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