of techniques are available to infer past temperatures from ocean sediment archives, some employing the inorganic chemical composition of calcified fossils1,2, and others the distribution of fossil lipids, or ‘biomarkers’, produced by specific organisms3,4. The latter category includes the TEX86 (TetraEther indeX of 86 carbons) proxy, based on the relative cyclization of isoprenoidal glycerol dialkyl glycerol tetraethers (GDGTs) produced by marine archaea. GDGTs are cell membrane lipids, and archaea alter the composition of these lipids in response to environmental temperature in order to optimize membrane packing and fluidity5,
TEX86 is an index designed to quantify the relative degree of cyclization4. It is defined as: where GDGTs 1–3 are compounds containing 1–3 cyclopentyl moieties, respectively, and cren' denotes the regioisomer of crenarchaeol, a characteristic lipid for Thaumarchaeota10. By definition, values of the TEX86 index span 0–1. Fig. 1a shows the structures of these compounds. GDGTs are analyzed via High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS); Fig. 1b shows their typical appearance in a HPLC-MS chromatogram.
Pelagic, nitrifying Thaumarchaeota are believed to be the primary10,11, but likely not exclusive12 producers of GDGTs in the marine environment. These organisms typically inhabit the upper water column, but may reside anywhere in the epi- or meso-pelagic zone13,
Calibration of the TEX86 index to temperatures relies on a collection of modern surface sediments, for which overlying water temperatures are known from historical observations4,16,
TEX86 data (n=1095) were collated from the literature and from direct contact with individual researchers (Fig. 2). Our collection includes data represented in previous global calibration efforts16,
Analytical determination of TEX86
Although the data in this collection derive from multiple publications and laboratories, TEX86 values were determined using the same HPLC-MS analysis method49. Briefly, extracts of sediment material containing GDGTs were dissolved in a mixture of hexane and isopropanol, injected into a HPLC, then separated on a Prevail Cyano column using a gradient spanning hexane:isopropanol (99:1) to hexane:isopropanol (98.2:1.8). The solvent stream is then sent to a mass spectrometer operated in single-ion monitoring (SIM) mode, scanning only target compound mass-to-charge ratios. The type of mass spectrometer (e.g., single quadrupole, ion trap) may be different between laboratories, but previous research has shown that there is no bias in TEX86 associated with different types of mass spectrometers50. TEX86 is calculated from integrating peak areas of the target compounds. Within a single laboratory, analytical error is typically 0.004 TEX86 units or better50,51, or about 0.3 °C when calibrated. Interlaboratory uncertainties are nearly an order-of-magnitude larger (0.03 TEX86 units50,51), equivalent to about 2–3 °C.
BAYSPAR calibration model
We have previously developed a Bayesian, spatially-varying regression (BAYSPAR) model18 for the calibration of TEX86. The adoption of this model was motivated by observations that the TEX86 response to temperature varies across different oceanic basins and environments26,35, the existence of strong spatial trends in the residuals of previous calibration models18,19, and the need to fully propagate uncertainties into resulting temperature predictions.
BAYSPAR assumes the regression parameters are constant within 20° by 20° latitude-longitude grid boxes, but imposes a spatial model on the intercepts (the vector α) and slopes (β) that forces nearby grid boxes to feature similar parameter values, with the degree of similarity controlled by a data-informed spatial decorrelation length scale. This hierarchical approach produces a calibration that is a data-determined compromise between a globally constant calibration and a set of independent local calibrations52,53. The calibration model is specified via the following set of equations:
The vector P consists of all core-top TEX86 observations; C is a diagonal matrix containing all temperature observations; and M is a selection matrix of zeros and ones, with each row containing a single one, such that corresponding entries of the vectors MCβ and P are at the same location in space. α and β are, respectively, vectors of spatially varying intercept and slope terms; along with the error variance, τ
2 (I denotes the identity matrix), they are the parameters of primary interest in calibrating the TEX86–temperature relationship. Spatial dependence arises from the specification of both α and β as stationary and isotropic Gaussian processes in space, defined on the centroids of 20° by 20° grid boxes, and with constant means given by μ
, respectively. indicates a truncated normal, defined on the positive half of the real line, reflecting the a priori assumption of a positive relationship between TEX86 and temperatures. Finally, R denotes the Matérn correlation function53, defined by a smoothness parameter ν, which we set to 3/2, and an inverse spatial range parameter, ϕ, that measures the strength of the spatial dependence. To provide mathematical closure, priors are required for all scalar parameters of the calibration model. With the exception of ϕ, which can be challenging to estimate54,
Prediction of temperature conditional on an observed TEX86 value proceeds by a second application of Bayes rule to invert Equation 2 for temperature in terms of TEX86. A prior distribution on the temperature is also required, and, to propagate uncertainty, we integrate over the posterior distributions of the calibration parameters. In practice, this is achieved by repeatedly sampling from the posterior distributions of the calibration parameters, and then drawing from the posterior predictive distribution of temperatures conditional on the TEX86 observation, the current draw of the calibration parameters, and the prior on past temperature.
