A Hidden Markov Model Approach to Infer Timescales for High-Resolution Climate Archives

• Published in: Proceedings of the AAAI Conference on Artificial Intelligence
• Main Author: Winstrup, Mai
• Format: Article in Journal/Newspaper
• Language: English
• Published: Association for the Advancement of Artificial Intelligence 2016
• Subjects: Greenland; Antarc*; Antarctica; ice core
• Online Access: https://ojs.aaai.org/index.php/AAAI/article/view/19084; https://doi.org/10.1609/aaai.v30i2.19084

We present a Hidden Markov Model-based algorithm for constructing timescales for paleoclimate records by annual layer counting. This objective, statistics-based approach has a number of major advantages over the current manual approach, beginning with speed. Manual layer counting of a single core (up to 3km in length) can require multiple person-years of time; the StratiCounter algorithm can count up to 100 layers/min, corresponding to a full-length timescale constructed in a few days. Moreover, the algorithm gives rigorous uncertainty estimates for the resulting timescale, which are far smaller than those produced manually. We demonstrate the utility of StratiCounter by applying it to ice-core data from two cores from Greenland and Antarctica. Performance of the algorithm is comparable to a manual approach. When using all available data, false-discovery rates and miss rates are 1-1.2% and 1.2-1.6%, respectively, for the two cores. For one core, even better agreement is found when using only the chemistry series primarily employed by human experts in the manual approach.