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Elastic Tie-Pointing—Transferring Chronologies between Records via a Gaussian Process

Published online by Cambridge University Press:  09 February 2016

Timothy J Heaton ,
Edouard Bard  and
Konrad A Hughen
Timothy J Heaton*
Affiliation:
School of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, United Kingdom
Edouard Bard
Affiliation:
CEREGE, Aix-Marseille University, CNRS, IRD, Collège de France, Technopole de l'Arbois BP 80, 13545 Aix en Provence Cedex 4, France
Konrad A Hughen
Affiliation:
Department of Marine Chemistry & Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA
*
Corresponding author. Email: t.heaton@sheffield.ac.uk.
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Abstract

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We consider a general methodology for the transferral of chronologies from a master reference record containing direct dating information to an undated record of interest that does not. Transferral is achieved through the identification, by an expert, of a series of tie-points within both records that are believed to correspond to approximately contemporaneous events. Through tying of the 2 records together at these points, the reference chronology is elastically deformed onto the undated record. The method consists of 3 steps: creation of an age-depth model for the reference record using its direct dating information; selection of the tie-points and translation of their age estimates from the reference to the undated record; and finally, creation of an age-depth model for the undated record using these uncertain tie-point age estimates. Our method takes full account of the uncertainties involved in all stages of the process to create a final chronology within the undated record that allows joint age estimates to be found together with their credible intervals. To achieve computational practicality, we employ a Gaussian process to create our age-depth models. Calculations can then be performed exactly without resort to extremely slow Monte Carlo methods involving multiple independent model fits that would be required by other age-depth models.

Type
Research Article
Information
Radiocarbon , Volume 55 , Issue 4: IntCal 13 , 2013 , pp. 1975 - 1997
Copyright
Copyright © 2013 by the Arizona Board of Regents on behalf of the University of Arizona 

