Inverse stochastic–dynamic models for high-resolution Greenland ice core records
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 t...
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ftdatacite:10.7916/d80k3jsz 2023-05-15T16:26:31+02:00 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 2017 https://dx.doi.org/10.7916/d80k3jsz https://academiccommons.columbia.edu/doi/10.7916/D80K3JSZ unknown Columbia University https://dx.doi.org/10.5194/esd-8-1171-2017 Paleoclimatology Stochastic models Dynamics Climatic changes Dust Text Articles article-journal ScholarlyArticle 2017 ftdatacite https://doi.org/10.7916/d80k3jsz https://doi.org/10.5194/esd-8-1171-2017 2021-11-05T12:55:41Z 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 59–22 ka b2k. 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. Text Greenland Greenland ice core Greenland Ice core Project Greenland ice cores ice core NGRIP North Greenland North Greenland Ice Core Project DataCite Metadata Store (German National Library of Science and Technology) Greenland |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Paleoclimatology Stochastic models Dynamics Climatic changes Dust |
spellingShingle |
Paleoclimatology Stochastic models Dynamics Climatic changes Dust 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 |
topic_facet |
Paleoclimatology Stochastic models Dynamics Climatic changes Dust |
description |
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 59–22 ka b2k. 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. |
format |
Text |
author |
Boers, Niklas Chekroun, Mickael D. Liu, Honghu Kondrashov, Dmitri Rousseau, Denis-Didier Svensson, Anders Bigler, Matthias Ghil, Michael |
author_facet |
Boers, Niklas Chekroun, Mickael D. Liu, Honghu Kondrashov, Dmitri Rousseau, Denis-Didier Svensson, Anders Bigler, Matthias Ghil, Michael |
author_sort |
Boers, Niklas |
title |
Inverse stochastic–dynamic models for high-resolution Greenland ice core records |
title_short |
Inverse stochastic–dynamic models for high-resolution Greenland ice core records |
title_full |
Inverse stochastic–dynamic models for high-resolution Greenland ice core records |
title_fullStr |
Inverse stochastic–dynamic models for high-resolution Greenland ice core records |
title_full_unstemmed |
Inverse stochastic–dynamic models for high-resolution Greenland ice core records |
title_sort |
inverse stochastic–dynamic models for high-resolution greenland ice core records |
publisher |
Columbia University |
publishDate |
2017 |
url |
https://dx.doi.org/10.7916/d80k3jsz https://academiccommons.columbia.edu/doi/10.7916/D80K3JSZ |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Greenland ice core Greenland Ice core Project Greenland ice cores ice core NGRIP North Greenland North Greenland Ice Core Project |
genre_facet |
Greenland Greenland ice core Greenland Ice core Project Greenland ice cores ice core NGRIP North Greenland North Greenland Ice Core Project |
op_relation |
https://dx.doi.org/10.5194/esd-8-1171-2017 |
op_doi |
https://doi.org/10.7916/d80k3jsz https://doi.org/10.5194/esd-8-1171-2017 |
_version_ |
1766015446050406400 |