Diurnal cycle of radiative convection

This example simulates a dramatic diurnal cycle of moist convection over a tropical land surface. During the day, the sun heats the ground, driving vigorous boundary-layer convection that lofts moisture into towering cumulus clouds. At night, the surface cools rapidly by longwave emission, stabilizing the boundary layer and shutting off convection.

The key ingredient is a time-varying surface temperature that follows the sun: peaking in the early afternoon and dipping well below the air temperature at night. This creates a strong diurnal contrast — afternoon thunderstorms that die at sunset and don't return until the next morning.

Interactive all-sky RRTMGP radiation computes spectrally-resolved shortwave and longwave fluxes. Saturation-adjustment microphysics diagnoses cloud liquid water that feeds back on the radiation. A stretched vertical grid resolves the cloud layer (100 m spacing below 3 km) while extending to 25 km for a realistic atmospheric column. A stratospheric sponge layer above 8 km prevents spurious temperature drift in the coarse upper cells.

using Breeze
using Oceananigans
using Oceananigans.Units
using Dates: DateTime
using Printf, Random, Statistics
using CairoMakie

using NCDatasets  # Required for RRTMGP lookup tables
using RRTMGP
using CUDA

Random.seed!(2025)
if CUDA.functional()
    CUDA.seed!(2025)
end

Grid

We use a 2D vertical slice (x-z) that is periodic in x and bounded in z. The vertical grid is stretched: fine 100 m cells resolve the cloud layer below 3 km, then a smooth transition to 1 km cells carries the column up to 25 km. This gives RRTMGP a realistic atmospheric column (including the stratosphere) while keeping the total cell count modest.

Nx = 128
Lx = 12800   # 12.8 km

arch = GPU()
Oceananigans.defaults.FloatType = Float32

z = PiecewiseStretchedDiscretization(
    z  = [0, 3000, 8000, 15000],
    Δz = [100,  100, 1000,  1000])

Nz = length(z) - 1

grid = RectilinearGrid(arch;
                       size = (Nx, Nz),
                       x = (0, Lx),
                       z,
                       halo = (5, 5),
                       topology = (Periodic, Flat, Bounded))

128×1×51 RectilinearGrid{Float32, Periodic, Flat, Bounded} on CUDAGPU with 5×0×5 halo
├── Periodic x ∈ [0.0, 12800.0) regularly spaced with Δx=100.0
├── Flat y                      
└── Bounded  z ∈ [0.0, 15000.0] variably spaced with min(Δz)=100.0, max(Δz)=1000.0

Reference state

p₀ = 101325  # Surface pressure [Pa]
θ₀ = 300     # Reference potential temperature [K]

constants = ThermodynamicConstants()

reference_state = ReferenceState(grid, constants;
                                 surface_pressure = p₀,
                                 potential_temperature = θ₀,
                                 vapor_mass_fraction = 0)

dynamics = AnelasticDynamics(reference_state)

AnelasticDynamics(p₀=101325.0, θ₀=300.0)
└── pressure_anomaly: not materialized

Background atmosphere

RRTMGP requires trace gas concentrations to compute spectral absorption and emission. We specify well-mixed greenhouse gas concentrations and a tropical ozone profile that transitions from low tropospheric values to a stratospheric peak near 25 km.

@inline function tropical_ozone(z)
    troposphere_O₃ = 30e-9 * (1 + 0.5 * z / 10_000)
    zˢᵗ = 25e3
    Hˢᵗ = 5e3
    stratosphere_O₃ = 8e-6 * exp(-((z - zˢᵗ) / Hˢᵗ)^2)
    χˢᵗ = 1 / (1 + exp(-(z - 15e3) / 2))
    return troposphere_O₃ * (1 - χˢᵗ) + stratosphere_O₃ * χˢᵗ
end

background_atmosphere = BackgroundAtmosphere(
    CO₂ = 348e-6,
    CH₄ = 1650e-9,
    N₂O = 306e-9,
    O₃ = tropical_ozone
)

BackgroundAtmosphere with 6 active gases:
  N₂ = 0.78084
  O₂ = 0.20946
  CO₂ = 348.0 ppm
  CH₄ = 1.65 ppm
  N₂O = 306.0 ppb
  O₃ = tropical_ozone (generic function with 1 method)

Diurnal surface temperature

Over land, the surface temperature swings dramatically with the sun. We model this as a sinusoidal cycle peaking 2 hours after solar noon: Tₛ(t) = T̄ₛ + ΔTₛ cos(2π(t - t_peak) / 24h), with T̄ₛ = 300 K (mean) and ΔTₛ = 10 K (amplitude). This gives 310 K in early afternoon and 290 K at night — a 20 K diurnal range, typical of tropical semi-arid land.

We start at midnight (t = 0), so the surface starts cold (290 K), warms through the morning, peaks at t = 14 h (2 pm local), and cools at night. A Field stores the surface temperature and a callback updates it each time step, keeping both the bulk fluxes and RRTMGP in sync.

T̄ₛ = 300   # Mean surface temperature [K]
ΔTₛ = 20   # Diurnal amplitude [K]

Tₛ = Field{Center, Center, Nothing}(grid)
set!(Tₛ, T̄ₛ - ΔTₛ)  # Start at midnight minimum

128×1×1 Field{Oceananigans.Grids.Center, Oceananigans.Grids.Center, Nothing} reduced over dims = (3,) on Oceananigans.Grids.RectilinearGrid on CUDAGPU
├── grid: 128×1×51 RectilinearGrid{Float32, Periodic, Flat, Bounded} on CUDAGPU with 5×0×5 halo
├── boundary conditions: FieldBoundaryConditions
│   └── west: Periodic, east: Periodic, south: Nothing, north: Nothing, bottom: Nothing, top: Nothing, immersed: Nothing
└── data: 138×1×1 OffsetArray(::CUDACore.CuArray{Float32, 3, CUDACore.DeviceMemory}, -4:133, 1:1, 1:1) with eltype Float32 with indices -4:133×1:1×1:1
    └── max=280.0, min=280.0, mean=280.0

Radiation with a diurnal cycle

We place the domain at 15°N latitude on the prime meridian and start at midnight on the spring equinox (March 20). The sun rises at t ≈ 6 h, reaches noon at t ≈ 12 h, and sets at t ≈ 18 h.

