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
Related authors
Luc Hallali, Eirik Myrvoll-Nilsen, and Christian L. E. Franzke
EGUsphere, https://doi.org/10.5194/egusphere-2025-2461, https://doi.org/10.5194/egusphere-2025-2461, 2025
Short summary
Short summary
We present an alternative statistical methodology to detect whether the Atlantic Ocean’s circulation system is approaching a tipping point. Our approach separates natural variability from real early warning signals of tipping, reducing false alarms. When applied to proxy of Atlantic Ocean’s circulation strength , we found significant signs that the system is ongoing destabilization . This suggests it may be approaching a tipping point, which could have major impacts on global climate patterns.
Eirik Myrvoll-Nilsen, Luc Hallali, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-436, https://doi.org/10.5194/egusphere-2024-436, 2024
Short summary
Short summary
Before a climate component reaches a tipping point, there may be observable changes in its statistical properties. These are known as early warning signals and include increased fluctuation and correlation times. We present a Bayesian approach to detect these signals, using a model where the correlation parameter depends linearly on time for which the slope can be estimated directly from the data. The model is then applied to Dansgaard-Oeschger events using Greenland Ice core data.
Michael Aich, Philipp Hess, Baoxiang Pan, Sebastian Bathiany, Yu Huang, and Niklas Boers
EGUsphere, https://doi.org/10.5194/egusphere-2025-2646, https://doi.org/10.5194/egusphere-2025-2646, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Accurately simulating rainfall is essential to understand the impacts of climate change, especially extreme events such as floods and droughts. Climate models simulate the atmosphere at a coarse resolution and often misrepresent precipitation, leading to biased and overly smooth fields. We improve the precipitation using a machine learning model that is data-efficient, preserves key climate signals such as trends and variability, and significantly improves the representation of extreme events.
Luc Hallali, Eirik Myrvoll-Nilsen, and Christian L. E. Franzke
EGUsphere, https://doi.org/10.5194/egusphere-2025-2461, https://doi.org/10.5194/egusphere-2025-2461, 2025
Short summary
Short summary
We present an alternative statistical methodology to detect whether the Atlantic Ocean’s circulation system is approaching a tipping point. Our approach separates natural variability from real early warning signals of tipping, reducing false alarms. When applied to proxy of Atlantic Ocean’s circulation strength , we found significant signs that the system is ongoing destabilization . This suggests it may be approaching a tipping point, which could have major impacts on global climate patterns.
Anna Poltronieri, Nils Bochow, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2025-1134, https://doi.org/10.5194/egusphere-2025-1134, 2025
Preprint archived
Short summary
Short summary
As Arctic sea ice shrinks, new shipping routes become more accessible. This study compares the effects of two main Arctic pathways: the Northern and the Transpolar Sea routes. Using a high-complexity climate model, we simulate black carbon emissions from ships. When deposited on sea ice, black carbon increases solar absorption, enhancing melt. We analyze absorbed solar radiation, sea ice extent, and air temperature, finding that the Transpolar Sea Route has a greater effect on Arctic sea ice.
Nils Bochow, Anna Poltronieri, and Niklas Boers
The Cryosphere, 18, 5825–5863, https://doi.org/10.5194/tc-18-5825-2024, https://doi.org/10.5194/tc-18-5825-2024, 2024
Short summary
Short summary
Using the latest climate models, we update the understanding of how the Greenland ice sheet responds to climate changes. We found that precipitation and temperature changes in Greenland vary across different regions. Our findings suggest that using uniform estimates for temperature and precipitation for modelling the response of the ice sheet can overestimate ice loss in Greenland. Therefore, this study highlights the need for spatially resolved data in predicting the ice sheet's future.
Takahito Mitsui, Peter Ditlevsen, Niklas Boers, and Michel Crucifix
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-39, https://doi.org/10.5194/esd-2024-39, 2024
Revised manuscript accepted for ESD
Short summary
Short summary
The late Pleistocene glacial cycles are dominated by a 100-kyr periodicity, rather than other major astronomical periods like 19, 23, 41, or 400 kyr. Various models propose distinct mechanisms to explain this, but their diversity may obscure the key factor behind the 100-kyr periodicity. We propose a time-scale matching hypothesis, suggesting that the ice-sheet climate system responds to astronomical forcing at ~100 kyr because its intrinsic timescale is closer to 100 kyr than to other periods.
Clara Hummel, Niklas Boers, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-3567, https://doi.org/10.5194/egusphere-2024-3567, 2024
Short summary
Short summary
We revisit early warning signals (EWS) for past abrupt climate changes known as Dansgaard-Oeschger (DO) events. Using advanced statistical methods, we find fewer significant EWS than previously reported. While some signals appear consistent across Greenland ice core records, they are not enough to identify the still unknown physical mechanisms behind DO events. This study highlights the complexity of predicting climate changes and urges caution in interpreting (paleo-)climate data.
John Slattery, Louise C. Sime, Francesco Muschitiello, and Keno Riechers
Clim. Past, 20, 2431–2454, https://doi.org/10.5194/cp-20-2431-2024, https://doi.org/10.5194/cp-20-2431-2024, 2024
Short summary
Short summary
Dansgaard–Oeschger events are a series of abrupt past climate change events during which the atmosphere, sea ice, and ocean in the North Atlantic underwent rapid changes. One current topic of interest is the order in which these different changes occurred, which remains unknown. In this work, we find that the current best method used to investigate this topic is subject to substantial bias. This implies that it is not possible to reliably determine the order of the different changes.
