www.cmar.csiro.au/bluelink/ building bluelink david griffin, peter oke, andreas schiller et al....
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www.cmar.csiro.au/bluelink/
Building Bluelink
David Griffin, Peter Oke, Andreas Schiller et al.March 2007
CSIRO Marine and Atmospheric Research
Bluelink: a partnership between the
Bureau of Meteorology, CSIRO and
the Royal Australian Navy
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
Introduction
WA example
HRRA - Gridded altimetry and SST,statistically projected to depth:
Free-running model:
BRAN1.0:
BRAN1.5smoother, more realistic, no warm bias
BRAN1.5 cf HRRA – 2005
Where they want it:
Ocean Forecasting Australia Model, OFAM
… every 10th 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
Bluelink Ocean Data Assimilation System, BODAS
Multivariate assimilation system:
sea level obs correct h,T,S,U,V
Single point assimilation …
Cross-section of temperaturebkgnd (grey) &
analysis (black-colour)
Plan view of sea-level
increments
-> need both SST and SLA.
BRAN1.0 BRAN1.5 BRAN2.1
BRAN1.0 BRAN1.5 BRAN2.1
10/1992-12/2004 1/2003-6/2006 10/1992-12/2006
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 1/4o OISST
no rivers 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, freshwater and momentum fluxes
ECMWF surface heat and freshwater fluxes; and momentum fluxes from 10 m winds
3 day assimilation cycle 7 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 bugs No known bugs (yet) Fits data fairly loosely, ie large residuals
BRAN1.0 BRAN1.5 BRAN2.1
BRAN1.0 BRAN1.5 BRAN2.1
Warm bias No temperature bias No temperature bias
Noticeably discontinuous in time (jumpy, shocks etc)
Acceptably continuous (can track features easily)
SST errors ~ 2-3 degrees SST errors ~ 0.6-0.8 degrees
SLA errors ~ 15 cm SLA errors ~ 8 cm
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.
Thank you
An application: dispersal of the larvaeof Southern Rock Lobster
What users want:(a week in advance?)
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
BRAN1.5 vs TAO ADCP zonal currents
165E 170W
147E 140W 110W
BRAN1.5 vs CLS 1/3o GSLA
AN
ALY
SIS
0-D
AY
FO
RE
CA
ST
7-D
AY
FO
RE
CA
ST
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
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”
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
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