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Abstract The elemental ratios of carbon, nitrogen, and phosphorus (C:N:P) within organic matter play a key role in coupling biogeochemical cycles in the global ocean. At the cellular level, these ratios are controlled by physiological responses to the environment. But linking these cellular‐level processes to global biogeochemical cycles remains challenging. We present a novel model framework that combines knowledge of phytoplankton cellular functioning with global scale hydrographic data, to assess the role of variable carbon‐to‐phosphorus ratios (RC:P) on the distribution of export production. We implement a trait‐based mechanistic model of phytoplankton growth into a global biogeochemical inverse model to predict global patterns of phytoplankton physiology and stoichiometry that are consistent with both biological growth mechanisms and hydrographic carbon and nutrient observations. We compare this model to empirical parameterizations relatingRC:Pto temperature or phosphate concentration. We find that the way the model represents variable stoichiometry affects the magnitude and spatial pattern of carbon export, with globally integrated fluxes varying by up to 10% (1.3 Pg C yr−1) across models. Despite these differences, all models exhibit strong consistency with observed dissolved inorganic carbon and phosphate concentrations (R2 > 0.9), underscoring the challenge of selecting the most accurate model structure. We also find that the choice of parameterization impacts the capacity of changingRC:Pto buffer predicted export declines. Our novel framework offers a pathway by which additional biological information might be used to reduce the structural uncertainty in model representations of phytoplankton stoichiometry, potentially improving our capacity to project future changes.
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Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage
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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- Award ID(s):
- 1924215
- PAR ID:
- 10532821
- Publisher / Repository:
- Scholar's Archive at OSU
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Oregon State University
- Sponsoring Org:
- National Science Foundation
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