Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2419-2021
https://doi.org/10.5194/gmd-14-2419-2021
Model description paper
 | 
05 May 2021
Model description paper |  | 05 May 2021

BFM17 v1.0: a reduced biogeochemical flux model for upper-ocean biophysical simulations

Katherine M. Smith, Skyler Kern, Peter E. Hamlington, Marco Zavatarelli, Nadia Pinardi, Emily F. Klee, and Kyle E. Niemeyer

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Cited articles

Abraham, E. R.: The generation of plankton patchiness by turbulent stirring, Nature, 391, 577–580, 1998. a
Ammerman, J. W., Hood, R. R., Case, D. A., and Cotner, J. B.: Phosphorus Deficiency in the Atlantic: An Emerging Paradigm in Oceanography, EOS, 84, 165–170, 2003. a, b, c
Anderson, T. R.: Plankton functional type modelling: running before we can walk?, J. Plankton Res., 27, 1073–1081, 2005. a, b
Ayata, S. D., Levy, M., Aumont, O., Siandra, 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, 2013. a, b, c, d, e, f, g, h
Baretta-Bekker, J. G., Baretta, J. W., and Ebenhoh, W.: Microbial dynamics in the marine ecosystem model ERSEM II with decoupled carbon assimilation and nutrient uptake, J. Sea Res., 38, 195–211, 1997. a
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Short summary
We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The model provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of real-world data can be achieved with a small number of variables.
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