latitude = 15 # 15°N

radiation = RadiativeTransferModel(grid, AllSkyOptics(), constants;
                                   surface_temperature = Tₛ,
                                   surface_albedo = 0.20,
                                   surface_emissivity = 0.95,
                                   solar_constant = 1361,
                                   background_atmosphere,
                                   solar_position = ApparentSolarPosition(coordinate = (0, latitude),
                                                                          epoch      = DateTime(2020, 3, 20, 0, 0, 0)),
                                   schedule = TimeInterval(5minutes),
                                   liquid_effective_radius = ConstantRadiusParticles(10e-6),
                                   ice_effective_radius = ConstantRadiusParticles(30e-6))

RadiativeTransferModel
├── solar_constant: 1361.0 W m⁻²
├── solar_position: ApparentSolarPosition(coordinate=(0, 15), epoch=2020-03-20T00:00:00)
├── surface_temperature: 128×1×1 Field{Oceananigans.Grids.Center, Oceananigans.Grids.Center, Nothing} reduced over dims = (3,) on Oceananigans.Grids.RectilinearGrid on CUDAGPU
├── surface_emissivity: ConstantField(0.95)
├── direct_surface_albedo: ConstantField(0.2)
├── liquid_effective_radius: Breeze.AtmosphereModels.ConstantRadiusParticles{Float32}(1.0f-5)
├── ice_effective_radius: Breeze.AtmosphereModels.ConstantRadiusParticles{Float32}(3.0f-5)
└── diffuse_surface_albedo: ConstantField(0.2)

Surface fluxes

Bulk aerodynamic formulae provide surface sensible heat, moisture, and momentum fluxes driven by the time-varying surface temperature. During the day Tₛ > Tair drives strong upward fluxes; at night Tₛ < Tair can produce downward fluxes that cool the boundary layer.

Cᴰ = Cᵀ = 1e-3
Cᵛ = 1.2e-3
Uᵍ = 1  # Gustiness [m/s]

ρθ_flux = BulkSensibleHeatFlux(coefficient=Cᵀ, gustiness=Uᵍ, surface_temperature=Tₛ)
ρqᵗ_flux = BulkVaporFlux(coefficient=Cᵛ, gustiness=Uᵍ, surface_temperature=Tₛ)

ρθ_bcs = FieldBoundaryConditions(bottom=ρθ_flux)
ρqᵗ_bcs = FieldBoundaryConditions(bottom=ρqᵗ_flux)
ρu_bcs = FieldBoundaryConditions(bottom=Breeze.BulkDrag(coefficient=Cᴰ, gustiness=Uᵍ))

Oceananigans.FieldBoundaryConditions, with boundary conditions
├── west: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── east: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── south: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── north: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── bottom: FluxBoundaryCondition: BulkDragFunction(direction=Nothing, coefficient=0.001, gustiness=1)
├── top: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
└── immersed: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)

Microphysics

Warm-phase saturation adjustment instantly converts supersaturated moisture to cloud liquid water.

microphysics = SaturationAdjustment(equilibrium=WarmPhaseEquilibrium())

Breeze.Microphysics.SaturationAdjustment{Breeze.Thermodynamics.WarmPhaseEquilibrium, Float32}(0.001f0, Inf32, Breeze.Thermodynamics.WarmPhaseEquilibrium())

Stratospheric sponge

The domain extends to 25 km, but the initial stratosphere isn't in radiative equilibrium: ozone absorbs shortwave radiation and the coarse upper cells respond strongly. A Newtonian relaxation of temperature toward the initial profile above 8 km keeps the stratosphere anchored without affecting the tropospheric dynamics. We apply this as an energy forcing on ρe, which Breeze automatically converts to a ρθ tendency.

Tᵣ = reference_state.temperature
ρᵣ = reference_state.density
cᵖᵈ = constants.dry_air.heat_capacity / constants.dry_air.molar_mass  # J/(kg·K)
τ_sponge = 6hours

@inline function stratospheric_relaxation(i, j, k, grid, clock, model_fields, p)
    @inbounds T = model_fields.T[i, j, k]
    @inbounds Tᵣ = p.Tᵣ[i, j, k]
    @inbounds ρ = p.ρᵣ[i, j, k]
    z = znode(i, j, k, grid, Center(), Center(), Center())
    α = clamp((z - 8000) / 4000, 0, 1)
    ∂T∂t = -α * (T - Tᵣ) / p.τ
    return ρ * p.cᵖᵈ * ∂T∂t
end

sponge = Forcing(stratospheric_relaxation; discrete_form=true,
                 parameters=(; Tᵣ, ρᵣ, cᵖᵈ, τ=τ_sponge))

forcing = (; ρe=sponge)

