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Volume 8, issue 4 | Copyright
Earth Syst. Dynam., 8, 1171-1190, 2017
https://doi.org/10.5194/esd-8-1171-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 15 Dec 2017

Research article | 15 Dec 2017

Inverse stochastic–dynamic models for high-resolution Greenland ice core records

Niklas Boers1, Mickael D. Chekroun2, Honghu Liu3, Dmitri Kondrashov2,4, Denis-Didier Rousseau1,5, Anders Svensson6, Matthias Bigler7, and Michael Ghil1,2 Niklas Boers et al.
  • 1Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), École Normale Supérieure and PSL Research University, Paris, France
  • 2Department of Atmospheric and Oceanic Sciences and Institute of Geophysics and Planetary Physics, University of California, Los Angeles, USA
  • 3Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, USA
  • 4Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russia
  • 5Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA
  • 6Centre for Ice and Climate, University of Copenhagen, Copenhagen, Denmark
  • 7Physics Institute and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland

Abstract. Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic–dynamic models from δ18O and dust records of unprecedented, subdecadal temporal resolution. The records stem from the North Greenland Ice Core Project (NGRIP), and we focus on the time interval 59–22kab2k. Our model reproduces the dynamical characteristics of both the δ18O and dust proxy records, including the millennial-scale Dansgaard–Oeschger variability, as well as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i) high-resolution training data, (ii) cubic drift terms, (iii) nonlinear coupling terms between the δ18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.

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We use a Bayesian approach for inferring inverse, stochastic–dynamic models from northern Greenland (NGRIP) oxygen and dust records of subdecadal resolution for the interval 59 to 22 ka b2k. Our model reproduces the statistical and dynamical characteristics of the records, including the Dansgaard–Oeschger variability, with no need for external forcing. The crucial ingredients are cubic drift terms, nonlinear coupling terms between the oxygen and dust time series, and non-Markovian contributions.
We use a Bayesian approach for inferring inverse, stochastic–dynamic models from northern...
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