Articles | Volume 15, issue 14
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, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí

Data sets

ARGO Data International Argo Program (Global Ocean Observing System)

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

Model code and software

NEMO and PISCES code Nucleus for European Modelling of the Ocean (NEMO)

PISCES-v2 Martí Galí

Genetic Algorithm Workflow Marcus Falls

Genetic Algorithm Pisces 1D Workflow and config files Marcus Falls

PISCES-v2 1D configuration used to study POC dynamics as observed by BGC-Argo floats (1.0.0) Martí Galí

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.