A global Bayesian temperature calibration for lacustrine brGDGTs

P. Martínez-Sosa, J.E. Tierney, I.C. Stefanescu, E. Dearing Crampton-Flood, B.N. Shuman, C. Routson

Research output: Contribution to journalArticlepeer-review

Abstract

Despite widespread use of branched glycerol dialkyl glycerol tetraethers (brGDGTs) for paleo-temperature reconstruction, no global calibration for their application in lakes has been generated since improved analytical methods have allowed for the separation of the structural isomers. This is a substantial obstacle for the application of this tool as soil calibrations underestimate temperature values when applied to lake sediments. Here, we present a comprehensive dataset (N=272) of lacustrine brGDGT distributions, consisting of both new and previously reported samples, spanning a wide range of geographical locations, air temperatures, and lakewater pH values. Empirical Orthogonal Function (EOF) analysis on the fractional abundance of brGDGTs indicates that temperature exerts a strong control (explaining 58% of the variance) on brGDGT distribution. The influence of water chemistry is weaker, with pH and conductivity explaining 24% of the variance. We use our dataset to generate a new Bayesian calibration to the mean temperature of Months Above Freezing (MAF), which has an R2=0.82 and RMSE = 2.9°C. Application of the new temperature calibration to a previously published lake core record spanning the last 900 years demonstrates that it generates values comparable to instrumental observations of MAF. Our new calibration facilitates the use of lacustrine brGDGTs to reconstruct continental temperatures, a vital piece of information for understanding past climates. © 2021 Elsevier Ltd
Original languageEnglish (US)
Pages (from-to)87-105
Number of pages19
JournalGeochimica et Cosmochimica Acta
Volume305
DOIs
StatePublished - Jul 15 2021

Bibliographical note

Funding Information:
We thank the LacCore facility for providing the samples used in this work. We also acknowledge Patrick Murphy for his assistance with the lipid analysis, and Darrell Kaufman for making the Alaskan surface sediments available. This research was funded by the National Science Foundation, grant EAR-1603674 and grant EPS-1655726, and by CONACYT through the student scholarship 440897.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Bayesian statistics
  • Calibration
  • Lakes
  • brGDGTs

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