Sensitivity of a 3D Shelf Sea Ecosystem Model to Parameterizations of the Underwater Light Field

Daniel Thewes, Emil V. Stanev, Oliver Zielinski

In: Frontiers in Marine Science (FMarS) 6 816 Seiten 1-15 Lausanne Schweiz 1/2020.


The inherent optical properties of water in the North Sea vary widely in space and time. Their impact on the performance of a 3D-ecosystem-model of the North Sea needs to be critically evaluated, which is the major research issue in the present paper, specifically the horizontal variability of turbidity. We have performed a sensitivity analysis to a modification of a common approach of light treatment that is both valid for the North Sea, as well as computationally efficient to implement within a 3D-ecosystem-model. Using a coupled hydrodynamical model (Regional Ocean Modeling System, ROMS) and biological model (Carbon Silicate and Nitrogen Ecosystem model, CoSiNE), we found that simple changes to the original parameterization can yield significant improvements. ROMS-CoSiNE is shown to be suitable for use in a coupled ecosystem model of the North Sea. The model accurately reproduces the seasonal cycle of primary production in terms of timing and magnitude, while still being more affordable in comparison to full hyperspectral treatment or solving the radiative transfer equation. The modification introduces vertically increasing attenuation that is stronger in shallow domains, in a way that is similar to attenuation due to sediment. The resulting reduction of light availability leads to strongly reduced phytoplankton growth in shallow areas with high turbidity and weak nutrient limitation. Areas of depths between 50 and 100 m show greatest relative change with respect to their total ranges, while the deepest areas remain largely unchanged. We found that the consideration of spacial variability of light attenuation is necessary when modeling a heterogeneous domain, such as the North Sea.

Sensitivity_of_a_3D_Shelf_Sea_Ecosystem_Model_to_Parameterizations_of_the_Unterwater_Light_Field.pdf (pdf, 3 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence