10:00 〜 10:15
[AOS13-05] Instantaneous Acclimation allows computationally efficient modelling of the biogeochemical impacts of plankton ecophysiology
キーワード:nutrients, photo-acclimation, phytoplankton, Earth System Modelling, marine ecosystems, biogeochemistry
Many coupled physical-biogeochemical models reproduce large-scale patterns of chlorophyll, primary
production and biogeochemistry, but they often underestimate observed variability and gradients.
One common reason is insufficient representation of systematic variations in the elemental
composition and chlorophyll (Chl) content of phytoplankton. Chl is widely taken as a proxy
for phytoplankton biomass, despite well known variations in Chl:biomass ratios as an acclimative
response to changing environmental conditions. However, for the sake of simplicity and computational
efficiency, many large scale biogeochemical models ignore this flexibility, compromising
their ability to capture phytoplankton dynamics. 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 timestep), such that they can be resolved as diagnostic variables.However, the IA approach
was developed in a 0-D model and has not yet been rigorously tested in spatially explicit
models. Here we present tests of IA in various spatially explicit models, including: an idealized,
1D vertical setup in the Framework for Aquatic Biogeochemical Models (FABM) coupled with the
General Ocean Turbulence Model (GOTM), as well as a 3-D regional model and another 3-D
global model. We show that the IA model and a fully dynamic, otherwise equivalently acclimative
(DA) variant with an additional state variable behave similarly, and that both resolve chlorophyll
patterns and nutrient and growth dynamics not captured by the typical fixed-stoichiometry (FS)
models [e.g., 1].
(1) Kerimoglu, O.; Anugerahanti, P.; Smith, S. Geosci. Model Dev. Discuss., in review 2021 , DOI:
10.5194/gmd-2020-396 .
production and biogeochemistry, but they often underestimate observed variability and gradients.
One common reason is insufficient representation of systematic variations in the elemental
composition and chlorophyll (Chl) content of phytoplankton. Chl is widely taken as a proxy
for phytoplankton biomass, despite well known variations in Chl:biomass ratios as an acclimative
response to changing environmental conditions. However, for the sake of simplicity and computational
efficiency, many large scale biogeochemical models ignore this flexibility, compromising
their ability to capture phytoplankton dynamics. 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 timestep), such that they can be resolved as diagnostic variables.However, the IA approach
was developed in a 0-D model and has not yet been rigorously tested in spatially explicit
models. Here we present tests of IA in various spatially explicit models, including: an idealized,
1D vertical setup in the Framework for Aquatic Biogeochemical Models (FABM) coupled with the
General Ocean Turbulence Model (GOTM), as well as a 3-D regional model and another 3-D
global model. We show that the IA model and a fully dynamic, otherwise equivalently acclimative
(DA) variant with an additional state variable behave similarly, and that both resolve chlorophyll
patterns and nutrient and growth dynamics not captured by the typical fixed-stoichiometry (FS)
models [e.g., 1].
(1) Kerimoglu, O.; Anugerahanti, P.; Smith, S. Geosci. Model Dev. Discuss., in review 2021 , DOI:
10.5194/gmd-2020-396 .