Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1895-2025
https://doi.org/10.5194/gmd-18-1895-2025
Model description paper
 | 
19 Mar 2025
Model description paper |  | 19 Mar 2025

The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces

Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum

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Computational library for the Nutrient-Unicellular-Multicellular plankton modeling framework v. 1.0
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Cited articles

Andersen, K. H.: Release v0.91 Kenhasteandersen/NUMmodel, GitHub [code], https://github.com/Kenhasteandersen/NUMmodel/releases/tag/v0.91 (last access: 14 February 2023), 2023. 
Andersen, K. H. and Visser, A. W.: From cell size and first principles to structure and function of unicellular plankton communities, Prog. Oceanogr., 213, 102995, https://doi.org/10.1016/j.pocean.2023.102995, 2023. 
Andersen, K. H., Aksnes, D. L., Berge, T., Fiksen, O., and Visser, A.: Modelling emergent trophic strategies in plankton, J. Plankton Res., 37, 862–868, https://doi.org/10.1093/plankt/fbv054, 2015. 
Archibald, K. M., Dutkiewicz, S., Laufkotter, C., and Moeller, H. V.: Thermal Responses in Global Marine Planktonic Food Webs Are Mediated by Temperature Effects on Metabolism, J. Geophys. Res.-Oceans, 127, e2022JC018932, https://doi.org/10.1029/2022jc018932, 2022. 
Bilal, N.: Implementation of Sobol's Method of Global Sensitivity Analysis to a Compressor Simulation Model, International Compressor Engineering Conference, Herrick Laboratoriesm, Purdue University, https://docs.lib.purdue.edu/icec/2385/ (last access: 25 August 2023), 2014. 
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Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
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