Vasilis Dakos, Chris A. Boulton, Joshua E. Buxton, Jesse F. Abrams, Beatriz Arellano-Nava, David I. Armstrong McKay, Sebastian Bathiany, Lana Blaschke, Niklas Boers, Daniel Dylewsky, Carlos López-Martínez, Isobel Parry, Paul Ritchie, Bregje van der Bolt, Larissa van der Laan, Els Weinans, and Sonia Kéfi
Earth Syst. Dynam., 15, 1117–1135, https://doi.org/10.5194/esd-15-1117-2024, https://doi.org/10.5194/esd-15-1117-2024, 2024
Short summary
Short summary
Tipping points are abrupt, rapid, and sometimes irreversible changes, and numerous approaches have been proposed to detect them in advance. Such approaches have been termed early warning signals and represent a set of methods for identifying changes in the underlying behaviour of a system across time or space that might indicate an approaching tipping point. Here, we review the literature to explore where, how, and which early warnings have been used in real-world case studies so far.
Maya Ben-Yami, Lana Blaschke, Sebastian Bathiany, and Niklas Boers
EGUsphere, https://doi.org/10.5194/egusphere-2024-1106, https://doi.org/10.5194/egusphere-2024-1106, 2024
Preprint archived
Short summary
Short summary
Recent work has used observations to find statistical signs that the Atlantic Meridional Overturning Circulation (AMOC) may be approaching a collapse. We find that in complex climate models in which the AMOC does not collapse before 2100, the statistical signs that are present in the observations are not found in the 1850–2014 equivalent model time series. This indicates that the observed statistical signs are not prone to false positives.
Takahito Mitsui and Niklas Boers
Clim. Past, 20, 683–699, https://doi.org/10.5194/cp-20-683-2024, https://doi.org/10.5194/cp-20-683-2024, 2024
Short summary
Short summary
In general, the variance and short-lag autocorrelations of the fluctuations increase in a system approaching a critical transition. Using these indicators, we identify statistical precursor signals for the Dansgaard–Oeschger cooling events recorded in two climatic proxies of three Greenland ice core records. We then provide a dynamical systems theory that bridges the gap between observing statistical precursor signals and the physical precursor signs empirically known in paleoclimate research.
Eirik Myrvoll-Nilsen, Luc Hallali, and Martin Rypdal
EGUsphere, https://doi.org/10.5194/egusphere-2024-436, https://doi.org/10.5194/egusphere-2024-436, 2024
Short summary
Short summary
Before a climate component reaches a tipping point, there may be observable changes in its statistical properties. These are known as early warning signals and include increased fluctuation and correlation times. We present a Bayesian approach to detect these signals, using a model where the correlation parameter depends linearly on time for which the slope can be estimated directly from the data. The model is then applied to Dansgaard-Oeschger events using Greenland Ice core data.
Takahito Mitsui, Matteo Willeit, and Niklas Boers
Earth Syst. Dynam., 14, 1277–1294, https://doi.org/10.5194/esd-14-1277-2023, https://doi.org/10.5194/esd-14-1277-2023, 2023
Short summary
Short summary
The glacial–interglacial cycles of the Quaternary exhibit 41 kyr periodicity before the Mid-Pleistocene Transition (MPT) around 1.2–0.8 Myr ago and ~100 kyr periodicity after that. The mechanism generating these periodicities remains elusive. Through an analysis of an Earth system model of intermediate complexity, CLIMBER-2, we show that the dominant periodicities of glacial cycles can be explained from the viewpoint of synchronization theory.
Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023, https://doi.org/10.5194/hess-27-2645-2023, 2023
Short summary
Short summary
Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
Short summary
Short summary
Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Keno Riechers, Leonardo Rydin Gorjão, Forough Hassanibesheli, Pedro G. Lind, Dirk Witthaut, and Niklas Boers
Earth Syst. Dynam., 14, 593–607, https://doi.org/10.5194/esd-14-593-2023, https://doi.org/10.5194/esd-14-593-2023, 2023
Short summary
Short summary
Paleoclimate proxy records show that the North Atlantic climate repeatedly transitioned between two regimes during the last glacial interval. This study investigates a bivariate proxy record from a Greenland ice core which reflects past Greenland temperatures and large-scale atmospheric conditions. We reconstruct the underlying deterministic drift by estimating first-order Kramers–Moyal coefficients and identify two separate stable states in agreement with the aforementioned climatic regimes.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Keno Riechers, Takahito Mitsui, Niklas Boers, and Michael Ghil
Clim. Past, 18, 863–893, https://doi.org/10.5194/cp-18-863-2022, https://doi.org/10.5194/cp-18-863-2022, 2022
Short summary
Short summary
Building upon Milancovic's theory of orbital forcing, this paper reviews the interplay between intrinsic variability and external forcing in the emergence of glacial interglacial cycles. It provides the reader with historical background information and with basic theoretical concepts used in recent paleoclimate research. Moreover, it presents new results which confirm the reduced stability of glacial-cycle dynamics after the mid-Pleistocene transition.
Keno Riechers and Niklas Boers
Clim. Past, 17, 1751–1775, https://doi.org/10.5194/cp-17-1751-2021, https://doi.org/10.5194/cp-17-1751-2021, 2021
Short summary
Short summary
Greenland ice core data show that the last glacial cycle was punctuated by a series of abrupt climate shifts comprising significant warming over Greenland, retreat of North Atlantic sea ice, and atmospheric reorganization. Statistical analysis of multi-proxy records reveals no systematic lead or lag between the transitions of proxies that represent different climatic subsystems, and hence no evidence for a potential trigger of these so-called Dansgaard–Oeschger events can be found.
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...
