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...
| Published in: | Earth System Dynamics |
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| Main Authors: | , , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2017
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/esd-8-1171-2017 https://noa.gwlb.de/receive/cop_mods_00007751 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00007708/esd-8-1171-2017.pdf https://esd.copernicus.org/articles/8/1171/2017/esd-8-1171-2017.pdf |
| Summary: | 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. |
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