Basics of numerical oceanic and coupled modelling

Antonio NavarraIstituto Nazionale di Geofisica e Vulcanologia

Italy

Simon MasonScripps Institution ofOceanography

USA

Sea Ice

Oceans

The Climate System

Biosphere

Soil MoistureRun-off

Atmosphere

PrecipitationEvaporation

Oceans -- Soil -- Cyosphere -- Biosphere

COOLING

HEATING

Latent Heat

Win

d St

ress

RAINEVAPORATION

Sensible Heat

REFL ECT IONEM

ISSI

ON

EMIS

SION

ABSO

RPTI

ONTRAN

SPORT

PRES

SIO

NE

Radiation

Temperature WaterVapor

TRANSPORT

Solar Radiation

EarthRadiationWind

Ocean Models

• All atmospheric GCMs have some form of ocean component, and all ocean models have some form of atmospheric component.

• Hierarchy of complexity: swamp ocean slab ocean detailed mixed-layer dynamical ocean

Atmosphere

Latent Heat Flux

Wind Stress

RAINEVAPORATION

Sensible Heat

Temperature

Currents

TRANSPORT

Solar Radiation

Salinity

TRANSPO

RT

Atmosphericradiation

Density

Swamp Slab Mixed-layer

Dynamicalnon-eddyresolving

Dynamicaleddy-resolving

Moistureexchange withatmosphereSea-surfacetemperaturecalculatedVerticaltransfer ofheatOceancurrents

Dynamical Models

• Important differences between ocean and atmosphere:

Dynamical Models

• Important differences between ocean and atmosphere:

Confined to only certain areas of the earth’s surface. Spectral representation is not used.

Dynamical Models

• Important differences between ocean and atmosphere:

Confined to only certain areas of the earth’s surface. Many of the important ocean models in climate prediction

are basin or sub-basin scale. Spectral representation is not used.

Smaller spatial scale of oceanic eddies compared to atmospheric eddies; also most transport is in relatively narrow ocean currents. Grid resolution needs to be much finer than in atmospheric GCMs.

Dynamical Models

• Important differences between ocean and atmosphere:

Confined to only certain areas of the earth’s surface. Spectral representation is not used.

Smaller spatial scale of oceanic eddies compared to atmospheric eddies; also most transport is in relatively narrow ocean currents. Grid resolution needs to be much finer than in atmospheric GCMs.

Much poorer observational data. Problems for initialization, verification, and parameterization

Dynamical Models

• Spatial scale:

eddy resolving models less than 0.25 resolution. non-eddy resolving models are at about 2. higher resolution required near equator, and near the

poles where currents are narrower. the coarser models are used in the fully coupled

models.

Dynamical Models

• Initialization: Problematic because of lack of observations (mainly

SSTs and surface height), very little sub-surface measurements, cf. atmospheric initialization given only surface data.

Spin-up the model using observed wind stress. Need to improve assimilation schemes – many

ocean models initialized with zero motion.

The BMRC Coupled Model

t=0

Forced Ocean Modelobs, SSTobs, ...

Assimilate Ocean

Data: T(z), , ...

FSU/BoM Winds BoM SST, SLEV

O G C MO G C MO G C MO G C M

A G C MA G C M

F O R E C A S TF O R E C A S T

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Integrate the coupled model for a period, e.g. two years, but impose observed surface temperature and salinity

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gSpin-up the oceanwith observedatmospheric forcing

Robust Diagnostic

Spin-up

But sometimes the models are simply started from climatological conditions or, in the case of climate change experiments, theprocedure may become much more sophisticated to account for effectsfrom soil and ice.

Oceans -- Sea Ice

Atmosphere

Wind Stress Precipitation Solar Radiation

AtmosphericRadiation

AirTemperature

SeaSurface

TemperatureSensible Heat Flux Latent Heat Flux

Wind Stress Fresh Water Flux

Surface Temperature

COUPLER: (1) Interpolate from the atmospheric grid tothe ocean grid and vice versa.(2) Compute fluxes

Very Large Compiuters are

needed

Project of the Earth Simulator Computer (Japan) : objective, a globalcoupled model with 5km resolution

The main problem is how to synchronize the time evolution of the atmosphere with the evolution of the ocean. The most natural choice is to have a complete synchronization (synchronous coupling):

This choice would require to have similar time steps forboth models, for instance 30min for the atmospheric model and 2 hours for the ocean model.Computationally very expensive

Atmosphere

Ocean

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t t

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Another possibility is to exploit the different time scales using the fact that the ocean changes much more slowly than the atmosphere (asynchronous coupling):

Atmosphere

Ocean

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t

Integrate for a very long time

This choice save computational time at the expenseof accuracy, but for very long simulations (thousandsof years) may be the only choice.

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Integrate for avery long time

Sea Surface Temperature

High marine temperatures in the model are too narrowly confined to the equator, in the observations the warm pool is wider

Observations

Model

Coupled models can reproduce the over-all pattern, but they tend to be warmer than observations in the eastern oceans and colder in the western portions of the oceans

Dynamical Models

• Systematic bias is a major problem with dynamical ocean models (including coupled models).

Errors in the annual cycle Climate drift - the systematic bias depends on the

forecast lead-time.

Forecast model bias

• A comparison of the coupled model 12 month Nino3 forecasts [top panel] for February (blue), May (red), August (green), and November (brown) initial conditions average over all years, compared with climatology (purple). The bottom panel show the bias relative to this climatology.

ConclusionReally, there should be no conclusion. We have only started to understand the behaviour of coupled models and there is still a long way to go.