1. A Forecasting system for the Southern California Current Emanuele Di Lorenzo Arthur Miller Bruce Cornuelle Scripps Institution of Oceanography, UCSD

2. Forecast the mesoscale eddies Understand the physics that control their generation and evolution Assess the biological response

3. Observational Dataset Southern California Coast and Baja Temperature, Salinity and Zooplankton 1949 – 2003 seasonal data 20 m vertical resolution, from 0– 500 m 70 - 80 km horizontal grid CalCOFI historical sampling grid California Cooperative Oceanic Fisheries Investigation Hydrography

4. The Strategy Initialize the model by assimilating the slowly evolving component of the eddy field which can potentially lead to forecast skill over a period of 2 months

5. The Strategy The Method The Green’s Function Method Initialize the model by assimilating the slowly evolving component of the eddy field which can potentially lead to forecast skill over a period of 2 months

6. A typical sampling of a mesoscale eddy E1 “SSH”

7. AVVISO TOPEX/ERS [m] E1 E2 SSH E1 E2 E1 “SSH” SSH CalCOFI Coastal observation Data Assimilation Ocean Circulation Model Satellite Data Independent verification Assimilation of CalCOFI T,S 65% reduction in error variance relative to the model initial guess!

8. SeaWIFS [  M N/m 3 ] E2 E1 E3 Chl-a [  M N/m 3 ] E1 E2 E3 Chl-a E3 Chl-a Ecosystem Model Forecasting and Hindcasting Ocean Productivity Independent verification CalCOFI in Situ REFERENCE: Di Lorenzo, E., A. J. Miller, D. J. Neilson, B. D. Cornuelle, and J. R. Moisan, 2003: Modeling observed California Current mesoscale eddies and the ecosystem response. International Journal of Remote Sensing, in press.

9. You need to know the physics that goes into the assimilation scheme

10. E1 “SSH”

11. 1010 JAN APR JUL OCT 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 0.5 SKILL associated with Persistence of Initial Condition SSHSurface TT 150 m days

12. E1 “SSH” Data is collected over a 20 day period

13. JAN APR JUL OCT 1010 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 0.5 SSHSurface TT 150 m days SKILL evolution when the true initial condition is replaced with a 20 day average in a dynamical forecast

14. JAN APR JUL OCT 1010 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 0.5 SSHSurface TT 150 m days

15. How about the uncertainties in Forcing Functions?

16. 1010 JAN APR JUL OCT 30 60 40 50 10 20 30 60 40 50 10 20 0.5 SKILL evolution with errors in Forcing Surface TT 150 m days

17. JAN APR JUL OCT 1010 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 0.5 SKILL evolution with errors in Forcing (and Open BC) Surface TT 150 m days SKILL evolution with errors in initial condition JUNE

18. Wavenumber Spectra

19. Forecast the mesoscale eddies Real time forecast of CalCOFI in April 2003 SCCOOS nowcast-forecast with UCLA and JPL Understand the physics that control their generation and evolution Error Covariances Seasonal dependence Assess the biological response In progress…. Concluding remarks:

20. ΔρΔρ Ekman Pumping  Δρ increase Δh westward propagation off the Bight Wind P. Conception Shelf (a) Instability processes on continental slope currents (b) (c) April July

24. Ocean Temperature Zooplankton Log e Tot. Vol. 5 7 6 4 Anomalies 199019701950200019801960 C 1 0 Observations along the California Coast

25. Assimilation Method

26. E1 “SSH” CalCOFI Coastal observation Assimilation of CalCOFI T,S

27. E1 E2 E1 “SSH” SSH CalCOFI Coastal observation Data Assimilation Ocean Circulation Model Assimilation of CalCOFI T,S

28. What have we learned about the mesoscale dynamics?

29. What have we learned about the mesoscale dynamics?

30. The Green’s Function Method Green’s Function

32. JAN APR JUL OCT 1010 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 30 60 40 50 10 20 0.5 Changes in SKILL associated with errors in Forcing Surface TT 150 m days