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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Inverse stochastic-dynamic models for high-resolution Greenland ice core records. / Boers, Niklas; Chekroun, Mickael D.; Liu, Honghu; Kondrashov, Dmitri; Rousseau, Denis Didier; Svensson, Anders; Bigler, Matthias; Ghil, Michael.

I: Earth System Dynamics, Bind 8, Nr. 4, 2017, s. 1171-1190.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Boers, N, Chekroun, MD, Liu, H, Kondrashov, D, Rousseau, DD, Svensson, A, Bigler, M & Ghil, M 2017, 'Inverse stochastic-dynamic models for high-resolution Greenland ice core records', Earth System Dynamics, bind 8, nr. 4, s. 1171-1190. https://doi.org/10.5194/esd-8-1171-2017

APA

Boers, N., Chekroun, M. D., Liu, H., Kondrashov, D., Rousseau, D. D., Svensson, A., Bigler, M., & Ghil, M. (2017). Inverse stochastic-dynamic models for high-resolution Greenland ice core records. Earth System Dynamics, 8(4), 1171-1190. https://doi.org/10.5194/esd-8-1171-2017

Vancouver

Boers N, Chekroun MD, Liu H, Kondrashov D, Rousseau DD, Svensson A o.a. Inverse stochastic-dynamic models for high-resolution Greenland ice core records. Earth System Dynamics. 2017;8(4):1171-1190. https://doi.org/10.5194/esd-8-1171-2017

Author

Boers, Niklas ; Chekroun, Mickael D. ; Liu, Honghu ; Kondrashov, Dmitri ; Rousseau, Denis Didier ; Svensson, Anders ; Bigler, Matthias ; Ghil, Michael. / Inverse stochastic-dynamic models for high-resolution Greenland ice core records. I: Earth System Dynamics. 2017 ; Bind 8, Nr. 4. s. 1171-1190.

Bibtex

@article{63a9e010c0134354834e900172578093,
title = "Inverse stochastic-dynamic models for high-resolution Greenland ice core records",
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 18O 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 18O 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 18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.",
author = "Niklas Boers and Chekroun, {Mickael D.} and Honghu Liu and Dmitri Kondrashov and Rousseau, {Denis Didier} and Anders Svensson and Matthias Bigler and Michael Ghil",
year = "2017",
doi = "10.5194/esd-8-1171-2017",
language = "English",
volume = "8",
pages = "1171--1190",
journal = "Earth System Dynamics",
issn = "2190-4979",
publisher = "Copernicus GmbH",
number = "4",

}

RIS

TY - JOUR

T1 - Inverse stochastic-dynamic models for high-resolution Greenland ice core records

AU - Boers, Niklas

AU - Chekroun, Mickael D.

AU - Liu, Honghu

AU - Kondrashov, Dmitri

AU - Rousseau, Denis Didier

AU - Svensson, Anders

AU - Bigler, Matthias

AU - Ghil, Michael

PY - 2017

Y1 - 2017

N2 - 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 18O 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 18O 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 18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.

AB - 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 18O 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 18O 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 18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.

U2 - 10.5194/esd-8-1171-2017

DO - 10.5194/esd-8-1171-2017

M3 - Journal article

AN - SCOPUS:85029618402

VL - 8

SP - 1171

EP - 1190

JO - Earth System Dynamics

JF - Earth System Dynamics

SN - 2190-4979

IS - 4

ER -

ID: 196140951