(ρe = DiscreteForcing{@NamedTuple{Tᵣ::Oceananigans.Fields.Field{Nothing, Nothing, Oceananigans.Grids.Center, Nothing, Oceananigans.Grids.RectilinearGrid{Float32, Oceananigans.Grids.Periodic, Oceananigans.Grids.Flat, Oceananigans.Grids.Bounded, Oceananigans.Grids.StaticVerticalDiscretization{OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}, OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}, OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}, OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}}, Float32, Float32, OffsetArrays.OffsetVector{Float32, StepRangeLen{Float32, Float64, Float64, Int64}}, Nothing, Oceananigans.Architectures.GPU{CUDACore.CUDAKernels.CUDABackend}}, Tuple{Colon, Colon, Colon}, OffsetArrays.OffsetArray{Float32, 3, CUDACore.CuArray{Float32, 3, CUDACore.DeviceMemory}}, Float32, Oceananigans.BoundaryConditions.FieldBoundaryConditions{Nothing, Nothing, Nothing, Nothing, Oceananigans.BoundaryConditions.NoFluxBoundaryCondition, Oceananigans.BoundaryConditions.NoFluxBoundaryCondition, Nothing, @NamedTuple{south_and_north::Nothing, west_and_east::Nothing, bottom_and_top::KernelAbstractions.Kernel{CUDACore.CUDAKernels.CUDABackend, KernelAbstractions.NDIteration.StaticSize{(1, 1)}, Oceananigans.Utils.OffsetStaticSize{(1:1, 1:1)}, typeof(Oceananigans.BoundaryConditions.gpu__fill_bottom_and_top_halo!)}}, @NamedTuple{south_and_north::Tuple{Nothing, Nothing}, west_and_east::Tuple{Nothing, Nothing}, bottom_and_top::Tuple{Oceananigans.BoundaryConditions.NoFluxBoundaryCondition, Oceananigans.BoundaryConditions.NoFluxBoundaryCondition}}}, Nothing, Nothing}, ρᵣ::Oceananigans.Fields.Field{Nothing, Nothing, Oceananigans.Grids.Center, Nothing, Oceananigans.Grids.RectilinearGrid{Float32, Oceananigans.Grids.Periodic, Oceananigans.Grids.Flat, Oceananigans.Grids.Bounded, Oceananigans.Grids.StaticVerticalDiscretization{OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}, OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}, OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}, OffsetArrays.OffsetVector{Float32, CUDACore.CuArray{Float32, 1, CUDACore.DeviceMemory}}}, Float32, Float32, OffsetArrays.OffsetVector{Float32, StepRangeLen{Float32, Float64, Float64, Int64}}, Nothing, Oceananigans.Architectures.GPU{CUDACore.CUDAKernels.CUDABackend}}, Tuple{Colon, Colon, Colon}, OffsetArrays.OffsetArray{Float32, 3, CUDACore.CuArray{Float32, 3, CUDACore.DeviceMemory}}, Float32, Oceananigans.BoundaryConditions.FieldBoundaryConditions{Nothing, Nothing, Nothing, Nothing, Oceananigans.BoundaryConditions.BoundaryCondition{Oceananigans.BoundaryConditions.Value{Nothing}, Float32}, Oceananigans.BoundaryConditions.NoFluxBoundaryCondition, Nothing, @NamedTuple{south_and_north::Nothing, west_and_east::Nothing, bottom_and_top::KernelAbstractions.Kernel{CUDACore.CUDAKernels.CUDABackend, KernelAbstractions.NDIteration.StaticSize{(1, 1)}, Oceananigans.Utils.OffsetStaticSize{(1:1, 1:1)}, typeof(Oceananigans.BoundaryConditions.gpu__fill_bottom_and_top_halo!)}}, @NamedTuple{south_and_north::Tuple{Nothing, Nothing}, west_and_east::Tuple{Nothing, Nothing}, bottom_and_top::Tuple{Oceananigans.BoundaryConditions.BoundaryCondition{Oceananigans.BoundaryConditions.Value{Nothing}, Float32}, Oceananigans.BoundaryConditions.NoFluxBoundaryCondition}}}, Nothing, Nothing}, cᵖᵈ::Float32, τ::Float64}}
├── func: stratospheric_relaxation (generic function with 1 method)
└── parameters: (Tᵣ = 1×1×51 Field{Nothing, Nothing, Oceananigans.Grids.Center} reduced over dims = (1, 2) on Oceananigans.Grids.RectilinearGrid on CUDAGPU
├── grid: 128×1×51 RectilinearGrid{Float32, Periodic, Flat, Bounded} on CUDAGPU with 5×0×5 halo
├── boundary conditions: FieldBoundaryConditions
│   └── west: Nothing, east: Nothing, south: Nothing, north: Nothing, bottom: ZeroFlux, top: ZeroFlux, immersed: Nothing
└── data: 1×1×61 OffsetArray(::CUDACore.CuArray{Float32, 3, CUDACore.DeviceMemory}, 1:1, 1:1, -4:56) with eltype Float32 with indices 1:1×1:1×-4:56
    └── max=300.642, min=159.593, mean=264.678, ρᵣ = 1×1×51 Field{Nothing, Nothing, Oceananigans.Grids.Center} reduced over dims = (1, 2) on Oceananigans.Grids.RectilinearGrid on CUDAGPU
├── grid: 128×1×51 RectilinearGrid{Float32, Periodic, Flat, Bounded} on CUDAGPU with 5×0×5 halo
├── boundary conditions: FieldBoundaryConditions
│   └── west: Nothing, east: Nothing, south: Nothing, north: Nothing, bottom: Value, top: ZeroFlux, immersed: Nothing
└── data: 1×1×61 OffsetArray(::CUDACore.CuArray{Float32, 3, CUDACore.DeviceMemory}, 1:1, 1:1, -4:56) with eltype Float32 with indices 1:1×1:1×-4:56
    └── max=1.16766, min=0.239471, mean=0.876104, cᵖᵈ = 34691.06f0, τ = 21600.0),)

Model assembly

coriolis = FPlane(; latitude)
boundary_conditions = (ρθ=ρθ_bcs, ρqᵗ=ρqᵗ_bcs, ρu=ρu_bcs)

weno_order = 5
momentum_advection = WENO(order=weno_order)

scalar_advection = (ρθ  = WENO(order=weno_order),
                    ρqᵗ = WENO(order=weno_order, bounds=(0, 1)))

model = AtmosphereModel(grid; dynamics, microphysics, radiation, forcing,
                        momentum_advection, scalar_advection,
                        boundary_conditions, coriolis)

AtmosphereModel{GPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── grid: 128×1×51 RectilinearGrid{Float32, Periodic, Flat, Bounded} on CUDAGPU with 5×0×5 halo
├── dynamics: AnelasticDynamics(p₀=101325.0, θ₀=300.0)
├── formulation: LiquidIcePotentialTemperatureFormulation
├── thermodynamic_constants: ThermodynamicConstants{Float32}
├── timestepper: SSPRungeKutta3
├── advection scheme: 
│   ├── momentum: WENO{3, Float32, Oceananigans.Utils.BackendOptimizedDivision}(order=5)
│   ├── ρθ: WENO{3, Float32, Oceananigans.Utils.BackendOptimizedDivision}(order=5)
│   └── ρqᵉ: Centered(order=2)
├── forcing: ρe=>DiscreteForcing
├── tracers: ()
├── coriolis: FPlane{Float32}(f=3.77468e-5)
└── microphysics: SaturationAdjustment

Initial conditions

The sounding has a dry-adiabatic sub-cloud layer (0–1 km) capped by a conditionally unstable troposphere (5 K/km lapse rate) that transitions to an isothermal stratosphere at 210 K. Moisture is 20 g/kg at the surface with a 2.5 km scale height, typical of the tropical maritime boundary layer.

function Tᵇᵍ(z)
    T₀ = 300
    Tˢᵗ = 210 # stratosphere temperature
    T = T₀ - 1e-3 * max(z, 1000) - 5e-3 * max(0, z - 1000)
    return max(T, Tˢᵗ)
end

uᵢ(x, z) = -5 * max(1 - z / 3000, 0)

uᵢ (generic function with 1 method)

Random perturbations in the lowest 1 km trigger convection.

