1. www.cmar.csiro.au/bluelink/ Building Bluelink David Griffin, Peter Oke, Andreas Schiller et al. March 2007 CSIRO Marine and Atmospheric Research

2. Bluelink: a partnership between the Bureau of Meteorology, CSIRO and the Royal Australian Navy Introduction

3. Bluelink: a partnership between the Bureau of Meteorology, CSIRO and the Royal Australian Navy Talk Outline Ocean Forecasting Australia Model, OFAM Bluelink Ocean Data Assimilation System, BODAS Bluelink ReANalysis, BRAN Bluelink High-Resolution Regional Analysis HRRA Introduction

4. WA example

5. HRRA - Gridded altimetry and SST, statistically projected to depth:

6. Free-running model:

7. BRAN1.0:

8. BRAN1.5 smoother, more realistic, no warm bias

9. BRAN1.5 cf HRRA – 2005

10. Where they want it:

11. Ocean Forecasting Australia Model, OFAM … every 10 th grid point shown  Global configuration of MOM4  Eddy-resolving around Australia  10 m vertical resolution to 200 m, then coarser  Surface fluxes from ECMWF (for reanalyses) Minimum resolution: ~100km ~10km resolution

12. Bluelink Ocean Data Assimilation System, BODAS  Multivariate assimilation system:  sea level obs correct h,T,S,U,V Single point assimilation …

13. Cross-section of temperature bkgnd (grey) & analysis (black- colour) Plan view of sea-level increments -> need both SST and SLA.

14. BRAN1.0  BRAN1.5  BRAN2.1 BRAN1.0BRAN1.5BRAN2.1 10/1992-12/20041/2003-6/200610/1992-12/2006 Assimilates along-track SLA, T(Z), S(z) Assimilates along-track SLA, T(z), S(z), AMSRE - SST Assimilates along-track SLA, T(z), S(z), AMSRE – SST or Rey 1/4 o OISST no rivers Seasonal climatological river fluxes SSS restoring (30 days); SST restoring (30 days) no SSS or SST restoring SSS restoring (30 days in deep water only); no SST restoring ECMWF surface heat, freshwater and momentum fluxes ECMWF surface heat and freshwater fluxes; and momentum fluxes from 10 m winds 3 day assimilation cycle7 day assimilation cycle with 1 day nudging using 1 day relaxation 7 day assimilation cycle with 1 day nudging using 0.25 day relaxation A few bugsNo known bugs (yet)Fits data fairly loosely, ie large residuals

15. BRAN1.0  BRAN1.5  BRAN2.1 BRAN1.0BRAN1.5BRAN2.1 Warm biasNo temperature bias Noticeably discontinuous in time (jumpy, shocks etc) Acceptably continuous (can track features easily) SST errors ~ 2-3 degreesSST errors ~ 0.6-0.8 degrees SLA errors ~ 15 cmSLA errors ~ 8 cm

16. Conclusion BRAN1.0  plenty of lessons learnt BRAN2.1 realistically reproduces the 3-d time-varying mesoscale ocean circulation around Australia We are working on ways of assimilating the data tighter without introducing spurious features.

17. Thank you

18. An application: dispersal of the larvae of Southern Rock Lobster

19. What users want: (a week in advance?)

20. Bluelink ReANalysis, BRAN BRAN1.5:  1/2003 – 6/2006  Forced with ECMWF forecast fluxes  Assimilates observations once per week  Assimilates SLA from Jason, Envisat and GFO (T/P with-held)  Assimilates AMSRE SST  Assimilates T and S from Argo and ENACT database

21. BRAN1.5 vs TAO ADCP zonal currents 165E170W 147E140W110W

22. BRAN1.5 vs CLS 1/3 o GSLA ANALYSIS 0-DAY FORECAST 7-DAY FORECAST

23. Comparisons with with-held T/P altimetry (top) and AMSRE (bottom) Comparisons between BRAN1.5 and with-held T/P altimetry:  RMS error of 8-10 cm  anomaly correlations of 0.6 Comparisons between BRAN1.5 and AMSRE (every 7 th day is assimilated):  RMS error of 0.7 o  anomaly correlations of 0.7

24. Observing System Experiments Experiment design  With-hold each component of the observing system  6-month integration (1 st half of 2003)  compare to with-held observations  treat BRAN1.5, with all observations assimilated, as the “truth”

25. Observing System Experiments Assimilation of Argo and SST reduces the forecast error of SLA by ~50% compared to the assimilation of altimetry Assimilation of Altimetry and Argo only reduces the forecast error of SST by a small amount 2003

26. Observing System Experiments For the 2003 - GOOS:  each component of the GOOS has a unique and important contribution to the forecast skill of upper ocean temperature  each component has comparable impact on the forecast skill of the upper ocean temperature Metric  Depth average (0-1000 m) of the RMS “error” in potential temperature