References

Altabet, MA, Higginson, MJ, Murray, DW. 2002. The effect of millennial-scale changes in Arabian Sea denitrification on atmospheric CO2 . Nature 415(6868):159–62.CrossRefGoogle ScholarPubMed
Bard, E, Ménot, G, Rostek, F, Licari, L, Böning, P, Edwards, RL, Cheng, H, Wang, YJ, Heaton, TJ. 2013. Radiocarbon calibration/comparison records based on marine sediments from the Pakistan and Iberian margins. Radiocarbon 55(4), this issue.Google Scholar
Blaauw, M. 2012. Out of tune: the dangers of aligning proxy archives. Quaternary Science Reviews 36:38–49.CrossRefGoogle Scholar
Blaauw, M, Christen, JA. 2011. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Analysis 6(3):457–74.Google Scholar
Böning, P, Bard, E. 2009. Millennial/centennial-scale thermocline ventilation changes in the Indian Ocean as reflected by aragonite preservation and geochemical variations in Arabian Sea sediments. Geochimica et Cosmochimica Acta 73(22):6771–88.Google Scholar
Bronk Ramsey, C. 2008. Deposition models for chronological records. Quaternary Science Reviews 27(1–2):42–60.CrossRefGoogle Scholar
Edwards, RL, Cheng, H, Wang, YJ, Yuan, DX, Kelly, MJ, Kong, XG, Wang, XF, Burnett, A, Smith, E. 2013. A refined Hulu and Dongge Cave climate record and the timing of the climate change during the last glacial cycle. Earth and Planetary Science Letters, submitted.Google Scholar
Haam, E, Huybers, P. 2010. A test for the presence of covariance between time-uncertain series of data with application to the Dongge Cave speleothem and atmospheric radiocarbon records. Paleoceanography 25(2): PA2209, doi:10.1029/2008PA001713.CrossRefGoogle Scholar
Haslett, J, Parnell, AC. 2008. A simple monotone process with application to radiocarbon-dated depth chronologies. Journal of the Royal Statistical Society, Series C (Applied Statistics) 57(4):399–418.Google Scholar
Hogg, AG, Hua, Q, Blackwell, PG, Niu, M, Buck, CE, Guilderson, TP, Heaton, TJ, Palmer, JG, Reimer, PJ, Reimer, RW, Turney, CSM, Zimmerman, SRH. 2013. SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55(4), this issue.Google Scholar
Hughen, K, Lehman, S, Southon, J, Overpeck, J, Marchal, O, Herring, C, Turnbull, J. 2004. 14C activity and global carbon cycle changes over the past 50,000 years. Science 303(5655):202–7.Google Scholar
Hughen, K, Southon, J, Lehman, S, Bertrand, C, Turnbull, J. 2006. Marine-derived 14C calibration and activity record for the past 50,000 years updated from the Cariaco Basin. Quaternary Science Reviews 25(23–24):3216–27.Google Scholar
Lisiecki, LE, Lisiecki, PA. 2002. Application of dynamic programming to the correlation of paleoclimate records. Paleoceanography 17(4):1049–60.Google Scholar
Martinson, DG, Menke, W, Stoffa, P. 1982. An inverse approach to signal correlation. Journal of Geophysical Research 87(B6):4807–18.Google Scholar
Parnell, AC, Buck, CE, Doan, TK. 2011. A review of statistical chronology models for high-resolution, proxy-based Holocene palaeoenvironmental reconstruction. Quaternary Science Reviews 30(21–22):2948–60.CrossRefGoogle Scholar
Pichevin, L, Bard, E, Martinez, P, Billy, I. 2007. Evidence of ventilation changes in the Arabian Sea during the Late Quaternary: implication for denitrification and nitrous oxide emission. Global Biogeochemical Cycles 21(4): GB4008, doi:10.1029/2006GB002852.Google Scholar
R Core Team. 2013. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. URL: http://www.R-project.org.Google Scholar
Rasmussen, CE, Williams, CKI. 2006. Gaussian Processes for Machine Learning. Cambridge: MIT Press.Google Scholar
Reimer, PJ, Baillie, MGL, Bard, E, Bayliss, A, Beck, JW, Blackwell, PG, Bronk Ramsey, C, Buck, CE, Burr, GS, Edwards, RL, Friedrich, M, Grootes, PM, Guilderson, TP, Hajdas, I, Heaton, TJ, Hogg, AG, Hughen, KA, Kaiser, KF, Kromer, B, McCormac, FG, Manning, SW, Reimer, RW, Richards, DA, Southon, JR, Talamo, S, Turney, CSM, van der Plicht, J, Weyhenmeyer, CE. 2009. IntCal09 and Marine09 radiocarbon age calibration curves, 0–50,000 years cal BP. Radiocarbon 51(4):1111–50.Google Scholar
Reimer, PJ, Bard, E, Bayliss, A, Beck, JW, Blackwell, PG, Bronk Ramsey, C, Buck, CE, Cheng, H, Edwards, RL, Friedrich, M, Grootes, PM, Guilderson, TP, Haflidason, H, Hajdas, I, Hatté, C, Heaton, TJ, Hoffman, DL, Hogg, AG, Hughen, KA, Kaiser, KF, Kromer, B, Manning, SW, Niu, M, Reimer, RW, Richards, DA, Scott, EM, Southon, JR, Staff, RA, Turney, CSM, van der Plicht, J. 2013. IntCal13 and Marine 13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55(4), this issue.Google Scholar
Schulte, S, Rostek, F, Bard, E, Rullkötter, J, Marchal, O. 1999. Variations of oxygen-minimum and primary productivity recorded in sediments of the Arabian Sea. Earth and Planetary Science Letters 173(3):205–21.Google Scholar
Schulz, H, von Rad, U, Erlenkeuser, H. 1998. Correlation between Arabian Sea and Greenland climate oscillations of the past 110,000 years. Nature 393(6680):54–7.Google Scholar
Wang, YJ, Cheng, H, Edwards, RL, An, ZS, Wu, JY, Shen, C-C, Dorale, JA. 2001. A high-resolution absolute-dated Late Pleistocene monsoon record from Hulu Cave, China. Science 294(5550):2345–8.Google Scholar