Abstract

The quantitative description of marine systems is constrained by a major issue of scale separation: phytoplankton production processes occur at sub-centimeter scales, while the contribution to the Earth's biogeochemical cycles is expressed at much larger scales, up to the planetary one. In spite of vastly improved computing power and observational capabilities, the modeling approach has remained anchored to an old view that sees the microscales as unable to substantially affect larger ones. The lack of a widespread theoretical appreciation of the interactions between vastly different scales has led to the proliferation of numerical models with uncertain predictive capabilities. In this paper, we use the phenology of phytoplankton blooms as one example of a macroscopic ecosystem feature affected by microscale interactions. We describe two distinct mechanisms that produce patchiness within a productive water column: turbulent entrainment of less-productive water at the base of the mixed layer, and stirring by slow turbulence of a vertical phytoplankton gradient sustained by depth–dependent light availability. In current eddy–diffusive models, patchiness produced in this way is wiped out very rapidly, because the time scales of irreversible mixing largely overlap those of mechanical stirring. We propose a novel Lagrangian modeling framework that allows for the existence of microscale patchiness, even when that is not fully resolved. We show, with a mixture of theoretical arguments and numerical simulations of increasing realism, how the presence of patchiness, in turn, affects larger-scale properties, demonstrating that the timing of phytoplankton blooms and vertical variability of chlorophyll in the oceanic upper layers is determined by the mutual interplay between the stirring, mixing and growing processes.

Highlights

  • Marine phytoplankton are involved in several biogeochemical processess at the microbial ocean scale that affect entire ecosystems (Azam and Worden, 2004; Legendre et al, 2018)

  • We focus on the open ocean mixed layer and phytoplankton dynamics, which is at the base of the water column biogeochemistry (Legendre et al, 2018)

  • We argue that a clear distinction should be made between single cell Lagrangian models (Yamazaki and Kamykowski, 1991; Kamykowski et al, 1994), or agent-based models, in which the movement of a single individual is followed, but growth/death processes and cell division are not included, and Lagrangian ensembles (Figure 1B), where the super-individual concept (Scheffer et al, 1995) is used to describe the plankton population dynamics, FIGURE 1 | Schematic of different phytoplankton modeling approaches. (A) The vertical distribution of phytoplankton carbon and chlorophyll can be simulated according to vertical nutrient and light gradients and a turbulent field

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Summary

INTRODUCTION

Marine phytoplankton are involved in several biogeochemical processess at the microbial ocean scale that affect entire ecosystems (Azam and Worden, 2004; Legendre et al, 2018). Three classes of processes should be included to model marine biogeochemical processes: turbulent stirring, caused by fluid eddies, which displaces, stretches, and folds water volumes, increasing the gradients of the transported fields; irreversible mixing, caused by sub-microscale processes, which decreases these gradients; and growth (or decay) which changes the concentration of a field or a group of interacting fields by chemical or biological means. These findings reveal that irreversible mixing shapes the interaction between biogeochemical processes occurring at the microscales and larger scale transport processes Building on these results, we use two simulations of an open ocean phytoplankton bloom, with parameters and forcings appropriate for the North East Pacific (PAPA station), and for the Southern Ocean sub-Antarctic Zone (SAZ), to illustrate that the same mechanisms are at work in the open ocean, affecting important parameters such as plankton patchiness, primary productivity and phenology of the bloom

METHODS
Idealized Eulerian Models
The SAZ and PAPA Eulerian Models
Numerical Eulerian Scheme
Lagrangian Aquacosm Simulations
Pure Stirring and Mixing
Sverdrup’s Model Expanded
Microscales and Phytoplankton Phenology
DISCUSSION AND CONCLUSION
DATA AVAILABILITY STATEMENT
Full Text
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