Incorporating the stable carbon isotope 13C in the ocean biogeochemical component of the Max Planck Institute Earth System Model

• Main Authors: Liu, Bo, Six, Katharina D., Ilyina, Tatiana
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications (EGU) 2021
• Subjects: Pacific; Indian; Eide; North Atlantic
• Online Access: https://oceanrep.geomar.de/id/eprint/52513/; https://oceanrep.geomar.de/id/eprint/52513/1/bg-2021-32.pdf; https://doi.org/10.5194/bg-2021-32

Direct comparison between paleo oceanic δ13C records and model results facilitates assessing simulated distributions and properties of water masses in the past. To accomplish this, we include a new representation of the stable carbon isotope 13C into the HAMburg Ocean Carbon Cycle model (HAMOCC), the ocean biogeochemical component of the Max Planck Institute Earth System Model (MPI-ESM). 13C is explicitly resolved for all existing oceanic carbon pools. We account for fractionation during air-sea gas exchange and for biological fractionation εp associated with photosynthetic carbon fixation during phytoplankton growth. We examine two εp parameterisations of different complexity: εpPopp varies with surface dissolved CO2 concentration (Popp et al., 1989), while εpLaws additionally depends on local phytoplankton growth rates (Laws et al., 1995). When compared to observations of δ13C in dissolved inorganic carbon (DIC), both parameterisations yield similar performance. However, with regard to δ13C in particulate organic carbon εpPopp shows a considerably improved performance than εpLaws, because the latter results in a too strong preference for 12C. The model also well reproduces the oceanic 13C Suess effect, i.e. the intrusion of the isotopically light anthropogenic CO2 into the ocean, based on comparison to other existing 13C models and to observation-based oceanic carbon uptake estimates over the industrial period. We further apply the approach of Eide et al. (2017a), who derived the first global oceanic 13C Suess effect estimate based on observations, to our model data that has ample spatial and temporal coverage. With this we are able to analyse in detail the underestimation of 13C Suess effect by this approach as it has been noted by Eide et al. (2017a). Based on our model we find underestimations of 13C Suess effect at 200 m by 0.24 ‰ in the Indian Ocean, 0.21 ‰ in the North Pacific, 0.26 ‰ in the South Pacific, 0.1 ‰ in the North Atlantic and 0.14 ‰ in the South Atlantic. We attribute the major sources of the ...