0. Recent Development in Ocean-Atmosphere Modelling for Seasonal
Prediction in Australia
Guomin Wang, Oscar Alves and Harry Hendon
Bureau of Meteorology Research Centre
Acknowledgement: Debbie Hudson, Eun-Pa Lim, Guo Liu, Faina Tseitkin, Harun Rashid, Claire Spillman and Yonghong Yin

1. POAMA (Predictive Ocean Atmosphere Model for Australia)
Outline
POAMA (Predictive Ocean Atmosphere Model for Australia)
Bureau Dynamical Seasonal Prediction System
First version went operational in 2002
A new version (POAMA1.5/2) is in transition to operational
POAMA3 development as part of Australian Earth System Modelling project ACCESS
System components
Skill assessment
Application examples
Summary

2. POAMA - Coupled Model Components
Atmospheric Model
BAM T47L17 -> T95L17->ACCESS(UKMO+LSM) 3h
OASIS Coupler
Heat flux, P-E
Ocean Model
ACOM2 lat/lon/lev=0.5~1.5/2/25 -> AUSCOM+SIM

3. Atmos/Land Initialisation System
Atmos 3D state from ERA40 (hindcast) or NWP (real-time) 6h
Nudging Scheme
Atmospheric Model
Atmos/Land Model Integration
Past-reanalyses
Realtime
Atmos Initial Conditions

4. Ocean Data Assimilation/Initialisation system
Atmos forcing from NCEP/ERA40 (hindcast) or NWP (realtime): 6hr SST from Reynolds: 1d
Ocean Temperature from Obs network: 3d
2D univariate OI (T)
static
current correction
Ocean Model
Past-reanalyses
Realtime
3D multivariate OI (T,S,u,v)
covariance time evolving
Ocean current correction
Ocean Initial Conditions

5. Hindcasts Design
Control run initialized at 00Z on the first day of each month,
Extra 9 members initialized prior to control run initial time in progressively 6 hours interval
Leadtime: (Mar forecast initialized Mar 1st 00Z => lead 1)
Each hindcast is integrated for 9 months (lead 1-9)

6. Skill Assessment: ACC for SST and Heat Content
SST H300 +1 +3 +5

7. Skill Assessment: ACC for SST Pacific & Indian Ocean Indices
Nino IOD
ACC
RMS

8. Skill Assessment: SST Pacific & Indian Ocean Indices Performance
Nino3.4 SSTA index Hindcasts vs Observation (Ini: AUG)
IOD index Hindcasts vs Observation (Ini: AUG)

9. Skill Assessment: 2-m Temperature & Precipitation
T2m Prec
All
MAM
All
SON

10. Aus Rainfall: ACC Skill Comparison with Stat Scheme
Stat-Dynamical: (4 EOFs of predicted SON SST from POAMA to predict SON rainfall) Stat: (4 EOFs of observed August SST to predict SON rainfall)

11. Aus Rainfall: SON 1997 & 2002 Prediction from POAMA
OBS
FCST

12. MJO forecasting: OLR ACC skill of Hovmoller plots
Lead time (days)

13. MJO Forecasting: Case 19970301 Ensemble member #1 Observed
MJO Phase diagram: Wheeler and Hendon (2004), Mon Wea Rev 132:1917

14. Application: Early Warning for Great Barrier Reef Coral Bleaching (1)
The Great Barrier Reef is undisputed as one of the world’s most important natural assets. It is the largest natural feature on earth stretching more than 2,300km along the northeast coast of Australia from the northern tip of Queensland to just north of Bundaberg.
Tourism in the Great Barrier Reef catchment area is worth about $5.1 billion each year (Access Economics, 2005) plus an estimated jobs in regional communities.

15. Application: Early Warning for Great Barrier Reef Coral Bleaching (2)

16. Application: Leeuwin Current Forecasting
Obs relationship between H300 and Slp at Freo
H300 ACC Skill at leadtime=7

17. Summary POAMA 1.5/2 - Significant improvements in skill
Promising potential for dynamical seasonal prediction of rainfall over Australia
Monthly scale forecasts including MJO
25+ years and 10 members of hind-casts and Ocean/Atmos/Land re-analyses available for collaborative research
Potential applications from using POAMA outputs. Application interface necessary