# Wind- and convection-driven mixing in an ocean surface boundary layer

This example simulates mixing by three-dimensional turbulence in an ocean surface boundary layer driven by atmospheric winds and convection. It demonstrates:

• How to set-up a grid with varying spacing in the vertical direction
• How to use the SeawaterBuoyancy model for buoyancy with a linear equation of state.
• How to use a turbulence closure for large eddy simulation.
• How to use a function to impose a boundary condition.

## Install dependencies

First let's make sure we have all required packages installed.

using Pkg
pkg"add Oceananigans, CairoMakie"

We start by importing all of the packages and functions that we'll need for this example.

using Random
using Printf
using CairoMakie

using Oceananigans
using Oceananigans.Units: minute, minutes, hour

## The grid

We use 32²×24 grid points with 2 m grid spacing in the horizontal and varying spacing in the vertical, with higher resolution closer to the surface. Here we use a stretching function for the vertical nodes that maintains relatively constant vertical spacing in the mixed layer, which is desirable from a numerical standpoint:

Nz = 24          # number of points in the vertical direction
Lz = 32          # (m) domain depth

refinement = 1.2 # controls spacing near surface (higher means finer spaced)
stretching = 12  # controls rate of stretching at bottom

# Normalized height ranging from 0 to 1
h(k) = (k - 1) / Nz

# Linear near-surface generator
ζ₀(k) = 1 + (h(k) - 1) / refinement

# Bottom-intensified stretching function
Σ(k) = (1 - exp(-stretching * h(k))) / (1 - exp(-stretching))

# Generating function
z_faces(k) = Lz * (ζ₀(k) * Σ(k) - 1)

grid = RectilinearGrid(CPU();
size = (32, 32, Nz),
x = (0, 64),
y = (0, 64),
z = z_faces)
32×32×24 RectilinearGrid{Float64, Periodic, Periodic, Bounded} on CPU with 3×3×3 halo
├── Periodic x ∈ [0.0, 64.0)  regularly spaced with Δx=2.0
├── Periodic y ∈ [0.0, 64.0)  regularly spaced with Δy=2.0
└── Bounded  z ∈ [-32.0, 0.0] variably spaced with min(Δz)=1.11123, max(Δz)=2.53571

We plot vertical spacing versus depth to inspect the prescribed grid stretching:

fig = Figure(resolution=(1200, 800))
ax = Axis(fig[1, 1], ylabel = "Depth (m)", xlabel = "Vertical spacing (m)")
lines!(ax, grid.Δzᵃᵃᶜ[1:grid.Nz], grid.zᵃᵃᶜ[1:grid.Nz])
scatter!(ax, grid.Δzᵃᵃᶜ[1:Nz], grid.zᵃᵃᶜ[1:Nz])

save("ocean_wind_mixing_convection_grid_spacing.svg", fig) ## Buoyancy that depends on temperature and salinity

We use the SeawaterBuoyancy model with a linear equation of state,

buoyancy = SeawaterBuoyancy(equation_of_state=LinearEquationOfState(thermal_expansion = 2e-4,
haline_contraction = 8e-4))
SeawaterBuoyancy{Float64}:
├── gravitational_acceleration: 9.80665
└── equation of state: LinearEquationOfState(thermal_expansion=0.0002, haline_contraction=0.0008)

## Boundary conditions

We calculate the surface temperature flux associated with surface heating of 200 W m⁻², reference density ρₒ, and heat capacity cᴾ,

Qʰ = 200.0  # W m⁻², surface _heat_ flux
ρₒ = 1026.0 # kg m⁻³, average density at the surface of the world ocean
cᴾ = 3991.0 # J K⁻¹ kg⁻¹, typical heat capacity for seawater

Qᵀ = Qʰ / (ρₒ * cᴾ) # K m s⁻¹, surface _temperature_ flux
4.884283985946938e-5

Finally, we impose a temperature gradient dTdz both initially and at the bottom of the domain, culminating in the boundary conditions on temperature,

dTdz = 0.01 # K m⁻¹

T_bcs = FieldBoundaryConditions(top = FluxBoundaryCondition(Qᵀ),
bottom = GradientBoundaryCondition(dTdz))
Oceananigans.FieldBoundaryConditions, with boundary conditions
├── west: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── east: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── south: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── north: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── top: FluxBoundaryCondition: 4.88428e-5
└── immersed: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)

Note that a positive temperature flux at the surface of the ocean implies cooling. This is because a positive temperature flux implies that temperature is fluxed upwards, out of the ocean.

