Sensitivity of phytoplankton distributions to vertical mixing along a North Atlantic transect
Using in situ data of upper ocean vertical mixing along a transect in the North Atlantic and a one-dimensional phytoplankton growth model, we study the sensitivity of the surface phytoplankton concentration to vertical mixing distributions. The study is divided into two parts. In the first part, the...
Published in: | Ocean Science |
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Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2014
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Subjects: | |
Online Access: | https://doi.org/10.5194/os-10-993-2014 https://doaj.org/article/f8fad6c2a7014c4eba7dac50881a5cdd |
Summary: | Using in situ data of upper ocean vertical mixing along a transect in the North Atlantic and a one-dimensional phytoplankton growth model, we study the sensitivity of the surface phytoplankton concentration to vertical mixing distributions. The study is divided into two parts. In the first part, the model is calibrated to the observations. The optical model parameters are determined from measurements of the light attenuation. The biological parameters are calibrated to three different reference stations with observed vertical profiles of the chlorophyll a (Chl a ) concentration and the nutrient concentration. In the second part, the sensitivity of the three model calibrations to the vertical mixing is studied. Therefore measured vertical mixing profiles are applied to the model. These mixing profiles are based on the measurements along the transect and are treated as a set of possible mixing situations of the North Atlantic. Results show that shifts in vertical mixing are able to induce a transition from an upper chlorophyll maximum to a deep one and vice versa. Furthermore, a clear correlation between the surface phytoplankton concentration and the mixing induced nutrient flux is found for nutrient-limited cases. This may open up the possibility to extract characteristics of vertical mixing from satellite ocean colour data using data-assimilation methods. |
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