Articles | Volume 18, issue 6
https://doi.org/10.5194/cp-18-1275-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-18-1275-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2022
Research article |
| 20 Jun 2022
| 20 Jun 2022
Comprehensive uncertainty estimation of the timing of Greenland warmings in the Greenland ice core records
Eirik Myrvoll-Nilsen
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Keno Riechers
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Earth System Modelling, School of Engineering & Design, Technical University of Munich, Munich, Germany
Martin Wibe Rypdal
Department of Mathematics and Statistics, The University of Tromsø – The Arctic University of Norway, Tromsø, Norway
Niklas Boers
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Earth System Modelling, School of Engineering & Design, Technical University of Munich, Munich, Germany
Department of Mathematics, Global Systems Institute, University of Exeter, Exeter, UK
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Martin Rypdal and Kristoffer Rypdal
Earth Syst. Dynam., 7, 281–293, https://doi.org/10.5194/esd-7-281-2016, https://doi.org/10.5194/esd-7-281-2016, 2016
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We analyse scaling in temperature signals for the late quaternary climate, and focus on the effects of regime shifting events such as the Dansgaard-Oeschger cycles and the shifts between glacial and interglacial conditions. When these events are omitted from a scaling description the climate noise is consistent with a 1/f law on timescales from months to 105 years. If the events are included in the description, we obtain a model that is inherently non-stationary.
L. Østvand, T. Nilsen, K. Rypdal, D. Divine, and M. Rypdal
Earth Syst. Dynam., 5, 295–308, https://doi.org/10.5194/esd-5-295-2014, https://doi.org/10.5194/esd-5-295-2014, 2014
L. Østvand, K. Rypdal, and M. Rypdal
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esdd-5-327-2014, https://doi.org/10.5194/esdd-5-327-2014, 2014
Revised manuscript not accepted
Related subject area
Subject: Ice Dynamics | Archive: Ice Cores | Timescale: Millenial/D-O
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Tyler J. Fudge, David A. Lilien, Michelle Koutnik, Howard Conway, C. Max Stevens, Edwin D. Waddington, Eric J. Steig, Andrew J. Schauer, and Nicholas Holschuh
Clim. Past, 16, 819–832, https://doi.org/10.5194/cp-16-819-2020, https://doi.org/10.5194/cp-16-819-2020, 2020
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Johannes Lohmann and Peter D. Ditlevsen
Clim. Past, 15, 1771–1792, https://doi.org/10.5194/cp-15-1771-2019, https://doi.org/10.5194/cp-15-1771-2019, 2019
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T. J. Fudge, E. D. Waddington, H. Conway, J. M. D. Lundin, and K. Taylor
Clim. Past, 10, 1195–1209, https://doi.org/10.5194/cp-10-1195-2014, https://doi.org/10.5194/cp-10-1195-2014, 2014
D. Veres, L. Bazin, A. Landais, H. Toyé Mahamadou Kele, B. Lemieux-Dudon, F. Parrenin, P. Martinerie, E. Blayo, T. Blunier, E. Capron, J. Chappellaz, S. O. Rasmussen, M. Severi, A. Svensson, B. Vinther, and E. W. Wolff
Clim. Past, 9, 1733–1748, https://doi.org/10.5194/cp-9-1733-2013, https://doi.org/10.5194/cp-9-1733-2013, 2013
E. Capron, A. Landais, D. Buiron, A. Cauquoin, J. Chappellaz, M. Debret, J. Jouzel, M. Leuenberger, P. Martinerie, V. Masson-Delmotte, R. Mulvaney, F. Parrenin, and F. Prié
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Cited articles
