Preprints
https://doi.org/10.5194/egusphere-2022-1362
https://doi.org/10.5194/egusphere-2022-1362
19 Dec 2022
 | 19 Dec 2022

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

Abstract. In recent years, several climate subsystems have been identified that may undergo a relatively rapid transition compared to the changes in their forcing. Such transitions are rare events in general and simulating long-enough trajectories in order to gather sufficient data to determine transition statistics would be too expensive. Conversely, rare-events algorithms like TAMS (Trajectory-Adaptive Multilevel Sampling) encourage the transition while keeping track of the model statistics. However, this algorithm relies on a score function whose choice is crucial to ensure its efficiency. The optimal score function, called committor function, is in practice very difficult to compute. In this paper, we compare different data-based methods (Analogue Markov Chains, Neural Networks, Reservoir Computing, Dynamical Galerkin Approximation) to estimate the committor from trajectory data. We apply these methods on two models of the Atlantic Ocean circulation featuring very different dynamical behavior. We compare these methods in terms of two measures, evaluating how close the estimate is from the true committor, and in terms of the computational time. We find that all methods are able to extract information from the data in order to provide a good estimate of the committor. Analogue Markov Chains provide a very reliable estimate of the true committor in simple models but prove not so robust when applied to systems with a more complex phase space. Neural network methods clearly stand out by their relatively low testing time, and their training time scales more favorably with the complexity of the model than the other methods. In particular, feedforward neural networks consistently achieve the best performance when trained with enough data, making this method promising for committor estimation in sophisticated climate models.

Journal article(s) based on this preprint

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
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023,https://doi.org/10.5194/npg-30-195-2023, 2023
Short summary

Valérian Jacques-Dumas et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1362', Anonymous Referee #1, 03 Feb 2023
    • AC1: 'Reply on RC1', Valérian Jacques-Dumas, 20 Mar 2023
  • RC2: 'Comment on egusphere-2022-1362', Anonymous Referee #2, 05 Feb 2023
    • AC2: 'Reply on RC2', Valérian Jacques-Dumas, 20 Mar 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1362', Anonymous Referee #1, 03 Feb 2023
    • AC1: 'Reply on RC1', Valérian Jacques-Dumas, 20 Mar 2023
  • RC2: 'Comment on egusphere-2022-1362', Anonymous Referee #2, 05 Feb 2023
    • AC2: 'Reply on RC2', Valérian Jacques-Dumas, 20 Mar 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Valérian Jacques-Dumas on behalf of the Authors (20 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 May 2023) by Stéphane Vannitsem
RR by Anonymous Referee #1 (30 May 2023)
ED: Publish as is (01 Jun 2023) by Stéphane Vannitsem
AR by Valérian Jacques-Dumas on behalf of the Authors (01 Jun 2023)

Journal article(s) based on this preprint

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
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023,https://doi.org/10.5194/npg-30-195-2023, 2023
Short summary

Valérian Jacques-Dumas et al.

Valérian Jacques-Dumas et al.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
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.