δT = 2
δℋ = 1e-2
zδ = 1000

ϵ() = rand() - 0.5
Tᵢ(x, z) = Tᵇᵍ(z) + δT * ϵ() * (z < zδ)
ℋᵢ(x, z) = (0.5 + δℋ * ϵ()) * (z < zδ)

ℋᵢ (generic function with 1 method)

After setting initial conditions, we recompute the reference state from the horizontally-averaged model state. This is important for Float32 accuracy in tall domains: the default dry-adiabat reference state diverges from the actual stratospheric profile, causing large density errors that overwhelm Float32 precision. set_to_mean! adjusts ρᵣ to match the current state while rescaling density-weighted prognostic fields to preserve specific quantities.

set!(model; T=Tᵢ, ℋ=ℋᵢ, u=uᵢ)

reference_state = model.dynamics.reference_state
set_to_mean!(reference_state, model, rescale_densities=true)

T = model.temperature
qᵗ = specific_prognostic_moisture(model)
u, w = model.velocities.u, model.velocities.w
qˡ = model.microphysical_fields.qˡ

@info "Diurnal Radiative Convection (2D)"
@info "Grid: $(Nx) × $(Nz) (stretched), domain: $(Lx/1000) km × 25 km"
@info "Initial T range: $(minimum(T)) – $(maximum(T)) K"
@info "Initial qᵗ range: $(minimum(qᵗ)*1000) – $(maximum(qᵗ)*1000) g/kg"

[ Info: Diurnal Radiative Convection (2D)
[ Info: Grid: 128 × 51 (stretched), domain: 12.8 km × 25 km
[ Info: Initial T range: 238.17168 – 300.13 K
[ Info: Initial qᵗ range: 0.0 – 12.097069 g/kg

Simulation

We run for two full diurnal cycles (48 hours) starting at midnight, so the on/off pattern of convection repeats convincingly.

simulation = Simulation(model; Δt=1, stop_time=3days)
conjure_time_step_wizard!(simulation, cfl=0.7)
Oceananigans.Diagnostics.erroring_NaNChecker!(simulation)

Surface temperature callback

At each time step we update the surface temperature field following a cosine curve that peaks 2 hours after solar noon (t = 14 h local). The period is 24 hours and the simulation starts at midnight.

function update_surface_temperature!(sim)
    t = time(sim)
    t_peak = 14hour  # peak at 14:00 local (2 pm)
    Tₛ_now = T̄ₛ + ΔTₛ * cos(2π * (t - t_peak) / day)
    set!(Tₛ, Tₛ_now)
    return nothing
end

add_callback!(simulation, update_surface_temperature!, TimeInterval(1minute))

wall_clock = Ref(time_ns())

function progress(sim)
    elapsed = 1e-9 * (time_ns() - wall_clock[])

    wmax = maximum(abs, w)
    Tmin, Tmax = extrema(T)
    qˡmax = maximum(qˡ)
    Tₛ_now = T̄ₛ + ΔTₛ * cos(2π * (time(sim) - 14hours) / 24hours)

    OLR = mean(view(radiation.upwelling_longwave_flux, :, 1, Nz+1))

    msg = @sprintf("Iter: %5d, t: %8s, Δt: %5.1fs, wall: %8s",
                   iteration(sim), prettytime(sim), sim.Δt, prettytime(elapsed))
    msg *= @sprintf(", max|w|: %5.2f m/s, T: [%5.1f, %5.1f] K, max(qˡ): %.2e",
                   wmax, Tmin, Tmax, qˡmax)
    msg *= @sprintf(", Tₛ: %.1f K, OLR: %.1f W/m²", Tₛ_now, OLR)
    @info msg

    wall_clock[] = time_ns()
    return nothing
end

add_callback!(simulation, progress, IterationInterval(1000))

Output

Horizontally-averaged profiles are saved every hour (time-averaged) and 2D slices every 10 minutes for animation.

qᵛ = model.microphysical_fields.qᵛ
Fᴿ = radiation.flux_divergence

outputs = (; u, w, T, qˡ, qᵛ, Fᴿ)
avg_outputs = NamedTuple(name => Average(outputs[name], dims=1) for name in keys(outputs))

filename = "radiative_convection"
averages_filename = filename * "_averages.jld2"
slices_filename = filename * "_slices.jld2"

simulation.output_writers[:averages] = JLD2Writer(model, avg_outputs;
                                                  filename = averages_filename,
                                                  schedule = AveragedTimeInterval(1hour),
                                                  overwrite_existing = true)

slice_outputs = (; w, qᵛ, T)
simulation.output_writers[:slices] = JLD2Writer(model, slice_outputs;
                                                filename = slices_filename,
                                                schedule = TimeInterval(10minutes),
                                                overwrite_existing = true)

@info "Starting simulation..."
run!(simulation)
@info "Simulation completed!"

