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

Niklas Boers (1) , Mickael D Chekroun (2) , Honghu Liu (3) , Dmitri Kondrashov (2, 4) , Denis-Didier Rousseau (5, 1) , Anders Svensson (6) , Matthias Bigler (7) , Michael Ghil (1, 2)

1 LMD - Laboratoire de Météorologie Dynamique (UMR 8539)
2 IGPP - Institute of Geophysics and Planetary Physics [Los Angeles]
3 Virginia Polytechnic Institute and State University [Blacksburg]
4 Institute of Applied Physics of RAS
5 LDEO - Lamont-Doherty Earth Observatory
6 Centre for Ice and Climate [Copenhagen]
7 OCCR - Oeschger Centre for Climate Change Research

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 δ 18 O 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 59-22 ka b2k. Our model reproduces the dynamical characteristics of both the δ 18 O 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 δ 18 O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.

Domaines: Climatologie

Niklas Boers, Mickael D Chekroun, Honghu Liu, Dmitri Kondrashov, Denis-Didier Rousseau, et al.. Inverse stochastic–dynamic models for high-resolution Greenland ice core records. Earth System Dynamics, 2017, 8 (4), pp.1171 - 1190. ⟨10.5194/esd-8-1171-2017⟩. ⟨insu-03321824⟩