Articles | Volume 30, issue 2
https://doi.org/10.5194/npg-30-195-2023
https://doi.org/10.5194/npg-30-195-2023
Research article
 | 
28 Jun 2023
Research article |  | 28 Jun 2023

Data-driven methods to estimate the committor function in conceptual ocean models

Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra

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Cited articles

Altman, N. S.: An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression, Am. Stat., 46, 175–185, https://doi.org/10.1080/00031305.1992.10475879, 1992. a
Armstrong McKay, D. I., Staal, A., Abrams, J. F., Winkelmann, R., Sakschewski, B., Loriani, S., Fetzer, I., Cornell, S. E., Rockström, J., and Lenton, T. M.: Exceeding 1.5 C global warming could trigger multiple climate tipping points, Science, 377, eabn7950, https://doi.org/10.1126/science.abn7950, 2022. a
Baars, S., Castellana, D., Wubs, F., and Dijkstra, H.: Application of adaptive multilevel splitting to high-dimensional dynamical systems, J. Comput. Phys., 424, 109876, https://doi.org/10.1016/j.jcp.2020.109876, 2021. a, b
Benedetti, R.: Scoring Rules for Forecast Verification, Mon. Weather Rev., 138, 203–211, https://doi.org/10.1175/2009MWR2945.1, 2010. a, b
Bentley, J. L.: Multidimensional Binary Search Trees Used for Associative Searching, Commun. ACM, 18, 509–517, https://doi.org/10.1145/361002.361007, 1975. a
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Computing the probability of occurrence of rare events is relevant because of their high impact but also difficult due to the lack of data. Rare event algorithms are designed for that task, but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.