[ Info: Starting simulation...
[ Info: Initializing simulation...
[ Info: Iter:     0, t: 0 seconds, Δt:   1.1s, wall: 48.632 seconds, max|w|:  0.00 m/s, T: [238.2, 300.1] K, max(qˡ): 0.00e+00, Tₛ: 282.7 K, OLR: 373.8 W/m²
[ Info:     ... simulation initialization complete (24.527 seconds)
[ Info: Executing initial time step...
[ Info:     ... initial time step complete (6.955 seconds).
[ Info: Iter:  1000, t: 1.517 hours, Δt:   5.1s, wall: 44.620 seconds, max|w|:  7.37 m/s, T: [217.8, 302.3] K, max(qˡ): 1.69e-03, Tₛ: 280.2 K, OLR: 330.5 W/m²
[ Info: Iter:  2000, t: 2.759 hours, Δt:   5.6s, wall: 29.785 seconds, max|w|: 38.39 m/s, T: [208.9, 302.7] K, max(qˡ): 1.05e-02, Tₛ: 280.4 K, OLR: 191.3 W/m²
[ Info: Iter:  3000, t: 4.055 hours, Δt:   4.0s, wall: 29.739 seconds, max|w|: 11.58 m/s, T: [216.0, 304.5] K, max(qˡ): 7.34e-03, Tₛ: 282.8 K, OLR: 122.8 W/m²
[ Info: Iter:  4000, t: 5.064 hours, Δt:   4.5s, wall: 27.808 seconds, max|w|:  9.49 m/s, T: [216.5, 305.7] K, max(qˡ): 7.61e-03, Tₛ: 286.1 K, OLR: 121.5 W/m²
[ Info: Iter:  5000, t: 6.286 hours, Δt:   3.0s, wall: 29.226 seconds, max|w|: 14.72 m/s, T: [217.0, 304.9] K, max(qˡ): 7.73e-03, Tₛ: 291.3 K, OLR: 121.6 W/m²
[ Info: Iter:  6000, t: 7.282 hours, Δt:   3.6s, wall: 27.392 seconds, max|w|: 12.32 m/s, T: [217.5, 304.5] K, max(qˡ): 8.31e-03, Tₛ: 296.3 K, OLR: 123.1 W/m²
[ Info: Iter:  7000, t: 8.379 hours, Δt:   4.2s, wall: 28.277 seconds, max|w|:  9.57 m/s, T: [217.9, 304.7] K, max(qˡ): 8.00e-03, Tₛ: 302.0 K, OLR: 124.9 W/m²
[ Info: Iter:  8000, t: 9.438 hours, Δt:   4.4s, wall: 27.765 seconds, max|w|: 11.99 m/s, T: [218.5, 308.6] K, max(qˡ): 8.73e-03, Tₛ: 307.4 K, OLR: 126.2 W/m²
[ Info: Iter:  9000, t: 10.400 hours, Δt:   2.8s, wall: 27.057 seconds, max|w|: 14.20 m/s, T: [218.4, 305.1] K, max(qˡ): 8.97e-03, Tₛ: 311.8 K, OLR: 127.0 W/m²
[ Info: Iter: 10000, t: 11.459 hours, Δt:   3.9s, wall: 28.998 seconds, max|w|: 10.15 m/s, T: [218.6, 305.7] K, max(qˡ): 8.20e-03, Tₛ: 315.7 K, OLR: 127.5 W/m²
[ Info: Iter: 11000, t: 12.538 hours, Δt:   3.6s, wall: 28.144 seconds, max|w|: 15.68 m/s, T: [219.4, 306.0] K, max(qˡ): 8.17e-03, Tₛ: 318.6 K, OLR: 127.7 W/m²
[ Info: Iter: 12000, t: 13.556 hours, Δt:   3.1s, wall: 27.540 seconds, max|w|: 19.15 m/s, T: [219.5, 306.9] K, max(qˡ): 8.44e-03, Tₛ: 319.9 K, OLR: 127.4 W/m²
[ Info: Iter: 13000, t: 14.489 hours, Δt:   3.9s, wall: 26.962 seconds, max|w|: 13.08 m/s, T: [219.2, 305.1] K, max(qˡ): 8.70e-03, Tₛ: 319.8 K, OLR: 127.0 W/m²
[ Info: Iter: 14000, t: 15.454 hours, Δt:   3.2s, wall: 27.563 seconds, max|w|:  9.77 m/s, T: [218.9, 306.4] K, max(qˡ): 9.36e-03, Tₛ: 318.6 K, OLR: 126.1 W/m²
[ Info: Iter: 15000, t: 16.511 hours, Δt:   4.5s, wall: 28.174 seconds, max|w|:  8.00 m/s, T: [218.1, 305.1] K, max(qˡ): 8.76e-03, Tₛ: 315.8 K, OLR: 124.8 W/m²
[ Info: Iter: 16000, t: 17.552 hours, Δt:   3.5s, wall: 27.630 seconds, max|w|: 13.37 m/s, T: [217.3, 305.8] K, max(qˡ): 8.78e-03, Tₛ: 312.0 K, OLR: 123.3 W/m²
[ Info: Iter: 17000, t: 18.527 hours, Δt:   4.6s, wall: 27.569 seconds, max|w|:  8.86 m/s, T: [216.3, 308.1] K, max(qˡ): 8.36e-03, Tₛ: 307.5 K, OLR: 121.7 W/m²
[ Info: Iter: 18000, t: 19.586 hours, Δt:   4.2s, wall: 28.179 seconds, max|w|: 11.19 m/s, T: [216.6, 305.8] K, max(qˡ): 8.30e-03, Tₛ: 302.2 K, OLR: 121.7 W/m²
[ Info: Iter: 19000, t: 20.625 hours, Δt:   4.2s, wall: 27.570 seconds, max|w|: 10.47 m/s, T: [216.5, 306.4] K, max(qˡ): 7.18e-03, Tₛ: 296.7 K, OLR: 121.7 W/m²
[ Info: Iter: 20000, t: 21.673 hours, Δt:   4.4s, wall: 28.212 seconds, max|w|:  9.93 m/s, T: [216.8, 305.9] K, max(qˡ): 7.66e-03, Tₛ: 291.5 K, OLR: 121.7 W/m²
[ Info: Iter: 21000, t: 22.591 hours, Δt:   4.5s, wall: 26.747 seconds, max|w|:  8.66 m/s, T: [216.7, 304.5] K, max(qˡ): 7.24e-03, Tₛ: 287.4 K, OLR: 121.7 W/m²
[ Info: Iter: 22000, t: 23.660 hours, Δt:   3.8s, wall: 26.983 seconds, max|w|: 12.70 m/s, T: [216.4, 306.5] K, max(qˡ): 7.59e-03, Tₛ: 283.6 K, OLR: 121.7 W/m²
