A Lagrangian View of Spring Phytoplankton Blooms

Abstract

The mechanisms of spring phytoplankton blooms are investigated from a Lagrangian framework by using a Lagrangian NPZD model that can track the movement and transfers of nutrient parcels in a turbulent environment. The model reveals that the onset of spring blooms depends on the cumulative euphotic age, which is the total time that inorganic nutrient is exposed to light before the photosynthetic conversion to phytoplankton biomass. A spring bloom, defined as a tenfold increase of near‐surface phytoplankton, occurs when this cumulative euphotic age is approximately μeff−1·italicln⁡10, where μeff is the effective growth rate in the euphotic layer, regardless of the underlying mechanism. If the turbulent layer depth is shallower than the critical depth and turbulence is strong, nutrient parcels accumulate enough light exposure through multiple entries to the sun‐lit zone near the surface. If turbulence is weak, as that considered in the critical turbulence theory, the accumulation of the light exposure depends on the residence time of the nutrients parcels near the surface. The spectral shape of the cumulative euphotic age can clearly distinguish these two modes of spring blooms. The spectrum shows a peak at the theoretical growth timescale when multiple entries become important, while it shows a maximum near age zero that decays with age when the near‐surface residence time becomes important. Mortality increases the cumulative euphotic age necessary for a bloom but does not affect the spectral shape, suggesting that it does not alter the primary mechanism behind the accumulation of cumulative euphotic age.

Stirring, Mixing, Growing: Microscale Processes Change Larger Scale Phytoplankton Dynamics

“…These results, for p = 10 −6 or lower, bear a strong resemblance to the statistics of the BGC-Argo float observations in the Southern Ocean ( Figure 6 in Carranza et al, 2018), but suggest that, rather than by external forcing, chlorophyll variability is mostly caused by differences in the Lagrangian histories of water parcels (Kida and Ito, 2017;Baudry et al, 2018) but modulated by irreversible mixing. We remark that BGC-Argo floats are not high-resolution chlorophyll profilers (Carranza et al, 2018), and can't accurately represent vertical fluctuations on scales smaller than a few meters.…”

“…Therefore, nearly all Lagrangian models (but see Dippner, 1998) neglect to include irreversible mixing processes. In the sporadic cases where Lagrangian and Eulerian formulations have been compared, this issue appears to have been overlooked (Wolf and Woods, 1988;Lande and Lewis, 1989;McGillicuddy, 1995;Kida and Ito, 2017;Baudry et al, 2018), even though it may lead to unrealistic, even paradoxical outcomes.…”

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

“…Figure 7 shows the PDF of τ r . Here τ r is calculated only during the daytime when μ = 1, and the euphotic zone is defined by the compensation depth ( z p = 23 m), as in Kida and Ito (2017). On a spring day of CON, τ r is divided into two groups of plankton; τ r = 0 h and τ r = 12 h. It is due to the fact that z p happens to be very close to h d in this case.…”

“…It is suggested that the CTH is applied when the mixed layer is deep and turbulence is weak, whereas the CDH is applied when the mixed layer is shallow and turbulence is strong (Huisman et al., 1999; Enriquez & Taylor, 2015; Kida & Ito, 2017; Taylor & Ferrari, 2011). Taylor and Ferrari (2011) proposed the condition based on the CTH.…”

“…The ideal approach to investigate this is to track the motion of individual plankton cells as Lagrangian particles. It led several scientists to take the Lagrangian approach for the study of spring blooms (Falkowski & Wirick, 1981; Kamykowski et al., 1994; Kida & Ito, 2017; Woods & Onken, 1982). To our knowledge, all previous models calculate the vertical motion of Lagrangian plankton cells by random walks instead of using the realistic turbulence field in the upper ocean.…”