LCDR. Chin-Lung Fang

1. Predictability of Japan / East Sea (JES) System to Uncertain Initial / Lateral Boundary Conditions and Surface Winds LCDR. Chin-Lung Fang

2. Outline • Introduction • Experimental design • Statistical analysis methods • Results • Conclusions

3. Introduction Numerical ocean modeling Three Difficulties JES Geography & bottom topography Princeton Ocean Model It is important for us to investigate the response of a ocean model to these uncertainties. Initial- and Boundary-value problems Three major difficulties Uncertainty of the initial velocity condition Uncertainty of the open boundary condition Uncertainty of the atmospheric forcing

4. Introduction Tatar Strait (connects with the Okhotsk Sea) • Three Difficulties • JESGeography & bottom topography • Princeton Ocean Model Soya Strait (connects with the Okhotsk Sea) Korea/Tsushima Strait (connects with the North Pacific) Tsugaru Strait (connects with the North Pacific)

5. σ =0 Z=0 σ =-1 Introduction • Three Difficulties • JES Geography & bottom topography • Princeton Ocean Model • General information • Surface & lateral boundary forcing • Two step initialization POM : a time-dependent, primitive equation model rendered on a three-dimensional grid that includes realistic topography and a free surface. 91 grid points 10’ (11~15 Km) 23 σ levels 10’ (18 Km) 100 grid points

6. Introduction • Three Difficulties • JES Geography & bottom topography • Princeton Ocean Model • General information • Surface & lateral boundary forcing • Two step initialization Wind stress at each time step is interpolated from monthly mean climatological wind stress from COADS (1945-1989). Volume transports at open boundaries are specified from historical data. Unit: Sv, 1 Sv = 106 m3s-1

7. Introduction The first step: • Three Difficulties • JES Geography & bottom topography • Princeton Ocean Model • General information • Surface & lateral boundary forcing • Two step initialization From zero velocity and Tc and Sc fields (Levitus). Wind stress from COADS data & without flux forcing. The final states are taken as initial conditions for the second step F.S. I.C. JD-1 JD-360/JD-1 JD-360 The second step: From the final states of the first step. Wind stress from COADS data & with flux forcing. The final states are taken as standard initial conditions(V0,T0,S0) for the experiments. F.S. (VJD180,TJD180,SJD180) I.C. JD-1 JD-360/JD-1 JD-180

8. Experimental Design

9. Experimental Design • Control Run • Uncertain Initial Conditions • Uncertain Wind Forcing • Uncertain Lateral Transport • Combined Uncertainty From the standard initial conditions(V0 = VJD180 , T0 = TJD180 , S0 = SJD180) . Lateral transport from historical data and Wind stress from COADS data & with flux forcing. I.C. JD-180 JD-360 The simulated temperature and salinity fields and circulation pattern are consistent with observational studies (Chu et al. 2003).

10. Experimental Design • Control Run • Uncertain Initial Conditions • Uncertain Wind Forcing • Uncertain Lateral Transport • Combined Uncertainty

11. Experimental Design • Control Run • Uncertain Initial Conditions • Uncertain Wind Forcing • Uncertain Lateral Transport • Combined Uncertainty

12. Experimental Design • Control Run • Uncertain Initial Conditions • Uncertain Wind Forcing • Uncertain Lateral Transport • Combined Uncertainty

13. Experimental Design • Control Run • Uncertain Initial Conditions • Uncertain Wind Forcing • Uncertain Lateral Transport • Combined Uncertainty

14. Statistical Analysis Methods Model Error : Root Mean Square Error (RMSE) : Relative Root Mean Square Error (RRMSE) :

15. Model Errors Due To Initial Conditions The 5th Day The 180th Day • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Model error is decreasing with time. Difference among the four runs is not significant. Difference among each run is < 0.3 cm/s Difference among each run is < 0.15 cm/s

16. Model Errors Due To Initial Conditions • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Difference among each run is < 0.2 cm/s Difference among each run is < 0.1 cm/s Model error is decreasing with time. Difference among the four runs is not significant.

17. Model Errors Due To Initial Conditions The 5th Day The 180th Day • Model Error Distribution • Relative Root Mean Square Error (RRMSE) • Vertical Variation • Temporal Evolution 26% In Run 2 75% In Run 1 Effects to the horizontal velocity prediction are quite significant. 50% 20% Noobvious difference among these four runs.

18. Model Errors Due To Wind Forcing • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Larger model error in Run 6. Model error is increasing with time.

19. Model Errors Due To Wind Forcing • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Larger model error in Run 6. Model error is increasing with time.

20. Model Errors Due To Wind Forcing The 5th Day The 180th Day • Model Error Distribution • Relative Root Mean Square Error (RRMSE) • Vertical Variation • Temporal Evolution Run 5 Run 6 76% In Run 6 58% In Run 6 Larger model error in Run 6. 28% 11% Effects to the horizontal velocity prediction are quite significant.

21. Model Errors Due To Open Boundary Conditions • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Larger model error in Run 8. Model error is increasing with time.

22. Model Errors Due To Open Boundary Conditions • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Larger model error in Run 8. Model error is increasing with time.

23. Model Errors Due To Open Boundary Conditions The 5th Day The 180th Day • Model Error Distribution • Relative Root Mean Square Error (RRMSE) • Vertical Variation • Temporal Evolution Run 7 Run 8 28% In Run 8 23% In Run 8 Larger model error in Run 8. 34% Effects to the horizontal velocity prediction are quite significant. 17%

24. Model Errors Due To Combined Uncertainty • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Larger model error in Run 11. Model error is decreasing with time.

25. Model Errors Due To Combined Uncertainty • Model Error Distribution • Horizontal distribution • Histogram • Relative Root Mean Square Error (RRMSE) Larger model error in Run 11. Model error is decreasing with time.

26. Model Errors Due To Combined Uncertainty The 5th Day The 180th Day • Model Error Distribution • Relative Root Mean Square Error (RRMSE) • Vertical Variation • Temporal Evolution 78% In Run 11 73% In Run 11 Run 11 Larger model error in Run 11. 55% Run 10 30% Effects to the horizontal velocity prediction are quite significant. Run 9

27. Conclusions For uncertainvelocity initial conditions : • The model errors decreases with time. • The model errors with and without diagnostic initialization are quite comparable and significant. • The magnitude of model errors is less dependent on the diagnostic initialization period no matter it is 30 day,60 day or 90 day.

28. Conclusions For uncertainwind forcing : • The model error increases with time and noise intensity.

29. Conclusions For uncertainlateral boundary transport : • The model error increases with time and noise intensity.

30. Conclusions For combined uncertainty :

31. ? ? ? ? ? Question ?

32. Thank you !!