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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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.
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Takahito Mitsui and Niklas Boers
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Eirik Myrvoll-Nilsen, Luc Hallali, and Martin Rypdal
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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
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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
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The BARCAST climate field reconstruction method is tested using synthetic data experiments. It is demonstrated that the output reconstructions have altered statistical properties compared with the input data, but they are also not necessarily consistent with the model assumption of the reconstruction method. The conclusion is that the statistical properties of a reconstruction not only reflect the statistics of the real climate, but they may very well be affected by the manipulation of the data.
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Earth Syst. Dynam., 8, 1171–1190, https://doi.org/10.5194/esd-8-1171-2017, https://doi.org/10.5194/esd-8-1171-2017, 2017
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Clim. Past, 13, 1181–1197, https://doi.org/10.5194/cp-13-1181-2017, https://doi.org/10.5194/cp-13-1181-2017, 2017
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Niklas Boers, Bedartha Goswami, and Michael Ghil
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Kristoffer Rypdal and Martin Rypdal
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This comment on the paper by Lovejoy and Varotsos demonstrates that their methods for establishing nonlinearity in the global temperature response in climate models are flawed. One of their methods is based on an invalid approximation, which when corrected does not falsify the hypothesis that the response is linear. This conclusion is enforced when internal variability in the models are accounted for. The other results in their paper are also shown to be reproduced by linear-response models.
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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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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Robert Mulvaney, Eric W. Wolff, Mackenzie M. Grieman, Helene H. Hoffmann, Jack D. Humby, Christoph Nehrbass-Ahles, Rachael H. Rhodes, Isobel F. Rowell, Frédéric Parrenin, Loïc Schmidely, Hubertus Fischer, Thomas F. Stocker, Marcus Christl, Raimund Muscheler, Amaelle Landais, and Frédéric Prié
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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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Greenland ice core records show that the climate of the last glacial period was frequently interrupted by rapid warming events, followed by cooling episodes of vastly different duration. We fit a generic waveform to the noisy ice core record in order to extract a robust climate signal and empirically study what controls the amplitude and duration of the warmings and coolings. We find that cooling transitions are more predictable than warmings and are influenced by different climate forcings.
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
Clim. Past, 9, 983–999, https://doi.org/10.5194/cp-9-983-2013, https://doi.org/10.5194/cp-9-983-2013, 2013
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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...