For the velocity field, we imagine a wind blowing over the ocean surface with an average velocity at 10 meters u₁₀, and use a drag coefficient cᴰ to estimate the kinematic stress (that is, stress divided by density) exerted by the wind on the ocean:

u₁₀ = 10    # m s⁻¹, average wind velocity 10 meters above the ocean
cᴰ = 2.5e-3 # dimensionless drag coefficient
ρₐ = 1.225  # kg m⁻³, average density of air at sea-level

Qᵘ = - ρₐ / ρₒ * cᴰ * u₁₀ * abs(u₁₀) # m² s⁻²
-0.0002984892787524367

The boundary conditions on u are thus

u_bcs = FieldBoundaryConditions(top = FluxBoundaryCondition(Qᵘ))
Oceananigans.FieldBoundaryConditions, with boundary conditions
├── west: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── east: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── south: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── north: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── bottom: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── top: FluxBoundaryCondition: -0.000298489
└── immersed: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)

For salinity, S, we impose an evaporative flux of the form

@inline Qˢ(x, y, t, S, evaporation_rate) = - evaporation_rate * S # [salinity unit] m s⁻¹

where S is salinity. We use an evporation rate of 1 millimeter per hour,

evaporation_rate = 1e-3 / hour # m s⁻¹
2.7777777777777776e-7

We build the Flux evaporation BoundaryCondition with the function Qˢ, indicating that Qˢ depends on salinity S and passing the parameter evaporation_rate,

evaporation_bc = FluxBoundaryCondition(Qˢ, field_dependencies=:S, parameters=evaporation_rate)
FluxBoundaryCondition: ContinuousBoundaryFunction Qˢ at (Nothing, Nothing, Nothing)

The full salinity boundary conditions are

S_bcs = FieldBoundaryConditions(top=evaporation_bc)
Oceananigans.FieldBoundaryConditions, with boundary conditions
├── west: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── east: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── south: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── north: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── bottom: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── top: FluxBoundaryCondition: ContinuousBoundaryFunction Qˢ at (Nothing, Nothing, Nothing)
└── immersed: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)

## Model instantiation

We fill in the final details of the model here: upwind-biased 5th-order advection for momentum and tracers, 3rd-order Runge-Kutta time-stepping, Coriolis forces, and the AnisotropicMinimumDissipation closure for large eddy simulation to model the effect of turbulent motions at scales smaller than the grid scale that we cannot explicitly resolve.

model = NonhydrostaticModel(; grid, buoyancy,
timestepper = :RungeKutta3,
tracers = (:T, :S),
coriolis = FPlane(f=1e-4),
closure = AnisotropicMinimumDissipation(),
boundary_conditions = (u=u_bcs, T=T_bcs, S=S_bcs))
NonhydrostaticModel{CPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── grid: 32×32×24 RectilinearGrid{Float64, Periodic, Periodic, Bounded} on CPU with 3×3×3 halo
├── timestepper: RungeKutta3TimeStepper
├── tracers: (T, S)
├── closure: AnisotropicMinimumDissipation{ExplicitTimeDiscretization, NamedTuple{(:T, :S), Tuple{Float64, Float64}}, Float64, Nothing}
├── buoyancy: SeawaterBuoyancy with g=9.80665 and LinearEquationOfState(thermal_expansion=0.0002, haline_contraction=0.0008) with -ĝ = ZDirection
└── coriolis: FPlane{Float64}(f=0.0001)

Notes:

• To use the Smagorinsky-Lilly turbulence closure (with a constant model coefficient) rather than AnisotropicMinimumDissipation, use closure = SmagorinskyLilly() in the model constructor.

• To change the architecture to GPU, replace CPU() with GPU() inside the grid constructor.

## Initial conditions

Our initial condition for temperature consists of a linear stratification superposed with random noise damped at the walls, while our initial condition for velocity consists only of random noise.

# Random noise damped at top and bottom
Ξ(z) = randn() * z / model.grid.Lz * (1 + z / model.grid.Lz) # noise

# Temperature initial condition: a stable density gradient with random noise superposed.
Tᵢ(x, y, z) = 20 + dTdz * z + dTdz * model.grid.Lz * 1e-6 * Ξ(z)

# Velocity initial condition: random noise scaled by the friction velocity.
uᵢ(x, y, z) = sqrt(abs(Qᵘ)) * 1e-3 * Ξ(z)

# set! the model fields using functions or constants:
set!(model, u=uᵢ, w=uᵢ, T=Tᵢ, S=35)

## Setting up a simulation

We set-up a simulation with an initial time-step of 10 seconds that stops at 40 minutes, with adaptive time-stepping and progress printing.

