1. Modeling study of the coastal upwelling system of the Monterey Bay area during 1999 and 2000. I. Shulman (1), J.D. Paduan (2), L. K. Rosenfeld (2), S. R. Ramp (2), J. C. Kindle (1) (1)Naval Research Laboratory, Stennis Space Center, MS (2) Naval Postgraduate School, Monterey, CA SUPPORT: NOPP “Innovative Coastal-Ocean Observing Network (ICON)” ONR Ocean Modeling and Prediction ONR Biological and Chemical Oceanography

2. OUTLINE Physical Model Configuration Model Validation and Related Issues Data Assimilation Conclusions and Future Plans

3. Hierarchy of different resolution models in the Pacific Ocean. Provides large-scale, basin-scale and small-scale view on the Monterey Bay circulation. Global (NLOM or NCOM) PWC (POM or NCOM)

4. ICON MODEL Grid resolution ~ 1-4 km, 30 vertical Open boundary conditions are derived from Pacific West Coast (PWC) NRL model (resolution ~10km). Atmospheric forcing from NOGAPS and COAMPS predictions. Assimilation of CODAR data. M1 M2 M3 M4

5. Observed and ICON model SSTs August 31, 1999 Pt. Sur Santa Crus

7. Impact of high-resolution wind forcing on ICON model predictions

8. PWC and ICON forced with ~90km wind (NOGAPS)

9. ICON forced with 9km wind ( COAMPS) PWC forced with ~ 90km wind (NOGAPS)

10. ICON forced with 9km wind (COAMPS) PWC forced with 27km wind (COAMPS)

11. Standard Deviation of SST. 4-6/99, energy at periods > 90 d removed 0.1  C 0.8  C NOGAPS (91km) COAMPS (9km) D. Blencoe, MS thesis The model run with COAMPS 9km wind forcing better captured the influence of the complex coastline and topography structure. The model run with COAMPS 9km wind displayed more details and produced stronger headland effects.

12. Coupling with larger-scale PWC model Comparison ADCP and model-predicted currents at buoy M2 Magnitude of complex correlationAngular displacements

13. Aircraft SSTs ICON PWC predictions at ICON western boundary

14. Impact of surface heat forcing on ICON model predictions. Observed and model predicted MLDs (m) 0.1 ˚C 0.2 ˚C 0.1 ˚C 0.2 ˚C

15. Offshore core of the California current California Undercurrent

16. California Undercurrent RAFOS floats vs ICON model currents, 300m

18. Conclusion With high-resolution atmospheric forcing the ICON model captures “the essence” but not the “details” of observed variability. Data Assimilation (“blending” of observations and model predictions) is needed

19. HIGH FREQUENCY RADAR (CODAR) DATA ASSIMILATION IN THE MONTEREY BAY.

20. APPROACH Methods of using HF radar data to provide corrections to the model wind forcing are investigated. Figure 4. Alongshore component of wind at the M1 mooring and the mode 1 amplitude for the radar-derived (CODAR) surface velocity fields as a scatter plot (left panel) and versus time (right panel). Inadequate knowledge of the wind stress is probably a significant source of error in the model solutions.

21. Figure 6. CODAR data footprints (dots) and locations of M1 and M2 moorings

22. Magnitudes of complex correlation (a) and angular displacements (b) between model- predicted currents and those observed at M2.

23. Map of complex-correlation magnitudes between observed currents at M2 and HF radar-derived surface currents (upper level in each panel) or model-predicted currents at various depths. No assim.With assim.

24. Map of complex-correlation magnitudes between observed currents at M1 and HF radar-derived surface currents (upper level in each panel) or model-predicted currents at various depths. No assim. With assim.

25. Div MICON forced with ~90km wind (NOGAPS)

26. 242d day245th day Along-shore model velocities Section AA Section BB Bioluminescence AA BB Bioluminescence maximums at 242d and 245 th days are located in the frontal areas representing a strong reversal of flow direction.

27. OBSERVED PROFILES Section AA (242d day)Section BB (245d day) SECTION AA Initialization (242d day) Prognostic calculations (245 th day)

28. FUTURE Use of circulation model for optimal and adaptive sampling Bio-optical and physical modeling Data Assimilation: CODAR, SSTs, glider and mooring data, estimation and modeling covariances.