# 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:

Nx = Ny = 32     # number of points in each of horizontal directions
Nz = 24          # number of points in the vertical direction

Lx = Ly = 64     # (m) domain horizontal extents
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(size = (Nx, Nx, Nz),
x = (0, Lx),
y = (0, Ly),
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(size=(1200, 800))
ax = Axis(fig[1, 1], ylabel = "Depth (m)", xlabel = "Vertical spacing (m)")

lines!(ax, zspacings(grid, Center()), znodes(grid, Center()))
scatter!(ax, zspacings(grid, Center()), znodes(grid, Center()))

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 cooling 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)
├── bottom: GradientBoundaryCondition: 0.01
├── 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
├── advection scheme: Upwind Biased reconstruction order 5
├── tracers: (T, S)
├── closure: AnisotropicMinimumDissipation{ExplicitTimeDiscretization, @NamedTuple{T::Float64, S::Float64}, Float64, Nothing}
├── buoyancy: SeawaterBuoyancy with g=9.80665 and LinearEquationOfState(thermal_expansion=0.0002, haline_contraction=0.0008) with ĝ = NegativeZDirection()
└── 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 days
├── 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)

Nice progress messaging is helpful:

# 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))

add_callback!(simulation, progress_message, 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{Float64}
├── including: [:grid, :coriolis, :buoyancy, :closure]
├── file_splitting: NoFileSplitting
└── file size: 50.3 KiB

run!(simulation)
[ Info: Initializing simulation...
Iteration: 0000, time: 0 seconds, Δt: 11 seconds, max(|w|) = 1.0e-05 ms⁻¹, wall time: 0 seconds
[ Info:     ... simulation initialization complete (16.116 seconds)
[ Info: Executing initial time step...
[ Info:     ... initial time step complete (7.508 seconds).
Iteration: 0020, time: 3.403 minutes, Δt: 13.310 seconds, max(|w|) = 8.5e-06 ms⁻¹, wall time: 25.037 seconds
Iteration: 0040, time: 7.488 minutes, Δt: 16.105 seconds, max(|w|) = 5.8e-06 ms⁻¹, wall time: 26.555 seconds
Iteration: 0060, time: 12 minutes, Δt: 9.989 seconds, max(|w|) = 1.1e-05 ms⁻¹, wall time: 28.140 seconds
Iteration: 0080, time: 14.884 minutes, Δt: 7.989 seconds, max(|w|) = 4.2e-05 ms⁻¹, wall time: 29.757 seconds
Iteration: 0100, time: 17.245 minutes, Δt: 6.845 seconds, max(|w|) = 2.0e-04 ms⁻¹, wall time: 31.200 seconds
Iteration: 0120, time: 19.320 minutes, Δt: 6.041 seconds, max(|w|) = 1.3e-03 ms⁻¹, wall time: 32.589 seconds
Iteration: 0140, time: 21.189 minutes, Δt: 5.407 seconds, max(|w|) = 6.8e-03 ms⁻¹, wall time: 33.983 seconds
Iteration: 0160, time: 22.924 minutes, Δt: 5.004 seconds, max(|w|) = 1.7e-02 ms⁻¹, wall time: 35.331 seconds
Iteration: 0180, time: 24.495 minutes, Δt: 5.171 seconds, max(|w|) = 3.5e-02 ms⁻¹, wall time: 36.808 seconds
Iteration: 0200, time: 26.178 minutes, Δt: 5.459 seconds, max(|w|) = 3.6e-02 ms⁻¹, wall time: 38.266 seconds
Iteration: 0220, time: 28 minutes, Δt: 6.244 seconds, max(|w|) = 4.6e-02 ms⁻¹, wall time: 39.555 seconds
Iteration: 0240, time: 30 minutes, Δt: 5.966 seconds, max(|w|) = 6.2e-02 ms⁻¹, wall time: 40.860 seconds
Iteration: 0260, time: 31.813 minutes, Δt: 5.956 seconds, max(|w|) = 5.4e-02 ms⁻¹, wall time: 42.172 seconds
Iteration: 0280, time: 33.700 minutes, Δt: 6.227 seconds, max(|w|) = 6.0e-02 ms⁻¹, wall time: 43.650 seconds
Iteration: 0300, time: 35.755 minutes, Δt: 6.534 seconds, max(|w|) = 7.5e-02 ms⁻¹, wall time: 45.080 seconds
Iteration: 0320, time: 37.753 minutes, Δt: 5.974 seconds, max(|w|) = 5.8e-02 ms⁻¹, wall time: 46.402 seconds
Iteration: 0340, time: 39.734 minutes, Δt: 6.434 seconds, max(|w|) = 6.3e-02 ms⁻¹, wall time: 47.698 seconds
[ Info: Simulation is stopping after running for 47.885 seconds.
[ Info: Simulation time 40 minutes equals or exceeds 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, 21.0, 23.0, 25.0, 27.0, 29.0, 31.0, 33.0, 35.0, 37.0, 39.0, 41.0, 43.0, 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, 21.0, 23.0, 25.0, 27.0, 29.0, 31.0, 33.0, 35.0, 37.0, 39.0, 41.0, 43.0, 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, -15.09116977761991, -13.947785764082138, -12.815662268579008, -11.690933327108011, -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 $t = 10$ minutes 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(size = (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, fontsize=24, tellwidth=false)

fig

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
n[] = i
end
[ Info: Making a motion picture of ocean wind mixing and convection...