simulation = Simulation(model, Δt=10.0, stop_time=40minutes)
Simulation of NonhydrostaticModel{CPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── Next time step: 10 seconds
├── Elapsed wall time: 0 seconds
├── Wall time per iteration: NaN years
├── Stop time: 40 minutes
├── Stop iteration : Inf
├── Wall time limit: Inf
├── Callbacks: OrderedDict with 4 entries:
│   ├── stop_time_exceeded => Callback of stop_time_exceeded on IterationInterval(1)
│   ├── stop_iteration_exceeded => Callback of stop_iteration_exceeded on IterationInterval(1)
│   ├── wall_time_limit_exceeded => Callback of wall_time_limit_exceeded on IterationInterval(1)
│   └── nan_checker => Callback of NaNChecker for u on IterationInterval(100)
├── Output writers: OrderedDict with no entries
└── Diagnostics: OrderedDict with no entries

The TimeStepWizard helps ensure stable time-stepping with a Courant-Freidrichs-Lewy (CFL) number of 1.0.

wizard = TimeStepWizard(cfl=1.0, max_change=1.1, max_Δt=1minute)
simulation.callbacks[:wizard] = Callback(wizard, IterationInterval(10))
Callback of TimeStepWizard(cfl=1.0, max_Δt=60.0, min_Δt=0.0) on IterationInterval(10)

# Print a progress message
progress_message(sim) = @printf("Iteration: %04d, time: %s, Δt: %s, max(|w|) = %.1e ms⁻¹, wall time: %s\n",
iteration(sim), prettytime(sim), prettytime(sim.Δt),
maximum(abs, sim.model.velocities.w), prettytime(sim.run_wall_time))

simulation.callbacks[:progress] = Callback(progress_message, IterationInterval(20))
Callback of progress_message on IterationInterval(20)

We then set up the simulation:

## Output

We use the JLD2OutputWriter to save $x, z$ slices of the velocity fields, tracer fields, and eddy diffusivities. The prefix keyword argument to JLD2OutputWriter indicates that output will be saved in ocean_wind_mixing_and_convection.jld2.

# Create a NamedTuple with eddy viscosity
eddy_viscosity = (; νₑ = model.diffusivity_fields.νₑ)

filename = "ocean_wind_mixing_and_convection"

simulation.output_writers[:slices] =
JLD2OutputWriter(model, merge(model.velocities, model.tracers, eddy_viscosity),
filename = filename * ".jld2",
indices = (:, grid.Ny/2, :),
schedule = TimeInterval(1minute),
overwrite_existing = true)
JLD2OutputWriter scheduled on TimeInterval(1 minute):
├── filepath: ./ocean_wind_mixing_and_convection.jld2
├── 6 outputs: (u, v, w, T, S, νₑ)
├── array type: Array{Float32}
├── including: [:grid, :coriolis, :buoyancy, :closure]
└── max filesize: Inf YiB

run!(simulation)
[ Info: Initializing simulation...
Iteration: 0000, time: 0 seconds, Δt: 11 seconds, max(|w|) = 1.2e-05 ms⁻¹, wall time: 0 seconds
[ Info:     ... simulation initialization complete (8.850 seconds)
[ Info: Executing initial time step...
[ Info:     ... initial time step complete (1.716 minutes).
Iteration: 0020, time: 3.403 minutes, Δt: 13.310 seconds, max(|w|) = 9.4e-06 ms⁻¹, wall time: 1.885 minutes
Iteration: 0040, time: 7.488 minutes, Δt: 16.105 seconds, max(|w|) = 6.4e-06 ms⁻¹, wall time: 1.906 minutes
Iteration: 0060, time: 12 minutes, Δt: 10.350 seconds, max(|w|) = 1.1e-05 ms⁻¹, wall time: 1.925 minutes
Iteration: 0080, time: 15 minutes, Δt: 8.285 seconds, max(|w|) = 2.9e-05 ms⁻¹, wall time: 1.943 minutes
Iteration: 0100, time: 17.509 minutes, Δt: 7.099 seconds, max(|w|) = 1.5e-04 ms⁻¹, wall time: 1.964 minutes
Iteration: 0120, time: 19.669 minutes, Δt: 6.323 seconds, max(|w|) = 6.0e-04 ms⁻¹, wall time: 1.984 minutes
Iteration: 0140, time: 21.602 minutes, Δt: 5.747 seconds, max(|w|) = 2.5e-03 ms⁻¹, wall time: 2.008 minutes
Iteration: 0160, time: 23.369 minutes, Δt: 5.344 seconds, max(|w|) = 9.7e-03 ms⁻¹, wall time: 2.028 minutes
Iteration: 0180, time: 25 minutes, Δt: 5.234 seconds, max(|w|) = 3.3e-02 ms⁻¹, wall time: 2.045 minutes
Iteration: 0200, time: 26.733 minutes, Δt: 5.596 seconds, max(|w|) = 6.3e-02 ms⁻¹, wall time: 2.064 minutes
Iteration: 0220, time: 28.567 minutes, Δt: 6.235 seconds, max(|w|) = 8.0e-02 ms⁻¹, wall time: 2.081 minutes
Iteration: 0240, time: 30.572 minutes, Δt: 7.545 seconds, max(|w|) = 7.9e-02 ms⁻¹, wall time: 2.101 minutes
Iteration: 0260, time: 33 minutes, Δt: 8.822 seconds, max(|w|) = 7.4e-02 ms⁻¹, wall time: 2.121 minutes
Iteration: 0280, time: 35.933 minutes, Δt: 9.530 seconds, max(|w|) = 8.6e-02 ms⁻¹, wall time: 2.143 minutes
Iteration: 0300, time: 38.770 minutes, Δt: 8.724 seconds, max(|w|) = 6.4e-02 ms⁻¹, wall time: 2.170 minutes
[ Info: Simulation is stopping. Model time 40 minutes has hit or exceeded simulation stop time 40 minutes.

