Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design

Mignot, Alexandre; Claustre, Hervé; Cossarini, Gianpiero; D'Ortenzio, Fabrizio; Gutknecht, Elodie; Lamouroux, Julien; Lazzari, Paolo; Perruche, Coralie; Salon, Stefano; Sauzède, Raphaëlle; Taillandier, Vincent; Teruzzi, Anna

doi:https://doi.org/10.5194/bg-20-1405-2023

Articles | Volume 20, issue 7

https://doi.org/10.5194/bg-20-1405-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Biogeochemistry in the BGC-Argo era: from process studies...

https://doi.org/10.5194/bg-20-1405-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 7

Research article

|

12 Apr 2023

Research article |

| 12 Apr 2023

Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design

Alexandre Mignot, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, Coralie Perruche, Stefano Salon, Raphaëlle Sauzède, Vincent Taillandier, and Anna Teruzzi

Data sets

Global Ocean Biogeochemistry Analysis and Forecast European Union-Copernicus Marine Service https://doi.org/10.48670/MOI-00015

Argo Float Data and Metadata from Global Data Assembly Centre (Argo GDAC) Argo https://doi.org/10.17882/42182

Short summary

Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and monitor ocean health. Here, we demonstrate the use of the global array of BGC-Argo floats for the assessment of biogeochemical models. We first detail the handling of the BGC-Argo data set for model assessment purposes. We then present 23 assessment metrics to quantify the consistency of BGC model simulations with respect to BGC-Argo data.