Sea ice modelling and data assimilation in the TOPAZ system

1. Sea ice modelling and data assimilation in the TOPAZ system Knut A. Lisæter and Laurent Bertino

2. 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

3. 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)

4. 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

5. 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

6. Advanced Data AssimilationHow 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, ..)

7. Assimilating data with holes • Example of AVHRR SST • Weekly averages • 1/3rd degree • Processed by CLS • No need to fill in the holes …

8. Weekly Forecast Cycle d+10 Forecast d-7 d-0 Forecast Analysis Nowcast Analysis Nowcast • 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)

9. 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 Sea Ice assimilation in TOPAZ

10. 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

11. Ice concentration 31. March 2004 Observations 10 day Forecast

12. Ice concentration 31.3.2004 Observations Analysis

13. 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

14. 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

15. Ice concentration assimilation - winter Surface temperature Ice concentration

16. Ice concentration assimilation - winter Surface salinity Ice concentration

17. Ice concentration assimilation - summer Surface temperature Ice concentration

18. Ice concentration assimilation - summer Surface salinity Ice concentration

19. 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

20. Ice thickness assimilation experiment • SIREOC Project(ESA) • Used “cryosat-like” synthetic ice thickness • Assimilated with EnKF • Coarse model grid (not the TOPAZ grid)

21. Evolution of model ice thickness error • 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

22. 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

23. Idealized view of forecasting

24. 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)

25. Assessing forecast uncertaintya necessity Risk Assessment

26. 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)

27. 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 Plans