Building Bluelink

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

What users want:(a week in advance?)

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

Ensemble OI … sequential assimilation technique



Assimilates observations of SLA, SST, in situ T and S



Assimilates observations of SLA, SST, in situ T and S

To constrain the model to match reality, then make a forecast


Multivariate assimilation system:

sea level obs correct h,T,S,U,V

Single point assimilation …


The spatial structure of the covariances are determined by the statistics of the free-running model.

Influence of sealevel obs at x

Ensemble OI: vertical projection of surface observations- similar to multiple linear regression

Cross-section of temperature

increments

Plan view of sea-level

increments

Cross-section of temperaturebkgnd (grey) &

analysis (black-colour)

Plan view of sea-level

increments

-> need both SST and SLA.

BRAN1.0 BRAN1.5 BRAN2.0


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) Fingers crossed



Warm 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

Free-running model:

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

BRAN1.0:

BRAN1.5smoother, more realistic, no warm bias

BRAN1.5 cf HRRA – 2005

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

Thank you

An application: dispersal of the larvaeof Southern Rock Lobster

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”


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


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

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