Preprints
https://doi.org/10.5194/egusphere-2026-3713
https://doi.org/10.5194/egusphere-2026-3713
15 Jul 2026
 | 15 Jul 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

FRISO (v1.0): A physiological carbon flux model for simulating algal 13C-fractionation

Yannick F. Bats, Appy Sluijs, and Gert-Jan Reichart

Abstract. The CO2-dependaency of stable carbon isotope fractionation (εp) during marine algal carbon acquisition is widely used as a proxy for past atmospheric pCO2. However, a mechanistic model linking εp and pCO2 that explains observations in the modern ocean, culture experiments and across Pleistocene glacial-interglacial cycles is still lacking. Here, we present a multi-compartment model of an algal cell that simulates environmental control on passive CO2 diffusion, active HCO3 transport, and 13C fractionation during C-fixation. Relative to previous models, our main new implementations include light-sensitive HCO3 acquisition and calcification, and daylength-dependent pyrenoid permeability, based on physiological expectations. The model is fitted to and tested with culture data of multiple dinoflagellate species and the coccolithophore Gephyrocapsa huxleyi. This shows robust performance across a wide range of parameter values from culture experiments, implying an appropriate representation of the mechanistic control of 13C-fractionation in algal organic matter, potentially paving the way for sedimentary work.

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Yannick F. Bats, Appy Sluijs, and Gert-Jan Reichart

Status: open (until 09 Sep 2026)

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Yannick F. Bats, Appy Sluijs, and Gert-Jan Reichart
Yannick F. Bats, Appy Sluijs, and Gert-Jan Reichart
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Short summary
Fossil remains of algae can help us estimate ancient carbon dioxide levels, but current methods are often inaccurate. We built a numeric model of an algal cell to better understand how carbon is processed in these organisms. Our research reveals that light and calcification strongly influence the chemical signals preserved in algae. By including these factors, our model accurately matches results from lab studies, suggesting it can improve reconstructions of Earth’s climate and carbon cycle.
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