Adolphi, F., Bronk Ramsey, C., Erhardt, T., Edwards, R. L., Cheng, H., Turney, C. S. M., Cooper, A., Svensson, A., Rasmussen, S. O., Fischer, H., and Muscheler, R.: Connecting the Greenland ice-core and U∕Th timescales via cosmogenic radionuclides: testing the synchroneity of Dansgaard–Oeschger events, Clim. Past, 14, 1755–1781, https://doi.org/10.5194/cp-14-1755-2018, 2018. a
Andersen, K. K., Svensson, A., Johnsen, S. J., Rasmussen, S. O., Bigler, M.,
Röthlisberger, R., Ruth, U., Siggaard-Andersen, M. L., Peder
Steffensen, J., Dahl-Jensen, D., Vinther, B. M., and Clausen, H. B.: The
Greenland Ice Core Chronology 2005, 15–42 ka. Part 1: constructing the time
scale, Quaternary Sci. Rev., 25, 3246–3257,
https://doi.org/10.1016/j.quascirev.2006.08.002, 2006. a, b, c, d, e, f, g
Blaauw, M. and Christeny, J. A.: Flexible paleoclimate age-depth models using
an autoregressive gamma process, Bayesian Anal., 6, 457–474,
https://doi.org/10.1214/11-BA618, 2011. a
Boers, N., Goswami, B., and Ghil, M.: A complete representation of uncertainties in layer-counted paleoclimatic archives, Clim. Past, 13, 1169–1180, https://doi.org/10.5194/cp-13-1169-2017, 2017. a, b
Buizert, C., Cuffey, K. M., Severinghaus, J. P., Baggenstos, D., Fudge, T. J., Steig, E. J., Markle, B. R., Winstrup, M., Rhodes, R. H., Brook, E. J., Sowers, T. A., Clow, G. D., Cheng, H., Edwards, R. L., Sigl, M., McConnell, J. R., and Taylor, K. C.: The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference, Clim. Past, 11, 153–173, https://doi.org/10.5194/cp-11-153-2015, 2015. a, b, c, d
Capron, E., Rasmussen, S. O., Popp, T. J., Erhardt, T., Fischer, H., Landais,
A., Pedro, J. B., Vettoretti, G., Grinsted, A., Gkinis, V., Vaughn, B.,
Svensson, A., Vinther, B. M., and White, J. W.: The anatomy of past abrupt
warmings recorded in Greenland ice, Nat. Commun., 12, 2106,
https://doi.org/10.1038/s41467-021-22241-w, 2021. a, b, c, d, e
Comboul, M., Emile-Geay, J., Evans, M. N., Mirnateghi, N., Cobb, K. M., and Thompson, D. M.: A probabilistic model of chronological errors in layer-counted climate proxies: applications to annually banded coral archives, Clim. Past, 10, 825–841, https://doi.org/10.5194/cp-10-825-2014, 2014. a, b
Corrick, E. C., Drysdale, R. N., Hellstrom, J. C., Capron, E., Rasmussen,
S. O., Zhang, X., Fleitmann, D., Couchoud, I., Wolff, E., and Monsoon, S. A.:
Synchronous timing of abrupt climate changes during the last glacial
period, Science, 369, 963–969, 2020. a
Dansgaard, W., Johnsen, S. J., Clausen, H. B., Dahl-Jensen, D., Gundestrup,
N. S., Hammer, C. U., Hvidberg, C. S., Steffensen, J. P.,
Sveinbjörnsdottir, A. E., Jouzel, J., and Bond, G.: Evidence for
general instability of past climate from a 250-kyr ice-core record, Nature,
364, 218–220, https://doi.org/10.1038/364218a0, 1993. a, b
Erhardt, T., Capron, E., Rasmussen, S. O., Schüpbach, S., Bigler, M., Adolphi, F., and Fischer, H.: Decadal-scale progression of the onset of Dansgaard–Oeschger warming events, Clim. Past, 15, 811–825, https://doi.org/10.5194/cp-15-811-2019, 2019. a, b, c
Gkinis, V., Simonsen, S. B., Buchardt, S. L., White, J. W., and Vinther, B. M.:
Water isotope diffusion rates from the NorthGRIP ice core for the last
16,000 years – Glaciological and paleoclimatic implications, Earth
Planet. Sc. Lett., 405, 132–141, https://doi.org/10.1016/j.epsl.2014.08.022,
2014. a, b
Goodman, J. and Weare, J.: Ensemble samplers with affine invariance, Comm. App. Math. Com. Sc., 5, 1–99, 2010. a
Haslett, J. and Parnell, A.: A simple monotone process with application to
radiocarbon-dated depth chronologies, J. R. Stat.
