Modelling a simple mechanism for the formation of phytoplankton thin layers using large-eddy simulation: in situ growth

Abstract

A curious phenomenon found in phytoplankton communities is the forming of socalled thin layers, wherein phytoplankton biomass can stretch out kilometres in the horizontal but only a few metres in the vertical. These layers are typically found at the pycnocline, just below the surface mixed layer. Thin layers are usually attributed to a range of complex environmental and species-dependent factors. However, we believe that, given the frequency at which this phenomenon is observed, a simpler mechanism is at play. In this study, we found that phytoplankton thin layers can be attributed simply to a decreasing light availability with depth, when there is an abundance of nutrients in the euphotic zone and below the mixed layer. This mechanism was ascertained using a number of modelling approaches ranging in complexity from analytical solutions of a simple 1-dimensional plankton model to a 3-dimensional biophysical model incorporating large-eddy simulation. The conditions which, according to the results of our study, allow thin layers to form are ubiquitous in the coastal ocean and are therefore a likely candidate explanation as to why planktonic thin layers are so frequently observed.

Modeling Physical and Physiological Processes Reveals the Role of Turbulence in the Prerequisites for Microcystis Blooms

“…In this study, turbulence is calculated by the RANS method due to its advantages of lower mesh quality, higher computational efficiency, and wider applicability (Kang & Sotiropoulos, 2012). However, RANS has lower turbulence computation accuracy, direct numerical simulation of turbulence (DNS) and large eddy simulation (LES) can compensate for this weakness but with lower computationally efficiency, therefore are usually only coupled with simple biological models (Brereton et al, 2018(Brereton et al, , 2020Whitt et al, 2019). In future studies, with the improvement of computational efficiency, a more accurate characterization of turbulence using LES or DNS methods will be more profound for understanding Microcystis aggregation and growth processes under the influence of turbulence.…”

The Route to Spring Phytoplankton Blooms Simulated by a Lagrangian Plankton Model

“…In the absence of particle inertia and terminal velocity, (7) becomes equivalent to (1), if is assumed and the depth of a particle is replaced by the depth of a grid. The same model has recently been applied to investigate the formation of phytoplankton thin layers (Brereton et al., 2020).…”