Synoptic‐to‐planetary scale wind variability enhances phytoplankton biomass at ocean fronts

Abstract

In nutrient‐limited conditions, phytoplankton growth at fronts is enhanced by winds, which drive upward nutrient fluxes via enhanced turbulent mixing and upwelling. Hence, depth‐integrated phytoplankton biomass can be 10 times greater at isolated fronts. Using theory and two‐dimensional simulations with a coupled physical‐biogeochemical ocean model, this paper builds conceptual understanding of the physical processes driving upward nutrient fluxes at fronts forced by unsteady winds with timescales of 4–16 days. The largest vertical nutrient fluxes occur when the surface mixing layer penetrates the nutricline, which fuels phytoplankton in the mixed layer. At a front, mixed layer deepening depends on the magnitude and direction of the wind stress, cross‐front variations in buoyancy and velocity at the surface, and potential vorticity at the base of the mixed layer, which itself depends on past wind events. Consequently, mixing layers are deeper and more intermittent in time at fronts than outside fronts. Moreover, mixing can decouple in time from the wind stress, even without other sources of physical variability. Wind‐driven upwelling also enhances depth‐integrated phytoplankton biomass at fronts; when the mixed layer remains shallower than the nutricline, this results in enhanced subsurface phytoplankton. Oscillatory along‐front winds induce both oscillatory and mean upwelling. The mean effect of oscillatory vertical motion is to transiently increase subsurface phytoplankton over days to weeks, whereas slower mean upwelling sustains this increase over weeks to months. Taken together, these results emphasize that wind‐driven phytoplankton growth is both spatially and temporally intermittent and depends on a diverse combination of physical processes.

Submesoscales Enhance Storm‐Driven Vertical Mixing of Nutrients: Insights From a Biogeochemical Large Eddy Simulation

“…For example, if the wind stress includes a down‐front component, meaning a component in the direction of the thermal wind shear k / f ×∇ h b , then the Ekman flow tends to reduce the mixed layer buoyancy, energize submesoscale symmetric instabilities aligned parallel to the front, and may cause entrainment. But, if a component of the wind is up front, meaning opposite to the direction of the thermal wind shear, then the Ekman flow tends to increase the mixed layer buoyancy and may cause subduction (Brannigan, ; Whitt et al, ; Whitt et al, ). In the simulations presented here, the wind includes a down‐front component, and the horizontally averaged Ekman buoyancy flux EBF= τ x ⟨ M 2 ⟩ x , y / ρ 0 f , which is driven by the down‐front component of the wind stress τ x , reaches maximum magnitudes of 2×10 −7 m 2 /s 3 (or about 660 W/m 2 Ekman heat flux) when the magnitude of the stress is 0.6 N/m 2 (Figure ).…”

“…The biogeochemical model is a modified version of the NPZD (nutrient, phytoplankton, zooplankton, and detritus) model implemented by Whitt, Taylor, et al () and Whitt, Lévy, et al (), which is based on classic models like that of Fasham et al () and a previous NPZD implementation by Powell et al (). The biogeochemical model equations are …”

“…The initial Z and D profiles (not shown) have a similar vertical structure to P , with maximum concentrations of 0.22 and 0.35 mmol/m 3 , respectively. This equilibrium solution is calculated numerically by running one‐dimensional simulations of the biogeochemical model equations for 10 years with a one‐dimensional NPZD model described earlier (Whitt, ; Whitt, Taylor, et al, ; Whitt, Lévy, et al, ). In addition, numerical experimentation suggests that the resulting equilibrium profile is effectively independent of the initial conditions of N , P , Z , and D for the parameter set in Table .…”

“…In order to assess the effectiveness of some existing ocean model parameterizations at representing the horizontally averaged behavior of LES, results from the BLES models are compared to results from a one‐dimensional column model implemented in the regional ocean modeling system (ROMS+NPZD) (e.g., Powell et al, ; Whitt, Taylor, et al, ). Two ROMS configurations are used that can be viewed as parameterized analogs of the two LES configurations, one with parameterized submesoscales (ROMS,F) and one with no submesoscales (ROMS,NF).…”

“…During storms, winds can increase the vertical nutrient flux (thereby enhancing net phytoplankton growth in oligotrophic oceans), both by enhancing submesoscale vertical velocities (e.g., Brannigan, ; Capet et al, ; Lévy et al, ; Mahadevan & Tandon, ; Mahadevan et al, ; Thomas et al, ) and via entrainment/mixing in submesoscale fronts (e.g., Lévy et al, ; Whitt, Taylor, et al, ). In addition, winds can enhance or suppress the mixed layer restratification rate depending on the magnitude and orientation of the wind stress relative to the horizontal density gradient in the ocean mixed layer and the frequency content of the wind (Long et al, ; Mahadevan et al, ; Thomas & Ferrari, ; Whitt, Taylor, et al, ; Whitt, Lévy, et al, ) as well as the surface wave field (Haney et al, ; Li et al, ). For reviews of recent work on submesoscale impacts on biogeochemistry, see Klein and Lapeyre (), Lévy et al (), Mahadevan (), and Lévy et al ().…”