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 δ 18 O and dust records of unprecedented, subdecadal...
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ftdoajarticles:oai:doaj.org/article:48dc44c1049f4b8b953507628d636309 2023-05-15T16:26:31+02:00 Inverse stochastic–dynamic models for high-resolution Greenland ice core records N. Boers M. D. Chekroun H. Liu D. Kondrashov D.-D. Rousseau A. Svensson M. Bigler M. Ghil 2017-12-01T00:00:00Z https://doi.org/10.5194/esd-8-1171-2017 https://doaj.org/article/48dc44c1049f4b8b953507628d636309 EN eng Copernicus Publications https://www.earth-syst-dynam.net/8/1171/2017/esd-8-1171-2017.pdf https://doaj.org/toc/2190-4979 https://doaj.org/toc/2190-4987 doi:10.5194/esd-8-1171-2017 2190-4979 2190-4987 https://doaj.org/article/48dc44c1049f4b8b953507628d636309 Earth System Dynamics, Vol 8, Pp 1171-1190 (2017) Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 article 2017 ftdoajarticles https://doi.org/10.5194/esd-8-1171-2017 2022-12-31T12:08:59Z 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. Article in Journal/Newspaper Greenland Greenland ice core Greenland Ice core Project Greenland ice cores ice core NGRIP North Greenland North Greenland Ice Core Project Directory of Open Access Journals: DOAJ Articles Greenland Earth System Dynamics 8 4 1171 1190 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 |
spellingShingle |
Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 N. Boers M. D. Chekroun H. Liu D. Kondrashov D.-D. Rousseau A. Svensson M. Bigler M. Ghil Inverse stochastic–dynamic models for high-resolution Greenland ice core records |
topic_facet |
Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 |
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 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. |
format |
Article in Journal/Newspaper |
author |
N. Boers M. D. Chekroun H. Liu D. Kondrashov D.-D. Rousseau A. Svensson M. Bigler M. Ghil |
author_facet |
N. Boers M. D. Chekroun H. Liu D. Kondrashov D.-D. Rousseau A. Svensson M. Bigler M. Ghil |
author_sort |
N. Boers |
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 |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/esd-8-1171-2017 https://doaj.org/article/48dc44c1049f4b8b953507628d636309 |
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_source |
Earth System Dynamics, Vol 8, Pp 1171-1190 (2017) |
op_relation |
https://www.earth-syst-dynam.net/8/1171/2017/esd-8-1171-2017.pdf https://doaj.org/toc/2190-4979 https://doaj.org/toc/2190-4987 doi:10.5194/esd-8-1171-2017 2190-4979 2190-4987 https://doaj.org/article/48dc44c1049f4b8b953507628d636309 |
op_doi |
https://doi.org/10.5194/esd-8-1171-2017 |
container_title |
Earth System Dynamics |
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8 |
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4 |
container_start_page |
1171 |
op_container_end_page |
1190 |
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1766015434434281472 |