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Realistic model representation of ocean phytoplankton is important for simulating nutrient cycles and the biological carbon pump, which affects atmospheric carbon dioxide (pCO2) concentrations and, thus, climate. Until recently, most models assumed constant ratios (or stoichiometry) of phosphorous (P), nitrogen (N), silicon (Si), and carbon (C) in phytoplankton, despite observations indicating systematic variations. Here, we investigate the effects of variable stoichiometry on simulated nutrient distributions, plankton community compositions, and the C cycle in the preindustrial (PI) and glacial oceans. Using a biogeochemical model, a linearly increasing P:N relation to increasing PO4 is implemented for ordinary phytoplankton (PO), and a nonlinearly decreasing Si:N relation to increasing Fe is applied to diatoms (PDiat). C:N remains fixed. Variable P:N affects modeled community composition through enhanced PO4 availability, which increases N-fixers in the oligotrophic ocean, consistent with previous research. This increases the NO3 fertilization of PO, the NO3 inventory, and the total plankton biomass. The accuracy of modeled surface nutrients is relatively unchanged. Conversely, variable Si:N shifts south the Southern Ocean’s meridional surface silicate gradient, which aligns better with observations, but depresses PDiat growth globally. In Last Glacial Maximum simulations, PO respond to more oligotrophic conditions by increasing their C:P. This strengthens the biologically mediated C storage such that dissolved organic (inorganic) C inventories increase by 34-40 (38-50) Pg C and 0.7-1.2 Pg yr-1 more particulate C is exported into the interior ocean. Thus, an additional 13-14 ppm of pCO2 difference from PI levels results, improving model agreement with glacial observations.
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Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition
Chlorophyll (Chl) is widely taken as a proxy for phytoplankton biomass, despite well-known variations in Chl:C:biomass ratios as an acclimative response to changing environmental conditions. For the sake of simplicity and computational efficiency, many large scale biogeochemical models ignore this flexibility, compromising their ability to capture phytoplankton dynamics. Here we evaluate modelling approaches of differing complexity for phytoplankton growth response: fixed stoichiometry, fixed stoichiometry with photoacclimation, classical variable-composition with photoacclimation, and Instantaneous Acclimation with optimal resource allocation. Model performance is evaluated against biogeochemical observations from time-series sites BATS and ALOHA, where phytoplankton composition varies substantially. We analyse the sensitivity of each model variant to the affinity parameters for light and nutrient, respectively. Models with fixed stoichiometry are more sensitive to parameter perturbations, but the inclusion of photoacclimation in the fixed-stoichiometry model generally captures Chl observations better than other variants when individually tuned for each site and when using similar parameter sets for both sites. Compared to the fixed stoichiometry model including photoacclimation, models with variable C:N ratio perform better in cross-validation experiments using model-specific parameter sets tuned for the other site; i.e., they are more portable. Compared to typical variable composition approaches, instantaneous acclimation, which requires fewer state variables, generally yields better performance but somewhat lower portability than the fully dynamic variant. Further assessments using objective optimisation and more contrasting stations are suggested.
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- Award ID(s):
- 1756517
- PAR ID:
- 10342384
- Date Published:
- Journal Name:
- Frontiers in Marine Science
- Volume:
- 8
- ISSN:
- 2296-7745
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fmars.2021.675428