[ Info: Iter: 23000, t: 1.029 days, Δt:   3.8s, wall: 27.640 seconds, max|w|: 10.70 m/s, T: [216.7, 304.5] K, max(qˡ): 8.09e-03, Tₛ: 281.1 K, OLR: 121.6 W/m²
[ Info: Iter: 24000, t: 1.072 days, Δt:   3.8s, wall: 27.372 seconds, max|w|: 11.21 m/s, T: [216.5, 305.6] K, max(qˡ): 7.48e-03, Tₛ: 280.1 K, OLR: 121.6 W/m²
[ Info: Iter: 25000, t: 1.111 days, Δt:   4.0s, wall: 28.077 seconds, max|w|: 11.76 m/s, T: [216.6, 304.6] K, max(qˡ): 8.20e-03, Tₛ: 280.3 K, OLR: 121.6 W/m²
[ Info: Iter: 26000, t: 1.153 days, Δt:   4.0s, wall: 27.405 seconds, max|w|:  8.01 m/s, T: [216.7, 309.8] K, max(qˡ): 7.90e-03, Tₛ: 281.9 K, OLR: 121.7 W/m²
[ Info: Iter: 27000, t: 1.196 days, Δt:   4.0s, wall: 27.342 seconds, max|w|: 11.34 m/s, T: [217.1, 304.8] K, max(qˡ): 7.30e-03, Tₛ: 284.8 K, OLR: 121.6 W/m²
[ Info: Iter: 28000, t: 1.239 days, Δt:   3.0s, wall: 27.324 seconds, max|w|: 18.40 m/s, T: [216.8, 304.2] K, max(qˡ): 7.60e-03, Tₛ: 288.8 K, OLR: 121.6 W/m²
[ Info: Iter: 29000, t: 1.283 days, Δt:   5.1s, wall: 27.871 seconds, max|w|:  9.02 m/s, T: [217.1, 304.3] K, max(qˡ): 7.45e-03, Tₛ: 293.7 K, OLR: 122.3 W/m²
[ Info: Iter: 30000, t: 1.328 days, Δt:   4.9s, wall: 27.938 seconds, max|w|:  9.16 m/s, T: [218.2, 306.1] K, max(qˡ): 7.52e-03, Tₛ: 299.4 K, OLR: 124.1 W/m²
[ Info: Iter: 31000, t: 1.366 days, Δt:   2.1s, wall: 26.713 seconds, max|w|: 18.96 m/s, T: [218.6, 308.5] K, max(qˡ): 7.41e-03, Tₛ: 304.1 K, OLR: 125.5 W/m²
[ Info: Iter: 32000, t: 1.404 days, Δt:   3.8s, wall: 26.618 seconds, max|w|: 11.37 m/s, T: [218.6, 304.4] K, max(qˡ): 7.09e-03, Tₛ: 308.6 K, OLR: 126.4 W/m²
[ Info: Iter: 33000, t: 1.450 days, Δt:   4.3s, wall: 24.360 seconds, max|w|: 10.85 m/s, T: [219.4, 305.3] K, max(qˡ): 7.13e-03, Tₛ: 313.4 K, OLR: 127.2 W/m²
[ Info: Iter: 34000, t: 1.491 days, Δt:   3.2s, wall: 19.126 seconds, max|w|: 12.23 m/s, T: [219.3, 305.7] K, max(qˡ): 7.25e-03, Tₛ: 316.7 K, OLR: 127.6 W/m²
[ Info: Iter: 35000, t: 1.530 days, Δt:   3.4s, wall: 18.102 seconds, max|w|: 13.25 m/s, T: [219.6, 306.3] K, max(qˡ): 7.32e-03, Tₛ: 318.9 K, OLR: 127.6 W/m²
[ Info: Iter: 36000, t: 1.571 days, Δt:   3.1s, wall: 19.119 seconds, max|w|: 15.20 m/s, T: [219.4, 304.9] K, max(qˡ): 7.30e-03, Tₛ: 319.9 K, OLR: 127.4 W/m²
[ Info: Iter: 37000, t: 1.612 days, Δt:   3.4s, wall: 16.348 seconds, max|w|: 10.69 m/s, T: [219.0, 304.5] K, max(qˡ): 6.93e-03, Tₛ: 319.7 K, OLR: 126.7 W/m²
[ Info: Iter: 38000, t: 1.657 days, Δt:   3.1s, wall: 11.439 seconds, max|w|: 13.49 m/s, T: [218.8, 304.3] K, max(qˡ): 7.14e-03, Tₛ: 317.9 K, OLR: 125.7 W/m²
[ Info: Iter: 39000, t: 1.698 days, Δt:   4.2s, wall: 11.858 seconds, max|w|:  8.55 m/s, T: [218.3, 305.2] K, max(qˡ): 7.16e-03, Tₛ: 315.0 K, OLR: 124.4 W/m²
[ Info: Iter: 40000, t: 1.737 days, Δt:   3.3s, wall: 10.857 seconds, max|w|: 12.86 m/s, T: [217.2, 304.4] K, max(qˡ): 7.29e-03, Tₛ: 311.4 K, OLR: 123.0 W/m²
[ Info: Iter: 41000, t: 1.777 days, Δt:   3.3s, wall: 10.859 seconds, max|w|: 12.09 m/s, T: [217.0, 305.0] K, max(qˡ): 7.10e-03, Tₛ: 306.9 K, OLR: 121.7 W/m²
[ Info: Iter: 42000, t: 1.820 days, Δt:   3.7s, wall: 11.463 seconds, max|w|: 11.11 m/s, T: [217.1, 306.3] K, max(qˡ): 7.38e-03, Tₛ: 301.7 K, OLR: 121.6 W/m²
[ Info: Iter: 43000, t: 1.865 days, Δt:   4.1s, wall: 11.428 seconds, max|w|:  8.12 m/s, T: [217.0, 305.6] K, max(qˡ): 7.16e-03, Tₛ: 296.1 K, OLR: 121.6 W/m²
[ Info: Iter: 44000, t: 1.904 days, Δt:   3.8s, wall: 10.932 seconds, max|w|: 11.68 m/s, T: [216.9, 306.8] K, max(qˡ): 7.04e-03, Tₛ: 291.4 K, OLR: 121.7 W/m²
[ Info: Iter: 45000, t: 1.941 days, Δt:   3.3s, wall: 10.616 seconds, max|w|: 11.19 m/s, T: [216.7, 304.8] K, max(qˡ): 7.05e-03, Tₛ: 287.5 K, OLR: 121.6 W/m²
[ Info: Iter: 46000, t: 1.987 days, Δt:   3.9s, wall: 11.459 seconds, max|w|:  9.73 m/s, T: [217.0, 304.7] K, max(qˡ): 6.39e-03, Tₛ: 283.6 K, OLR: 121.5 W/m²
