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

• Published in: Earth System Dynamics
• Main Authors: Boers, Niklas, Chekroun, Mickael, D, Liu, Honghu, Kondrashov, Dmitri, Rousseau, Denis-Didier, Svensson, Anders, Bigler, Matthias, Ghil, Michael
• Other Authors: Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institute of Geophysics and Planetary Physics Los Angeles (IGPP), University of California Los Angeles (UCLA), University of California (UC)-University of California (UC), Virginia Polytechnic Institute and State University Blacksburg, Institute of Applied Physics of RAS, Russian Academy of Sciences Moscow (RAS), Lamont-Doherty Earth Observatory (LDEO), Columbia University New York, Centre for Ice and Climate Copenhagen, Niels Bohr Institute Copenhagen (NBI), Faculty of Science Copenhagen, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Science Copenhagen, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Oeschger Centre for Climate Change Research (OCCR), Universität Bern / University of Bern (UNIBE)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2017
• Subjects: [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; Greenland; Greenland ice core; Greenland Ice core Project; Greenland ice cores; ice core; NGRIP; North Greenland; North Greenland Ice Core Project
• Online Access: https://insu.hal.science/insu-03321824; https://insu.hal.science/insu-03321824/document; https://insu.hal.science/insu-03321824/file/DDR_ESD_Boers17.pdf; https://doi.org/10.5194/esd-8-1171-2017

International audience 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.