Under certain oceanographic conditions, TEX86 may be recording subsurface, rather than surface, temperature variability11,19,22,
The TEX86 surface sediment database is archived at the National Oceanic and Atmospheric Administration's National Climatic Data Center for Paleoclimatology: http://www.ncdc.noaa.gov/paleo/study/18615 in machine-readable ASCII format (http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/contributing). The database is also archived on Figshare (Data Citation 1: Figshare http://dx.doi.org/10.6084/m9.figshare.1348830). Each data entry includes the following information:
Geospatial information, including latitude, longitude and (if available) recorded water depth at the collection site.
Sediment core information, including the name of the core, type of core (e.g., gravity, piston), and depth at which the TEX86 sample was taken.
TEX86 value and (if available) fractional abundances of the six main isoprenoidal GDGTs.
Overlying sea-surface temperatures and gamma-averaged (Fig. 3) subsurface temperatures derived from the 1°×1° World Ocean Atlas 2009 product59 (https://www.nodc.noaa.gov/OC5/WOA09/pr_woa09.html) and sea-surface temperatures from the 0.25°×0.25° NOAA daily Optimum Interpolation Sea Surface Temperature (OISST) 1981–present climatology based on Advanced Very-High Resolution Radiometer (AVHRR) measurements60 (http://www.ncdc.noaa.gov/oisst).
Name and DOI of the associated reference, if available.
The database includes all available sedimentary TEX86 data as of January 2015. This version of the database and the accompanying calibrations is designed as version 1.0. The authors will update the database, and the BAYSPAR calibrations, yearly with newly published sediment core top data; previous versions of the database and calibrations will be archived at the NCDC for posterity.
The new TEX86 data compilation shows a clear relationship with both SST and Sub-T (subsurface temperatures), which respectively account for 72 and 73% of the variance in the TEX86 data (Fig. 4). The relationship is not straightforwardly linear due to regional differences in the TEX86-temperature slope. In particular, and in agreement with previous findings18,35, the TEX86-temperature relationship features a lower slope at higher latitudes, and there is more scatter about the regression relationship in the Arctic region (Fig. 4). The reasons for the poor relationship between TEX86 and temperatures in the Arctic remain unclear. In some locations it may reflect interference from terrestrial or sedimentary methanogenic/methanotrophic sources of GDGTs35 but could also plausibly indicate the presence of different pelagic archaeal producers. Whatever the case, the scatter in the data and the subsequent collapse in predictability18 justify their current exclusion from global calibration models.
In agreement with our previous work18, both the SST and Sub-T (subsurface temperature) BAYSPAR calibrations show spatial variation in the α (intercept) and β (slope) parameters that reflect the regional differences in the TEX86 response to temperature variations (Fig. 5). Globally, for the SST (Sub-T) model, β varies by 30% (22%) and α varies by 22% (10%). The relatively smaller variance of the parameters in the Sub-T model, particularly in the case of α, may indicate a slightly less globally-variable TEX86 response when calibrating to a deeper water temperature.
Calibration uncertainties vary spatially as a function of data availability, and as a function of β, with lower β values associated with higher uncertainties (Fig. 6). For the SST model, calibration uncertainties vary between 1.2–10 °C with a median of 5 °C; for the Sub-T model, they vary between 1.4–9 °C with a median of 5 °C. Unlike the existing least squares calibrations16,17,61, and in agreement with our previous calibrations18, we do not detect any significant trends in the residuals as a function of latitude (for the SST model, ρ=−0.07, P=0.11, while for the sub T model, ρ=−0.05, P=0.27, where ρ is the Spearman correlation).
We provide an example application of the new BAYSPAR calibration, based on the updated TEX86 core top dataset, to demonstrate applicability and usage (Fig. 7). In this case, we apply the SST calibration to predict SSTs for the past 25,000 years at a site in the eastern Mediterranean44. We find that the predicted temperatures are in reasonable agreement with independent alkenone-based SST estimates down core (Fig. 7a), indicating that the use of an SST model at this site is appropriate. One advantage of our Bayesian approach is that predictions take the form of posterior probability distributions as opposed to single time series with error bars (Fig. 7a). Probabilistic reconstructions of this form permit for a statistically rigorous assessment of a much broader array of scientific issues62,
The performance of our new BAYSPAR calibrations and their application demonstrate the general ability of the new TEX86 database to provide predictions of past changes in both surface and subsurface temperatures. The choice of whether to calibrate to surface or sub-surface temperatures is ultimately up to the user, although we recommend that it be informed not only by the target variable that the user seeks to predict but also an understanding of the oceanography of the location from which the data derive. As previous investigations have shown22,28, a Sub-T calibration is likely the most suitable choice for regions with steep thermoclines and nutriclines, such as upwelling zones. The database may also foster future investigations into secondary influences on the distribution of isoprenoidal GDGTs in marine sediments, such as lipid contributions from different archaeal communities12,66,67.
How to cite this article: Tierney, J. E. & Tingley, M. P. A TEX86 surface sediment database and extended Bayesian calibration. Sci. Data 2:150029 doi: 10.1038/sdata.2015.29 (2015).
Tierney, J. E., & Tingley, M. P. Figshare http://dx.doi.org/10.6084/m9.figshare.1348830 (2015)
The authors would like to thank Laura Fleming for assistance with the development of the BAYSPAR website.
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.