[ Info: Iter: 47000, t: 2.033 days, Δt:   4.2s, wall: 11.548 seconds, max|w|: 10.23 m/s, T: [217.0, 304.7] K, max(qˡ): 6.90e-03, Tₛ: 281.0 K, OLR: 121.6 W/m²
[ Info: Iter: 48000, t: 2.077 days, Δt:   3.4s, wall: 11.292 seconds, max|w|:  8.90 m/s, T: [217.0, 304.9] K, max(qˡ): 6.49e-03, Tₛ: 280.0 K, OLR: 121.6 W/m²
[ Info: Iter: 49000, t: 2.118 days, Δt:   3.6s, wall: 10.007 seconds, max|w|: 10.11 m/s, T: [216.8, 305.3] K, max(qˡ): 6.76e-03, Tₛ: 280.5 K, OLR: 121.7 W/m²
[ Info: Iter: 50000, t: 2.156 days, Δt:   3.5s, wall: 10.905 seconds, max|w|: 10.09 m/s, T: [216.9, 304.7] K, max(qˡ): 6.63e-03, Tₛ: 282.1 K, OLR: 121.7 W/m²
[ Info: Iter: 51000, t: 2.195 days, Δt:   3.4s, wall: 11.178 seconds, max|w|:  9.94 m/s, T: [217.1, 304.1] K, max(qˡ): 6.80e-03, Tₛ: 284.7 K, OLR: 121.7 W/m²
[ Info: Iter: 52000, t: 2.243 days, Δt:   5.0s, wall: 12.595 seconds, max|w|:  7.74 m/s, T: [216.6, 304.1] K, max(qˡ): 6.49e-03, Tₛ: 289.3 K, OLR: 121.5 W/m²
[ Info: Iter: 53000, t: 2.285 days, Δt:   3.6s, wall: 10.825 seconds, max|w|: 14.42 m/s, T: [217.4, 306.5] K, max(qˡ): 6.48e-03, Tₛ: 294.0 K, OLR: 122.4 W/m²
[ Info: Iter: 54000, t: 2.327 days, Δt:   5.0s, wall: 11.519 seconds, max|w|: 10.70 m/s, T: [218.1, 304.1] K, max(qˡ): 6.17e-03, Tₛ: 299.2 K, OLR: 124.2 W/m²
[ Info: Iter: 55000, t: 2.366 days, Δt:   3.4s, wall: 10.872 seconds, max|w|: 12.43 m/s, T: [218.5, 306.7] K, max(qˡ): 6.18e-03, Tₛ: 304.0 K, OLR: 125.5 W/m²
[ Info: Iter: 56000, t: 2.409 days, Δt:   3.2s, wall: 11.205 seconds, max|w|: 12.30 m/s, T: [218.8, 304.0] K, max(qˡ): 5.99e-03, Tₛ: 309.2 K, OLR: 126.5 W/m²
[ Info: Iter: 57000, t: 2.452 days, Δt:   3.6s, wall: 11.466 seconds, max|w|:  8.89 m/s, T: [219.3, 306.3] K, max(qˡ): 6.09e-03, Tₛ: 313.5 K, OLR: 127.3 W/m²
[ Info: Iter: 58000, t: 2.488 days, Δt:   3.4s, wall: 10.579 seconds, max|w|:  8.95 m/s, T: [219.4, 307.4] K, max(qˡ): 6.11e-03, Tₛ: 316.5 K, OLR: 127.6 W/m²
[ Info: Iter: 59000, t: 2.527 days, Δt:   4.0s, wall: 10.825 seconds, max|w|:  8.70 m/s, T: [219.4, 306.0] K, max(qˡ): 6.04e-03, Tₛ: 318.7 K, OLR: 127.6 W/m²
[ Info: Iter: 60000, t: 2.568 days, Δt:   4.0s, wall: 11.105 seconds, max|w|: 10.46 m/s, T: [219.2, 306.9] K, max(qˡ): 6.07e-03, Tₛ: 319.9 K, OLR: 127.4 W/m²
[ Info: Iter: 61000, t: 2.610 days, Δt:   3.1s, wall: 11.154 seconds, max|w|: 14.11 m/s, T: [219.2, 306.6] K, max(qˡ): 5.95e-03, Tₛ: 319.7 K, OLR: 126.9 W/m²
[ Info: Iter: 62000, t: 2.648 days, Δt:   3.3s, wall: 9.896 seconds, max|w|: 15.42 m/s, T: [218.7, 306.2] K, max(qˡ): 5.89e-03, Tₛ: 318.3 K, OLR: 126.0 W/m²
[ Info: Iter: 63000, t: 2.689 days, Δt:   3.6s, wall: 11.157 seconds, max|w|: 10.51 m/s, T: [218.2, 304.6] K, max(qˡ): 5.90e-03, Tₛ: 315.7 K, OLR: 124.8 W/m²
[ Info: Iter: 64000, t: 2.732 days, Δt:   3.7s, wall: 11.182 seconds, max|w|: 10.92 m/s, T: [217.8, 305.2] K, max(qˡ): 6.01e-03, Tₛ: 311.9 K, OLR: 123.2 W/m²
[ Info: Iter: 65000, t: 2.773 days, Δt:   3.8s, wall: 11.850 seconds, max|w|: 12.70 m/s, T: [216.9, 305.6] K, max(qˡ): 6.01e-03, Tₛ: 307.4 K, OLR: 121.6 W/m²
[ Info: Iter: 66000, t: 2.816 days, Δt:   3.7s, wall: 18.631 seconds, max|w|: 12.25 m/s, T: [217.0, 305.5] K, max(qˡ): 5.98e-03, Tₛ: 302.1 K, OLR: 121.5 W/m²
[ Info: Iter: 67000, t: 2.855 days, Δt:   4.2s, wall: 11.430 seconds, max|w|:  9.82 m/s, T: [217.1, 304.4] K, max(qˡ): 5.85e-03, Tₛ: 297.3 K, OLR: 121.6 W/m²
[ Info: Iter: 68000, t: 2.896 days, Δt:   4.1s, wall: 11.339 seconds, max|w|: 12.69 m/s, T: [216.9, 307.1] K, max(qˡ): 5.83e-03, Tₛ: 292.3 K, OLR: 121.6 W/m²
[ Info: Iter: 69000, t: 2.938 days, Δt:   3.0s, wall: 11.425 seconds, max|w|: 11.94 m/s, T: [216.9, 303.6] K, max(qˡ): 5.78e-03, Tₛ: 287.8 K, OLR: 121.6 W/m²
[ Info: Iter: 70000, t: 2.978 days, Δt:   3.6s, wall: 11.076 seconds, max|w|:  8.84 m/s, T: [217.0, 305.8] K, max(qˡ): 5.74e-03, Tₛ: 284.3 K, OLR: 121.7 W/m²
[ Info: Simulation is stopping after running for 23.401 minutes.
[ Info: Simulation time 3 days equals or exceeds stop time 3 days.
[ Info: Simulation completed!

