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...
| Published in: | Earth System Dynamics |
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| Main Authors: | , , , , , , , |
| Format: | Text |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/esd-8-1171-2017 https://esd.copernicus.org/articles/8/1171/2017/ |
| 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 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. |
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