On the computer

What is ocean modeling?

Instruments sample the ocean at a few points. The questions that decide an operation concern the whole water body and what it does next: where a plume travels, how much CO₂ the surface takes up, how a bay responds to a heatwave. A model that solves the physics answers those questions where nothing has been measured yet, and observations say how far to trust it. This page explains how such a model works, what makes it fast, and how its skill is checked.

01 / Foundations

What an ocean model is

An ocean model is a program that solves the equations for how seawater moves and changes over time. Give it the current state of the water and the forces acting on it, and it computes what the ocean does next. atdepth solves two sets of equations at once: the physics of how water moves, and the biogeochemistry of what it carries.

Inputs · the state & forcing
TemperatureSalinityBathymetryCurrentsWind & heatTides
PhysicsEquations of motionsolved every timestep
Chemistry & biologyBiogeochemistryreacts every timestep
Carbonate chemistryDissolved O₂PlanktonNutrients
Outputs · what the ocean does next
CirculationMixingTracer transportCarbon budgetpH & pCO₂Forecast

02 / Two kinds of model

Physics, not patterns

Two ways to model the ocean. One learns from history; one solves the laws of fluid motion. The difference decides whether a model can be trusted where it has no precedent, and ocean interventions rarely have one.

observed · trainingnovel interventiontime →responsetruth

Fails outside its training data.

A pattern-learner interpolates well inside its data, then leaves the regime it was trained on and has no law to fall back on.

StatisticalLearns from past data
  • Fast where history is dense
  • Cheap to run once trained
  • Fails outside its training regime
  • Cannot explain a novel intervention
Fails outside its training data.
MechanisticSolves the physics from first principles
  • Grounded in the equations of motion
  • Generalizes to new conditions
  • Resolves cause and effect
  • Works where there is no precedent
Holds in new conditions.

atdepth builds models. When you dose alkalinity into a bay no one has dosed before, only physics can tell you where it goes.

03 / Resolution

The action happens at the meter scale

Most ocean models resolve the water in cells 10 to 100 kilometers across. But a plume, a dosing field, a farm, the things you operate, live at the scale of meters. At coarse resolution that structure is invisible. atdepth resolves the ocean down to the meter.

Legacy modelstructure lostatdepth modelplume resolved

Drag · wipe the coarse legacy filter aside to reveal the same plume resolved by atdepth

04 / Spanning scales

From ocean basin to a single bay, without a supercomputer

You cannot solve a million square kilometers at one-meter resolution. handles it: a coarse regional model sets the for a finer one, each resolving the scales it should. GPUs make it tractable, roughly 100 times faster than legacy codes.

Throughput · simulated days / wall-clock hour
Legacy CPU model0sim-days / hr
atdepth GPU twin0sim-days / hr

Real-time forecasting on accessible hardware.

05 / Coupled biogeochemistry

Physics is only half the ocean

The currents matter for what they carry. These five fields come from one Gulf of Mexico simulation at a single instant: temperature, salinity, and turbulence on the physics side; air–sea CO₂ flux and dissolved carbon on the chemistry side. Same eddies, same fronts, different quantity. That coupling turns a map of currents into a carbon budget you can report.

Temperature

Sea-surface temperature. Warm and cool water masses set stratification, gas solubility, and reaction rates, the physical conditions every tracer responds to.

Same water, different quantity. Circulation + chemistry, one carbon budget

06 / Validation

A model is a claim until you validate against data

A simulation is a hypothesis until you check it against reality. atdepth continuously compares the twin against real observations from buoys, ships, and satellites, and reports the skill: the error, the bias, the correlation. Uncertainty quantification is built into the digital twin.

Skill assessment
Skill: unvalidatedn = 0
RMSE——
root mean square error
Bias——
mean (model − obs)
Correlation——
model vs. observed
No observations. The line may look right, but without data to check it, skill is unknowable.

Sweeping the model past the observations…

RMSETypical error vs. observations
BiasSystematic offset, kept near zero
CorrelationHow well the signal is captured

See it grounded in the field

Everything here is kept honest by real ocean data. See how atdepth monitors the water.