Hovmöller diagrams of mean profile evolution

Time–height Hovmöller diagrams show how the horizontally-averaged temperature, cloud liquid water, and radiative flux divergence evolve over two diurnal cycles. The vertical axis is zoomed to the lowest 6 km where the convective dynamics live.

Tts  = FieldTimeSeries(averages_filename, "T")
qᵛts = FieldTimeSeries(averages_filename, "qᵛ")
Fᴿts = FieldTimeSeries(averages_filename, "Fᴿ")

times = Tts.times
Nt = length(times)

z = Oceananigans.Grids.znodes(Tts.grid, Center())
t_hours = times ./ 3600

0.0:1.0:72.0

Build time–height matrices from the averaged profiles.

T_zt  = permutedims(interior(Tts,  1, 1, :, :))
T_zt .-= mean(T_zt, dims=1)
qᵛ_zt = permutedims(interior(qᵛts, 1, 1, :, :))
Fᴿ_zt = permutedims(interior(Fᴿts, 1, 1, :, :))

fig = Figure(size=(900, 800), fontsize=14)

zmax = 12000

axT  = Axis(fig[1, 1]; ylabel="z (m)", title="Temperature anomaly (K)",
            limits=((t_hours[1], t_hours[end]), (0, zmax)))
axqᵛ = Axis(fig[2, 1]; ylabel="z (m)", title="Specific humidity (kg/kg)",
            limits=((t_hours[1], t_hours[end]), (0, zmax)))
axF  = Axis(fig[3, 1]; ylabel="z (m)", xlabel="time (hours)", title="Radiative flux divergence (W/m³)",
            limits=((t_hours[1], t_hours[end]), (0, zmax)))

hidexdecorations!(axT;  grid=false)
hidexdecorations!(axqᵛ; grid=false)

hmT  = heatmap!(axT,  t_hours, z, T_zt;  colormap=:balance, colorrange=(-2, 2))
hmqᵛ = heatmap!(axqᵛ, t_hours, z, qᵛ_zt; colormap=:dense, colorrange=(0, 1e-2))
hmF  = heatmap!(axF,  t_hours, z, Fᴿ_zt; colormap=:balance, colorrange=(-0.1, 0.1))

Colorbar(fig[1, 2], hmT;  label="T′ (K)")
Colorbar(fig[2, 2], hmqᵛ; label="qᵛ (kg/kg)")
Colorbar(fig[3, 2], hmF;  label="Fᴿ (W/m³)")

fig

Animation of cloud structure

We animate xz slices of vertical velocity and cloud liquid water, zoomed to the lowest 5 km where the convective dynamics and clouds live.

wts  = FieldTimeSeries(slices_filename, "w")
qᵛts = FieldTimeSeries(slices_filename, "qᵛ")

times = wts.times
Nt = length(times)

wlim  = maximum(abs, wts) / 4
qᵛlim = maximum(qᵛts) / 2

fig = Figure(size=(1000, 600), fontsize=14)

n = Observable(Nt)
axw  = Axis(fig[1, 1]; xlabel="x (km)", ylabel="z (km)", title="w (m/s)", limits=(nothing, (0, 5e3)))
axqᵛ = Axis(fig[1, 2]; xlabel="x (km)", ylabel="z (km)", title="qᵛ (kg/kg)", limits=(nothing, (0, 5e3)))

title = @lift "Diurnal Radiative Convection at t = " * prettytime(times[$n])
fig[0, :] = Label(fig, title, fontsize=16, tellwidth=false)

wn  = @lift wts[$n]
qᵛn = @lift qᵛts[$n]

hmw  = heatmap!(axw,  wn;  colormap=:balance, colorrange=(-wlim, wlim))
hmqᵛ = heatmap!(axqᵛ, qᵛn; colormap=:dense,   colorrange=(0, qᵛlim))

Colorbar(fig[2, 1], hmw;  vertical=false, label="w (m/s)")
Colorbar(fig[2, 2], hmqᵛ; vertical=false, label="qᵛ (g/kg)")

hideydecorations!(axqᵛ; grid=false)

CairoMakie.record(fig, "radiative_convection.mp4", 1:Nt; framerate=12) do nn
    n[] = nn
end

Julia version and environment information

This example was executed with the following version of Julia:

using InteractiveUtils: versioninfo
versioninfo()

Julia Version 1.12.6
Commit 15346901f00 (2026-04-09 19:20 UTC)
Build Info:
  Official https://julialang.org release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 8 × AMD EPYC 7R13 Processor
  WORD_SIZE: 64
  LLVM: libLLVM-18.1.7 (ORCJIT, znver3)
  GC: Built with stock GC
Threads: 1 default, 1 interactive, 1 GC (on 8 virtual cores)
Environment:
  JULIA_GPG = 3673DF529D9049477F76B37566E3C7DC03D6E495
  JULIA_LOAD_PATH = :@breeze
  JULIA_PKG_SERVER_REGISTRY_PREFERENCE = eager
  JULIA_VERSION = 1.12.6
  JULIA_DEPOT_PATH = /usr/local/share/julia:
  JULIA_PATH = /usr/local/julia
  JULIA_PROJECT = @breeze

These were the top-level packages installed in the environment:

import Pkg
Pkg.status()

Status `/__w/Breeze.jl/Breeze.jl/docs/Project.toml`
  [86bc3604] AtmosphericProfilesLibrary v0.1.7
  [660aa2fb] Breeze v0.5.3 `.`
⌅ [052768ef] CUDA v6.0.0
  [13f3f980] CairoMakie v0.15.11
⌅ [6a9e3e04] CloudMicrophysics v0.35.0
  [e30172f5] Documenter v1.17.0
  [daee34ce] DocumenterCitations v1.4.1
  [7da242da] Enzyme v0.13.156
  [46192b85] GPUArraysCore v0.2.0
  [63c18a36] KernelAbstractions v0.9.41
  [98b081ad] Literate v2.21.0
  [85f8d34a] NCDatasets v0.14.15
  [9e8cae18] Oceananigans v0.110.1
  [a01a1ee8] RRTMGP v0.21.9
  [3c362404] Reactant v0.2.264
  [b77e0a4c] InteractiveUtils v1.11.0
  [44cfe95a] Pkg v1.12.1
  [9a3f8284] Random v1.11.0
Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`

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