[A-OS12] Marine ecosystems and biogeochemical cycles: theory, observation and modeling

[AOS12-13] INSTANTANEOUS ACCLIMATION ALLOWS COMPUTATIONALLY EFFICIENT MODELLING OF PLANKTON ECOPHYSIOLOGY AND NUTRIENT AND CARBON CYCLING

*S. Lan Smith¹, Onur Kerimoglu², Prima Anugerahanti³, Markus Pahlow⁴, Yoshio Masuda⁵, Yasuhiro Yamanaka⁵, Yoshikazu Sasai¹ (1.Marine Ecosystem Dynamics Research Group, RIGC, JAMSTEC, 2.ICBM, University of Oldenburg, Germany, 3.University of Liverpool, UK, 4.GEOMAR, Kiel University, Germany, 5.Hokkaido University, Sapporo)

Keywords:plankton ecology, ecosystem model, numerical model, biogeochemistry

Spatio-temporal variability of ambient nutrient and light conditions in the ocean induce acclimative eco-physiological responses in phytoplankton, resulting in substantial variations in their chlorophyll (Chl) content and Chl:C:nutrient ratios. These variations substantially impact observed biogeochemical patterns, with implications for feedbacks with changing climate. However, capturing such variations poses formidable challenges for ocean biogeochemical and Earth System modelling. Therefore, for the sake of simplicity and computational efficiency, many large-scale biogeochemical models have ignored this flexibility, compromising their realism. Although some models account for the dynamics of phytoplankton composition by adding state variables (one for each element or pigment considered), that approach substantially increases computational requirements in spatially explicit (1-D and 3-D) setups. The Instantaneous Acclimation (IA) approach addresses these challenges by assuming that Chl:C:nutrient ratios are instantly optimized locally (within each modelled grid cell, at each time step), such that they can be resolved as diagnostic variables. We review recently published studies, which have shown that IA-based models that represent phytoplankton biomass in terms of only one nutrient tracer provide a computationally efficient and accurate approach for capturing variations in phytoplankton composition in spatially explicit models. We also present more recent tests of IA in models that explicitly represent carbon biomass, and show that this approach conserves total carbon and nitrogen, in idealized 0-D model setups. Acclimative models resolve chlorophyll patterns and nutrient and growth dynamics not captured by typical fixed-stoichiometry models. Compared to their fully dynamic counterparts, IA models could allow greater computational efficiency without substantially compromising realism in many model applications.