iicwg 5 th science workshop, april 19-21 - 2004 sea ice modelling and data assimilation in the topaz...

IICWG 5th Science Workshop, April 19-21 - 2004

Sea ice modelling and data assimilation in the

TOPAZ system

Knut A. Lisæter and

Laurent Bertino


Acknowledgement Funding from projects

• European Commission– DIADEM (Mast-III 1998-2000)– TOPAZ (FP5 2000-2003) MERSEA IP (FP6 2004-2008)

• ESA– SIREOC (2001-2002)– EMOFOR (2003-2005) Gulf of Mexico– ROSES

• Industry (NWAG, WANE…)• Norwegian research council


Ingredients of a ocean forecasting system

• Numerical models– HYCOM + KPP (U.

Miami - LANL,USA) – Sea Ice

thermodynamics model – Sea Ice Dynamics

model (EVP, Hunke & Dukowicz 1997)

– Ecosystem models (AWI, D)


Ingredients of a ocean forecasting system

• Observations– Altimetry, SST (CLS, F)– Sea Ice concentration (NSIDC, USA)– In-situ T & S (CORIOLIS, F)

• Data assimilation– Ensemble Kalman Filter (Evensen 1994,

2003)– OI


TOPAZ model system• Atlantic and Arctic: 18-30 km

resolution. • EnKF data assimilation (SLA,

SST and Ice concentration)• Downscaling: high resolution

regional models (4-5 km)• A flexible modular system

used for hindcast studies• Real-time operations

– DIADEM: 1999-2000– TOPAZ: Jan. 2003 -> now – MERSEA IP: 2004 onwards

• http://topaz.nersc.no


Advanced Data Assimilation

How observations should influence the model

• The bottleneck of numerical weather forecasts?• Theory: system control + spatial statistics• Ensemble Kalman filter

– “The model has the best knowledge of the ocean processes”– Forecast + the related uncertainty– Assumes errors in atm. fields – Robust and flexible (SLA, SST, ice concentrations and

thickness, in-situ T-S profiles, Ocean color, TB, ..)


Assimilating data with holes

• Example of AVHRR SST

• Weekly averages• 1/3rd degree• Processed by CLS• No need to fill in

the holes …


Analysis Nowcast

Forecast

Weekly Forecast Cycle

Analysis Nowcast

Forecast

• Atlantic, North Sea and Gulf of Mexico models– Same cycle, different forecast length– Atmospheric forcing fields from ECMWF (10d Forecast,

reverts to Climatology 28 days)

d-7 d-0

d+10


Sea Ice assimilation in TOPAZ

• Observations - ice concentration– Near Real-time TB data from NSIDC (SSM/I)– Conversion to ice concentrations at NERSC

• Sea Surface Temperature must be considered– Assimilation without SST correction can quickly melt ice

• The influence on e.g. salinity is PROCESS dependent:– “Local” ice melting/freezing– Transport through thermal fronts

• Requires dynamical error handling• Assimilation of CRYOSAT-like ice thickness evaluated


Ice concentration Maps

• Examples 31st March 2004• Comparison of

– Observations– Forecast– Analysis

• Assimilation affects the position of the ice edge

• Analysis and forecasts similar on large scale, details are different


Ice concentration 31. March 2004

Observations 10 day Forecast


Ice concentration 31.3.2004

Observations Analysis


Sea ice information available from TOPAZ

• Model fields of– Ice concentration– Ice thickness– Ice drift– Ice temperature

• Categories-daily fields– Analysis– Forecasts

• Regional models– Barents sea (to come)

• Rheology• nesting


Examples of ice assimilation updates

• Illustrates the effect of assimilating ice concentration– Updates: After assim. - Before assim– “Typical” winter and summer situations– Shows the impact assimilation has on T & S– Different behavior at different times of the

year– Strongest effect on the ice edge, especially

in winter– From Lisæter et al. 2003


Ice concentration assimilation - winter

Ice concentration Surface temperature


Ice concentration assimilation - winter

Ice concentration Surface salinity


Ice concentration assimilation - summer

Ice concentration Surface temperature


Ice concentration assimilation - summer

Ice concentration Surface salinity


Ice concentration assimilation experiment -

cumulative effect• RMS Difference model-

observations– Assimilation corrects model

behavior– Strongest effect in summer– Sawtooth effect due to

assimilation– Winter forcing provides

“relaxation” in both runs…– Observations problematic in

summer


Ice thickness assimilation experiment

• SIREOC Project(ESA)• Used “cryosat-like”

synthetic ice thickness

• Assimilated with EnKF

• Coarse model grid (not the TOPAZ grid)


• EnKF provides time-varying statistics

• Highest error near the ice edge

• Decreasing error within the ice pack(bias)

• Region of high error variance “follows” the ice edge

• Similar behavior for ice concentration errors

Evolution of model ice thickness error


Important for ice assimilation

• The ice and ocean are connected!– > multivariate assimilation – > “coupled” assimilation of variables in the

ice and ocean model

• Model error statistics are process-dependent– Transport across fronts + melting– “Local” melting

• Error statistics have highest magnitudes close to the ice edge


Idealized view of forecasting

Decision making

User

Forecast

Researcher


Why statistics again?

“As soon as a map is put out, everybody around the table tends to consider it as the

truth” (old saying from the mining industry)


Risk Assessment

Assessing forecast uncertainty

a necessity

Decision making

User

Forecasts Uncertainty

Researcher


Advantages of the NERSC/TOPAZ system

• Advanced data assimilation techniques– A physical view on the system uncertainty– Intensive machine use, but high reliability

• Model flexibility – General formulation(hybrid coordinate)– Easily relocatable: any sea in the world

• TOPAZ is the NERSC contribution to the – GMES – MERSEA and GODAE initiatives (Arctic

system)


Plans

• Data assimilation:– In-situ + MDT products– Cryosat/ICESAT Ice Thickness– Ice Drift– Local assimilative systems– 100 down to 30 members?

• Model Improvements– New HYCOM version (MPI)– Multi-category Sea Ice model– Sea-Ice rheology suitable for small-scale modelling?

• Further applications– Ice forecasts, ship routing, oil spills, environmental monitoring

iicwg 5 th science workshop, april 19-21 - 2004 sea ice modelling and data assimilation in the topaz...

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