Transcript 20041019_csiro_powerpoint_template.pot

Building Bluelink
David Griffin, Peter Oke, Andreas Schiller et al.
March 2007
CSIRO Marine and Atmospheric Research
www.cmar.csiro.au/bluelink/
Introduction
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
WA example
HRRA - Gridded altimetry and SST,
statistically projected to depth:
Free-running model:
BRAN1.0:
BRAN1.5
smoother, more realistic, no warm bias
BRAN1.5 cf HRRA – 2005
Where they want it:
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)
Minimum resolution: ~100km
~10km resolution
… every 10th grid point shown
Bluelink Ocean Data Assimilation System,
BODAS
Multivariate assimilation system:
sea level obs correct h,T,S,U,V
Single point assimilation …
-> need both SST and SLA.
Plan view of
sea-level
increments
Cross-section
of temperature
bkgnd (grey) &
analysis (blackcolour)
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
no rivers
no rivers
Assimilates along-track SLA,
T(z), S(z), AMSRE – SST or
Rey 1/4o OISST
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
No known bugs (yet)
7 day assimilation cycle with 1
day nudging using 0.25 day
relaxation
Fits data fairly loosely, ie large
residuals
A few bugs
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 larvae
of 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
147E
170W
140W
110W
7-DAY
FORECAST
0-DAY
FORECAST
ANALYSIS
BRAN1.5 vs CLS 1/3o GSLA
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 Altimetry and
Argo only reduces the
forecast error of SST by a
small amount
Assimilation of Argo and SST
reduces the forecast error of
SLA by ~50% compared to the
assimilation of altimetry
2003
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