Articles | Volume 15, issue 14
Geosci. Model Dev., 15, 5713–5737, 2022
https://doi.org/10.5194/gmd-15-5713-2022

Special issue: Nucleus for European Modelling of the Ocean - NEMO

Geosci. Model Dev., 15, 5713–5737, 2022
https://doi.org/10.5194/gmd-15-5713-2022
Development and technical paper
22 Jul 2022
Development and technical paper | 22 Jul 2022

Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon

Marcus Falls et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2021-222', Juan Antonio Añel, 09 Aug 2021
    • AC1: 'Reply on CEC1', Marcus Falls, 25 Aug 2021
  • RC1: 'Review of gmd-2021-222', Anonymous Referee #1, 05 Jan 2022
  • RC2: 'Comment on gmd-2021-222', Urmas Raudsepp, 11 Apr 2022
  • AC2: 'Comment on gmd-2021-222', Marcus Falls, 23 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Marcus Falls on behalf of the Authors (23 May 2022)  Author's response    Manuscript
ED: Publish as is (22 Jun 2022) by Olivier Marti
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Short summary
This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.