Soc. C-Appl., 57, 399–418,
https://doi.org/10.1111/j.1467-9876.2008.00623.x, 2008. a
Jouzel, J., Alley, R. B., Cuffey, K. M., Dansgaard, W., Grootes, P., Hoffmann,
G., Johnsen, S. J., Koster, R. D., Peel, D., Shuman, C. A., Stievenard, M.,
Stuiver, M., and White, J.: Validity of the temperature reconstruction from
water isotopes in ice cores, J. Geophys. Res.-Oceans, 102,
26471–26487, https://doi.org/10.1029/97JC01283, 1997. a
Li, T. Y., Han, L. Y., Cheng, H., Edwards, R. L., Shen, C. C., Li, H. C., Li,
J. Y., Huang, C. X., Zhang, T. T., and Zhao, X.: Evolution of the Asian
summer monsoon during Dansgaard/Oeschger events 13–17 recorded in a
stalagmite constrained by high-precision chronology from southwest China,
Quaternary Res., 88, 121–128, https://doi.org/10.1017/qua.2017.22,
2017. a
McKay, N. P., Emile-Geay, J., and Khider, D.: geoChronR – an R package to model, analyze, and visualize age-uncertain data, Geochronology, 3, 149–169, https://doi.org/10.5194/gchron-3-149-2021, 2021. a, b
Myrvoll-Nilsen, E.: eirikmn/dating_uncertainty: First release (v1.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6637528, 2022.
North Greenland Ice Core Project members: High-resolution record of Northern
Hemisphere climate extending into the last interglacial period, Nature, 431,
147–151, https://doi.org/10.1038/nature02805, 2004. a, b
Parnell, A. C., Haslett, J., Allen, J. R., Buck, C. E., and Huntley, B.: A
flexible approach to assessing synchroneity of past events using Bayesian
reconstructions of sedimentation history, Quaternary Sci. Rev., 27,
1872–1885, https://doi.org/10.1016/j.quascirev.2008.07.009, 2008. a
Ramsey, C. B.: Radiocarbon calibration and analysis of stratigraphy: the OxCal
program, Radiocarbon, 37, 425–430, https://doi.org/10.1017/s0033822200030903, 1995. a
Ramsey, C. B.: Deposition models for chronological records, Quaternary
Sci. Rev., 27, 42–60, https://doi.org/10.1016/j.quascirev.2007.01.019, 2008. a
Rasmussen, S. O., Andersen, K. K., Svensson, A. M., Steffensen, J. P., Vinther,
B. M., Clausen, H. B., Siggaard-Andersen, M. L., Johnsen, S. J., Larsen,
L. B., Dahl-Jensen, D., Bigler, M., Röthlisberger, R., Fischer, H.,
Goto-Azuma, K., Hansson, M. E., and Ruth, U.: A new Greenland ice core
chronology for the last glacial termination, J. Geophys. Res.-Atmos., 111, 1–16, https://doi.org/10.1029/2005JD006079, 2006. a, b, c, d, e, f
Rasmussen, S. O., Bigler, M., Blockley, S. P., Blunier, T., Buchardt, S. L.,
Clausen, H. B., Cvijanovic, I., Dahl-Jensen, D., Johnsen, S. J., Fischer, H.,
Gkinis, V., Guillevic, M., Hoek, W. Z., Lowe, J. J., Pedro, J. B., Popp, T.,
Seierstad, I. K., Steffensen, J. P., Svensson, A. M., Vallelonga, P.,
Vinther, B. M., Walker, M. J., Wheatley, J. J., and Winstrup, M.: A
stratigraphic framework for abrupt climatic changes during the Last Glacial
period based on three synchronized Greenland ice-core records: refining and
extending the INTIMATE event stratigraphy, Quaternary Sci. Rev., 106,
14–28, https://doi.org/10.1016/j.quascirev.2014.09.007, 2014. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Riechers, K. and Boers, N.: A statistical approach to the phasing of
atmospheric reorganization and sea ice retreat at the onset of
Dansgaard–Oeschger events under rigorous treatment of uncertainties, Clim. Past Discuss. [preprint], 2020, 1–30, https://doi.org/10.5194/cp-2020-136, 2020. a, b
Rue, H., Martino, S., and Chopin, N.: Approximate Bayesian inference for
latent Gaussian models using integrated nested Laplace approximations
(with discussion), J. R. Stat. Soc. Series B, 71, 319–392, 2009. a
Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., and
Lindgren, F. K.: Bayesian Computing with INLA: A Review, Annu. Rev. Stat.
