Articles | Volume 30, issue 2
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


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)
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.