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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-353
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-353
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 06 Sep 2019

Submitted as: research article | 06 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Influence of oceanic conditions in the energy transfer efficiency estimation of a micronekton model

Audrey Delpech1,2, Anna Conchon2,3, Olivier Titaud2, and Patrick Lehodey2 Audrey Delpech et al.
  • 1Laboratoire d'Etudes Géophysiques et d'Océanographie Spatiale, LEGOS - UMR 5566 CNRS/CNES/IRD/UPS, Toulouse, France
  • 2Collecte Localisation Satellite, CLS, Toulouse, France
  • 3Mercator Ocean, Toulouse, France

Abstract. Micronekton – small marine pelagic organisms mostly in the size range 1–10 cm – is a key component of the ocean ecosystem, as it constitutes the main source of forage for all larger predators. Moreover, the mesopelagic component of micronekton that undergoes Diel Vertical Migration (DVM) likely plays a key role in the transfer and storage of CO2 in the deep ocean: the so-called ‘biological pump’ mechanism. SEAPODYM-MTL is a spatially explicit dynamical model of micronekton. It simulates six functional groups of migrant and non-migrant micronekton, in the epipelagic and mesopelagic layers. Coefficients of energy transfer efficiency between primary production and each group are unknown. But they are essential as they control the predicted biomass. Since these coefficients are not directly measurable, a data assimilation method is used to estimate them. In this study, Observing System Simulation Experiments (OSSE) in the framework of twin experiments are used to test various observation networks at a global scale regarding energy transfer coefficients estimation. Observational networks show a variety of performances. It appears that environmental conditions are crucial to determine network efficiency. According to our study, ideal sampling areas are warm, non-dynamic and productive waters like the eastern side of tropical Oceans. These regions are found to reduce the error of estimated coefficients by 20 % compared to cold and dynamic sampling regions. The results are discussed in term of interactions between physical and biological processes.

Audrey Delpech et al.
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Audrey Delpech et al.
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Short summary
Micronekton is an important, yet poorly known, component of the trophic chain, which partly contributes to the storage of CO2 in the deep ocean thanks to biomass vertical migrations. In this study, we characterize the ideal sampling regions to estimate the amount of biomass that undergoes theses migrations. We find that observations made in warm, non-dynamic and productive waters reduce the error of the estimation by 20%. This result should likely serve for future in-situ network deployment.
Micronekton is an important, yet poorly known, component of the trophic chain, which partly...
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