Appl., 4, 395–421, 2017. a
Ruth, U., Wagenbach, D., Steffensen, J. P., and Bigler, M.: Continuous record
of microparticle concentration and size distribution in the central Greenland
NGRIP ice core during the last glacial period, J. Geophys. Res.-Atmos., 108, 1–12, https://doi.org/10.1029/2002jd002376, 2003. a, b, c
Ruth, U., Bigler, M., Röthlisberger, R., Siggaard-Andersen, M. L.,
Kipfstuhl, S., Goto-Azuma, K., Hansson, M. E., Johnsen, S. J., Lu, H., and
Steffensen, J. P.: Ice core evidence for a very tight link between North
Atlantic and east Asian glacial climate, Geophys. Res. Lett., 34,
1–5, https://doi.org/10.1029/2006GL027876, 2007. a
Schüpbach, S., Fischer, H., Bigler, M., Erhardt, T., Gfeller, G.,
Leuenberger, D., Mini, O., Mulvaney, R., Abram, N. J., Fleet, L., Frey,
M. M., Thomas, E., Svensson, A., Dahl-Jensen, D., Kettner, E., Kjaer, H.,
Seierstad, I., Steffensen, J. P., Rasmussen, S. O., Vallelonga, P., Winstrup,
M., Wegner, A., Twarloh, B., Wolff, K., Schmidt, K., Goto-Azuma, K.,
Kuramoto, T., Hirabayashi, M., Uetake, J., Zheng, J., Bourgeois, J., Fisher,
D., Zhiheng, D., Xiao, C., Legrand, M., Spolaor, A., Gabrieli, J., Barbante,
C., Kang, J. H., Hur, S. D., Hong, S. B., Hwang, H. J., Hong, S., Hansson,
M., Iizuka, Y., Oyabu, I., Muscheler, R., Adolphi, F., Maselli, O.,
McConnell, J., and Wolff, E. W.: Greenland records of aerosol source and
atmospheric lifetime changes from the Eemian to the Holocene, Nat.
Commun., 9, 1476, https://doi.org/10.1038/s41467-018-03924-3, 2018. a
Svensson, A., Andersen, K. K., Bigler, M., Clausen, H. B., Dahl-Jensen, D., Davies, S. M., Johnsen, S. J., Muscheler, R., Parrenin, F., Rasmussen, S. O., Röthlisberger, R., Seierstad, I., Steffensen, J. P., and Vinther, B. M.: A 60 000 year Greenland stratigraphic ice core chronology, Clim. Past, 4, 47–57, https://doi.org/10.5194/cp-4-47-2008, 2008. a, b, c
Vinther, B. M., Clausen, H. B., Johnsen, S. J., Rasmussen, S. O., Andersen,
K. K., Buchardt, S. L., Dahl-Jensen, D., Seierstad, I. K., Siggaard-Andersen,
M. L., Steffensen, J. P., Svensson, A., Olsen, J., and Heinemeier, J.: A
synchronized dating of three Greenland ice cores throughout the Holocene,
J. Geophys. Res.-Atmos., 111, 1–11,
https://doi.org/10.1029/2005JD006921, 2006. a, b, c
Zhou, H., Zhao, J. X., Feng, Y., Chen, Q., Mi, X., Shen, C. C., He, H., Yang,
L., Liu, S., Chen, L., Huang, J., and Zhu, L.: Heinrich event 4 and
Dansgaard/Oeschger events 5–10 recorded by high-resolution speleothem oxygen
isotope data from central China, Quaternary Res., 82,
394–404, https://doi.org/10.1016/j.yqres.2014.07.006, 2014. a
Short summary
In layer counted proxy records each measurement is accompanied by a timestamp typically measured by counting periodic layers. Knowledge of the uncertainty of this timestamp is important for a rigorous propagation to further analyses. By assuming a Bayesian regression model to the layer increments we express the dating uncertainty by the posterior distribution, from which chronologies can be sampled efficiently. We apply our framework to dating abrupt warming transitions during the last glacial.
In layer counted proxy records each measurement is accompanied by a timestamp typically measured...
