Transcript Sub-Seasonal Prediction Activities and Plans in CAWCR Debbie Hudson, Andrew Marshall, Oscar Alves, Harry Hendon, Matthew Wheeler, Yonghong Yin, Guomin Wang The Centre for.

Sub-Seasonal Prediction Activities and
Plans in CAWCR
Debbie Hudson, Andrew Marshall, Oscar Alves, Harry
Hendon, Matthew Wheeler, Yonghong Yin, Guomin Wang
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
POAMA: Predictive Ocean Atmosphere
Model for Australia
POAMA v1.5, v2
Originally designed for seasonal prediction
Atmos
and Land
Initial
Condition
Atmospheric Model
BAM T47L17
3h
OASIS Coupler
•
POAMA-1 operational 2002
•
POAMA-1.5 operational 2007 (10 member ensemble)
•
POAMA-2 operational 2011 (33 member ensemble)
Ocean
Initial
Condition
Ocean Model
ACOM2 (lat 0.5~1.5º; lon 2º; 25 lvls)
Has separate sub-seasonal and seasonal systems
•
POAMA-3 ACCESS-based development version
(UM atmospheric model; ocean model based on GFDL MOM4)
POAMA-2
Systems
Seasonal System
Sub-seasonal System
Atmosphere/land
data assimilation
ALI (Atmosphere Land
Initialisation Scheme: nudging
atmos model to ERA-40 or
operational NWP)
ALI (Atmosphere Land
Initialisation Scheme: nudging
atmos model to ERA-40 or
operational NWP)
Ocean data
assimilation
PEODAS (Multivariate pseudoEnsemble Kalman Filter)
PEODAS (Multivariate pseudoEnsemble Kalman Filter)
30 members
33 members
Multi-model (3 versions)
Multi-model (3 versions)
Ocean perturbations from
PEODAS;
No atmosphere perturbations
Ocean and atmosphere
perturbations from Coupled
Ensemble Initialisation Scheme
(CEIS)
30 members on the 1st of the
month out to 9 months (19602010)
33 members on the 1st, 11th
and 21st of the month out to
120 days (1989-2010)
30 members on the 1st and 15th
of the month out to 9 months
33 members every 0z
Thursday out to 120 days
Ensemble
generation
Hindcast
Operational
Activities in sub-seasonal
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Documenting the skill of the current POAMA system
Understanding predictability of Australian climate on subseasonal timescales
Producing experimental products
Development of POAMA-3
Two externally funded sub-seasonal R&D projects:
a) Sub-seasonal prediction with POAMA
System development; system skill; impact of initialisation strategy; sources of
predictability (MJO, SAM, blocking); experimental products (end 2012)
b) Northern Australia/Monsoon Prediction
Development and delivery of climate products for northern Australia, especially to
characterise aspects of the monsoon and northern wet season (end 2013)
Why do the seasonal and sub-seasonal systems
differ in their ensemble generation strategy?
Ensemble spread is far too small in the POAMA-2 seasonal system in
the first month of the forecast
Ensemble Spread (stddev): 500hPa heights (Jul)
Day 10 of the forecast
POAMA-1.5
Ensemble from lagged
atmospheric initial
conditions
POAMA-2
(seasonal system)
Ensemble from ocean
perturbations only
Initial Conditions for the Sub-Seasonal System
Towards Coupled Assimilation...
Based on the PEODAS infrastructure
Coupled Ensemble Initialisation System
Coupled Model integrations
Central unperturbed analyses:
PEODAS and ALI
Bred vectors are rescaled and
centred to the central
analyses
1 day
Generates coupled perturbations of the atmosphere
and ocean based on a breeding method
u10 anom; SHEM 20º- 60ºS; JAN/JUL/AUG
POAMA-1.5
POAMA-2 (seas)
RMSE
Ensemble spread
POAMA-1.5
POAMA-2 (seas)
POAMA-2 (intra)
Correlation
POAMA-2 (intra)
Australian RAINFALL above upper tercile: all forecast start months
Fortnight 1
POAMA-1.5
POAMA-2
(seas)
POAMA-2
(intraseas)
Fortnight 2
POAMA has good skill in predicting rainfall and TMAX
over eastern Australia in the second fortnight of the
forecast, particularly during spring forecast months.
Precipitation
TMAX
ROC area of the probability that rainfall (left) and TMAX (right) for the 2nd
fortnight of the forecast is in the upper tercile for spring forecast months
(SON, 1989-2006). ROC areas significantly more skilful than climatology are
shaded (5% significance level).
Evaluating key drivers of Australian intraseasonal climate variability
MJO
Blocking
SAM
(Risbey et al. 2009)
Climate drivers operating on timescales longer than
intraseasonal influence prediction skill
For rainfall forecast in the 2nd fortnight, there is higher skill when the IOD
is strong and when ENSO is in an extreme (JJASON)
El Nino / La Nina (n=48)
IOD large (n=30)
ENSO in neutral state
Neutral (n=60)
IOD small (n=30)
ENSO in neutral state
Rainfall correlation skill
(Hudson et al 2011)
Research Approach: MJO, SAM, and blocking
Simulation of
rainfall/temperature
anomalies associated
with each climate driver
Prediction of each climate
driver (~weeks)
Prediction of rainfall/temperature
anomalies associated with each
climate driver (intra-seasonal)
MJO – simulation of rainfall
(Marshall et al 2011)
MJO – Prediction of Index
Wheeler and Hendon (2004) RMM Index
RMSE & correlation between observed and POAMA RMM indices (over all start months)
RMSE
COR
Climatological
forecast skill
POAMA-2 skill exceeds POAMA-1.5
(Rashid et al 2010, Marshall et al 2011)
MJO – prediction of rainfall in weeks 3-4 (Nov-Apr)
ROC area: rainfall in the upper tercile (POAMA-2)
Real-time products on POAMA web
http://poama.bom.gov.au
Operational
configuration:
• 33 member ensemble
• Updated once per week
• 120 day forecast
Also include skill
MJO Example
Products for
hindcasts also
available
Plans (2012)
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Continue research in understanding predictability of
Australian climate on sub-seasonal timescales
Extend sub-seasonal hindcast set back to 1980
Produce experimental products
Re-contribute POAMA-2 MJO forecasts to MJO task force
Development of POAMA-3
ACCESS model: UM atmos (N96L38 – N216L80) and MOM4 ocean (0.25º tropics)
Coupled breeding + coupled assimilation
Maybe stochastic physics
2012 two additional externally funded R&D projects:
a) Predictions of heat extremes over Australia
Large-scale climate drivers/processes; prediction skill (drivers and heat event);
potentially skilful products
b) Un-coupled predictions with ACCESS
d0-45; N96L80 atmosphere-only model; sensitivity experiments to explore optimal
model configurations (e.g. horizontal and stratospheric resolution)