Articles | Volume 14, issue 10
Geosci. Model Dev., 14, 6025–6047, 2021
https://doi.org/10.5194/gmd-14-6025-2021
Geosci. Model Dev., 14, 6025–6047, 2021
https://doi.org/10.5194/gmd-14-6025-2021
Model description paper
08 Oct 2021
Model description paper | 08 Oct 2021

FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth

Onur Kerimoglu et al.

Related authors

Interactive impacts of meteorological and hydrological conditions on the physical and biogeochemical structure of a coastal system
Onur Kerimoglu, Yoana G. Voynova, Fatemeh Chegini, Holger Brix, Ulrich Callies, Richard Hofmeister, Knut Klingbeil, Corinna Schrum, and Justus E. E. van Beusekom
Biogeosciences, 17, 5097–5127, https://doi.org/10.5194/bg-17-5097-2020,https://doi.org/10.5194/bg-17-5097-2020, 2020
Short summary
The acclimative biogeochemical model of the southern North Sea
Onur Kerimoglu, Richard Hofmeister, Joeran Maerz, Rolf Riethmüller, and Kai W. Wirtz
Biogeosciences, 14, 4499–4531, https://doi.org/10.5194/bg-14-4499-2017,https://doi.org/10.5194/bg-14-4499-2017, 2017
Short summary

Related subject area

Biogeosciences
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022,https://doi.org/10.5194/gmd-15-4313-2022, 2022
Short summary
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022,https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model
Nicolas Azaña Schnedler-Meyer, Tobias Kuhlmann Andersen, Fenjuan Rose Schmidt Hu, Karsten Bolding, Anders Nielsen, and Dennis Trolle
Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022,https://doi.org/10.5194/gmd-15-3861-2022, 2022
Short summary
Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET
Hamze Dokoohaki, Bailey D. Morrison, Ann Raiho, Shawn P. Serbin, Katie Zarada, Luke Dramko, and Michael Dietze
Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022,https://doi.org/10.5194/gmd-15-3233-2022, 2022
Short summary
Predicting global terrestrial biomes with the LeNet convolutional neural network
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022,https://doi.org/10.5194/gmd-15-3121-2022, 2022
Short summary

Cited articles

Aksnes, D. L. and Egge, J.: A theoretical model for nutrient uptake in phytoplankton, Mar. Ecol. Prog. Ser., 70, 65–72, 1991. a
Anderson, T. R. and Pondaven, P.: Non-redfield carbon and nitrogen cycling in the Sargasso Sea: Pelagic imbalances and export flux, Deep-Sea Res. Pt. I, 50, 573–591, https://doi.org/10.1016/S0967-0637(03)00034-7, 2003. a
Anugerahanti, P., Kerimoglu, O., and Smith, S. L.: Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition, Front. Mar. Sci., 8, 675428, https://doi.org/10.3389/fmars.2021.675428, 2021. a, b
Armstrong, R. A.: An optimization-based model of iron–light–ammonium colimitation of nitrate uptake and phytoplankton growth, Limnol. Oceanogr., 44, 1436–1446, https://doi.org/10.4319/lo.1999.44.6.1436, 1999. a
Ayata, S. D., Lévy, M., Aumont, O., Sciandra, A., Sainte-Marie, J., Tagliabue, A., and Bernard, O.: Phytoplankton growth formulation in marine ecosystem models: Should we take into account photo-acclimation and variable stoichiometry in oligotrophic areas?, J. Marine Syst., 125, 29–40, https://doi.org/10.1016/j.jmarsys.2012.12.010, 2013. a, b
Download
Short summary
In large-scale models, variations in cellular composition of phytoplankton are often insufficiently represented. Detailed modeling approaches exist, but they require additional state variables that increase the computational costs. In this study, we test an instantaneous acclimation model in a spatially explicit setup and show that its behavior is mostly similar to that of a variant with an additional state variable but different from that of a fixed composition variant.