Preprints
https://doi.org/10.5194/bg-2021-2
https://doi.org/10.5194/bg-2021-2
20 Jan 2021
 | 20 Jan 2021
Status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

Defining BGC-Argo-based metrics of ocean health and biogeochemical functioning for the evaluation of global ocean models

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

Abstract. Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and ocean health. Classically, validation of such models relies on comparison with surface quantities from satellite (such as chlorophyll-a concentrations), climatologies, or sparse in situ data (such as cruises observations, and permanent fixed oceanic stations). However, these datasets are not fully suitable to assess how models represent many climate-relevant biogeochemical processes.  These limitations now begin to be overcome with the availability of a large number of vertical profiles of light, pH, oxygen, nitrate, chlorophyll-a concentrations and particulate backscattering acquired by the Biogeochemical-Argo (BGC-Argo) floats network. Additionally, other key biogeochemical variables such as dissolved inorganic carbon and alkalinity, not measured by floats, can be predicted by machine learning-based methods applied to float oxygen concentrations. Here, we demonstrate the use of the global array of BGC-Argo floats for the validation of biogeochemical models at the global scale. We first present 18 key metrics of ocean health and biogeochemical functioning to quantify the success of BGC model simulations. These metrics are associated with the air-sea CO2 flux, the biological carbon pump, oceanic pH, oxygen levels and Oxygen Minimum Zones (OMZs). The metrics are either a depth-averaged quantity or correspond to the depth of a particular feature. We also suggest four diagnostic plots for displaying such metrics.

Alexandre Mignot et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-2', Anonymous Referee #1, 17 Feb 2021
  • RC2: 'Comment on bg-2021-2', Marcello Vichi, 07 Mar 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-2', Anonymous Referee #1, 17 Feb 2021
  • RC2: 'Comment on bg-2021-2', Marcello Vichi, 07 Mar 2021

Alexandre Mignot et al.

Alexandre Mignot et al.

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