IICWG 5 th Science Workshop, April Sea ice modelling and data assimilation in the TOPAZ system Knut A. Lisæter and Laurent Bertino

IICWG 5 th Science Workshop, April Acknowledgement Funding from projects European Commission –DIADEM (Mast-III ) –TOPAZ (FP )  MERSEA IP (FP ) ESA –SIREOC ( ) –EMOFOR ( ) Gulf of Mexico –ROSES Industry (NWAG, WANE…) Norwegian research council

IICWG 5 th Science Workshop, April 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)

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April TOPAZ model system Atlantic and Arctic: 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: –TOPAZ: Jan > now –MERSEA IP: 2004 onwards

IICWG 5 th Science Workshop, April 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,..)

IICWG 5 th Science Workshop, April Assimilating data with holes Example of AVHRR SST Weekly averages 1/3 rd degree Processed by CLS No need to fill in the holes …

IICWG 5 th Science Workshop, April AnalysisNowcast Forecast Weekly Forecast Cycle AnalysisNowcast 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-7d-0 d+10

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April Ice concentration 31. March 2004 Observations 10 day Forecast

IICWG 5 th Science Workshop, April Ice concentration Observations Analysis

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April Ice concentration assimilation - winter Ice concentration Surface temperature

IICWG 5 th Science Workshop, April Ice concentration assimilation - winter Ice concentration Surface salinity

IICWG 5 th Science Workshop, April Ice concentration assimilation - summer Ice concentration Surface temperature

IICWG 5 th Science Workshop, April Ice concentration assimilation - summer Ice concentration Surface salinity

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April Ice thickness assimilation experiment SIREOC Project(ESA) Used “cryosat-like” synthetic ice thickness Assimilated with EnKF Coarse model grid (not the TOPAZ grid)

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April 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

IICWG 5 th Science Workshop, April Idealized view of forecasting

IICWG 5 th Science Workshop, April 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)

IICWG 5 th Science Workshop, April Risk Assessment Assessing forecast uncertainty a necessity

IICWG 5 th Science Workshop, April 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)

IICWG 5 th Science Workshop, April 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