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.

Data sets

ARGO Data International Argo Program (Global Ocean Observing System) https://argo.ucsd.edu

Datasets for the comparison between POC estimated from BGC-Argo floats and PISCES model simulations (1.0.0) M. Galí, R. Benardello, M. Falls, H. Claustre, and O. Aumont https://doi.org/10.5281/zenodo.5139602

Model code and software

NEMO and PISCES code Nucleus for European Modelling of the Ocean (NEMO) https://www.nemo-ocean.eu/

PISCES-v2 Martí Galí https://earth.bsc.es/gitlab/mgalitap/p1d_share/-/tree/gapoc

Genetic Algorithm Workflow Marcus Falls https://earth.bsc.es/gitlab/cp/genetic_algorithm_pisces1d

Genetic Algorithm Pisces 1D Workflow and config files Marcus Falls https://doi.org/10.5281/zenodo.5205760

PISCES-v2 1D configuration used to study POC dynamics as observed by BGC-Argo floats (1.0.0) Martí Galí https://doi.org/10.5281/zenodo.5243343

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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.