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Building Bluelink

Building Bluelink. David Griffin, Peter Oke, Andreas Schiller et al. March 2007 CSIRO Marine and Atmospheric Research. Introduction. Bluelink : a partnership between the Bureau of Meteorology, CSIRO and the Royal Australian Navy. Introduction. Bluelink : a partnership between the

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Building Bluelink

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  1. Building Bluelink David Griffin, Peter Oke, Andreas Schiller et al. March 2007 CSIRO Marine and Atmospheric Research

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

  3. Introduction • 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

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

  5. Where they want it:

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

  7. Bluelink Ocean Data Assimilation System, BODAS • Ensemble OI … sequential assimilation technique

  8. Bluelink Ocean Data Assimilation System, BODAS • Ensemble OI … sequential assimilation technique • Assimilates observations of SLA, SST, in situ T and S

  9. Bluelink Ocean Data Assimilation System, BODAS • Ensemble OI … sequential assimilation technique • Assimilates observations of SLA, SST, in situ T and S • To constrain the model to match reality, then make a forecast

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

  11. Bluelink Ocean Data Assimilation System, BODAS • The spatial structure of the covariances are determined by the statistics of the free-running model. Influence of sealevel obs at x

  12. Ensemble OI: vertical projection of surface observations- similar to multiple linear regression Plan view of sea-level increments Cross-section of temperature increments

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

  14. BRAN1.0  BRAN1.5  BRAN2.0

  15. BRAN1.0  BRAN1.5  BRAN2.0

  16. Free-running model:

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

  18. BRAN1.0:

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

  20. BRAN1.5 cf HRRA – 2005

  21. Conclusion • BRAN1.0  plenty of lessons learnt • BRAN1.5 realistically reproduces the 3-d time-varying mesoscale ocean circulation around Australia • Major threat to real-time equivalent of BRAN: • Less data available for assimilation

  22. Thank you

  23. An application: dispersal of the larvaeof Southern Rock Lobster

  24. 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

  25. BRAN1.5 vs TAO ADCP zonal currents 165E 170W 147E 140W 110W

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

  27. 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 7th day is assimilated):  RMS error of 0.7o  anomaly correlations of 0.7

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

  29. 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 Observing System Experiments 2003

  30. Observing System Experiments Metric • Depth average (0-1000 m) of the RMS “error” in potential temperature 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

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