## Turbulence visualization

We animate the data saved in ocean_wind_mixing_and_convection.jld2. We prepare for animating the flow by loading the data into FieldTimeSeries and defining functions for computing colorbar limits.

filepath = filename * ".jld2"

time_series = (w = FieldTimeSeries(filepath, "w"),
T = FieldTimeSeries(filepath, "T"),
S = FieldTimeSeries(filepath, "S"),
νₑ = FieldTimeSeries(filepath, "νₑ"))

# Coordinate arrays
xw, yw, zw = nodes(time_series.w)
xT, yT, zT = nodes(time_series.T)
([1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0  …  45.0, 47.0, 49.0, 51.0, 53.0, 55.0, 57.0, 59.0, 61.0, 63.0], [1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0  …  45.0, 47.0, 49.0, 51.0, 53.0, 55.0, 57.0, 59.0, 61.0, 63.0], [-30.73214655800215, -28.344120885446316, -26.245517670529452, -24.406267643406927, -22.77515636448867, -21.301119444298777, -19.9410533865803, -18.66145654635607, -17.43754626408272, -16.251584220508228  …  -10.57103192002591, -9.45426630377412, -8.339528643431164, -7.226097424980429, -6.1135049573687645, -5.001449332843377, -3.889736372710594, -2.7782415919634484, -1.666885417004453, -0.5556171156552487])

We start the animation at $t = 10minutes$ since things are pretty boring till then:

times = time_series.w.times
intro = searchsortedfirst(times, 10minutes)
11

We are now ready to animate using Makie. We use Makie's Observable to animate the data. To dive into how Observables work we refer to Makie.jl's Documentation.

n = Observable(intro)

wₙ = @lift interior(time_series.w[$n], :, 1, :) Tₙ = @lift interior(time_series.T[$n],  :, 1, :)
Sₙ = @lift interior(time_series.S[$n], :, 1, :) νₑₙ = @lift interior(time_series.νₑ[$n], :, 1, :)

fig = Figure(resolution = (1000, 500))

axis_kwargs = (xlabel="x (m)",
ylabel="z (m)",
aspect = AxisAspect(grid.Lx/grid.Lz),
limits = ((0, grid.Lx), (-grid.Lz, 0)))

ax_w  = Axis(fig[2, 1]; title = "Vertical velocity", axis_kwargs...)
ax_T  = Axis(fig[2, 3]; title = "Temperature", axis_kwargs...)
ax_S  = Axis(fig[3, 1]; title = "Salinity", axis_kwargs...)
ax_νₑ = Axis(fig[3, 3]; title = "Eddy viscocity", axis_kwargs...)

title = @lift @sprintf("t = %s", prettytime(times[\$n]))

wlims = (-0.05, 0.05)
Tlims = (19.7, 19.99)
Slims = (35, 35.005)
νₑlims = (1e-6, 5e-3)

hm_w = heatmap!(ax_w, xw, zw, wₙ; colormap = :balance, colorrange = wlims)
Colorbar(fig[2, 2], hm_w; label = "m s⁻¹")

hm_T = heatmap!(ax_T, xT, zT, Tₙ; colormap = :thermal, colorrange = Tlims)
Colorbar(fig[2, 4], hm_T; label = "ᵒC")

hm_S = heatmap!(ax_S, xT, zT, Sₙ; colormap = :haline, colorrange = Slims)
Colorbar(fig[3, 2], hm_S; label = "g / kg")

hm_νₑ = heatmap!(ax_νₑ, xT, zT, νₑₙ; colormap = :thermal, colorrange = νₑlims)
Colorbar(fig[3, 4], hm_νₑ; label = "m s⁻²")

fig[1, 1:4] = Label(fig, title, textsize=24, tellwidth=false)
Label()

And now record a movie.

frames = intro:length(times)

@info "Making a motion picture of ocean wind mixing and convection..."

record(fig, filename * ".mp4", frames, framerate=8) do i
msg = string("Plotting frame ", i, " of ", frames[end])
print(msg * " \r")
n[] = i
end
[ Info: Making a motion picture of ocean wind mixing and convection...
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