Influence of oceanic conditions in the energy transfer efficiency estimation of a micronekton model
<p>Micronekton – small marine pelagic organisms around 1–10 cm in size – are a key component of the ocean ecosystem, as they constitute the main source of forage for all larger predators. Moreover, the mesopelagic component of micronekton that undergoes diel vertical migration (DVM)...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-02-01
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| Series: | Biogeosciences |
| Online Access: | https://www.biogeosciences.net/17/833/2020/bg-17-833-2020.pdf |
| Summary: | <p>Micronekton – small marine pelagic organisms around 1–10 cm in size – are a key component of the ocean ecosystem, as they constitute 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 CO<span class="inline-formula"><sub>2</sub></span> in the deep ocean: this is known as the “biological pump”. SEAPODYM-MTL is a spatially explicit dynamical model of micronekton. It simulates six functional groups of vertically migrant (DVM) and nonmigrant (no DVM) 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 production of micronekton biomass. Since these coefficients are not directly measurable, a data assimilation method is used to estimate them. In this study, Observing System Simulation Experiments (OSSEs) are used at a global scale to explore the response of oceanic regions regarding energy transfer coefficient estimation. In our experiments, we obtained different results for spatially distinct sampling regions based on their prevailing ocean conditions. According to our study, ideal sampling areas are warm and productive waters associated with weak surface currents like the eastern side of tropical oceans. These regions are found to reduce the error of estimated coefficients by 20 % compared to cold and more dynamic sampling regions.</p> |
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| ISSN: | 1726-4170 1726-4189 |