The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Sub-Seasonal Prediction Activities and Plans in CAWCR Debbie Hudson, Andrew Marshall, Oscar Alves, Harry Hendon, Matthew Wheeler, Yonghong Yin, Guomin Wang

Originally designed for seasonal prediction POAMA-1 operational 2002 POAMA-1.5 operational 2007 (10 member ensemble) POAMA-2 operational 2011 (33 member ensemble) Has separate sub-seasonal and seasonal systems POAMA-3 ACCESS-based development version (UM atmospheric model; ocean model based on GFDL MOM4) POAMA: Predictive Ocean Atmosphere Model for Australia Atmospheric Model BAM T47L17 Ocean Model ACOM2 (lat 0.5~1.5º; lon 2º; 25 lvls) 3h OASIS Coupler Atmos and Land Initial Condition Ocean Initial Condition POAMA v1.5, v2

Seasonal System Sub-seasonal System Atmosphere/land data assimilation ALI (Atmosphere Land Initialisation Scheme: nudging atmos model to ERA-40 or operational NWP) Ocean data assimilation PEODAS (Multivariate pseudo- Ensemble Kalman Filter) Ensemble generation 30 members Multi-model (3 versions) Ocean perturbations from PEODAS; No atmosphere perturbations 33 members Multi-model (3 versions) Ocean and atmosphere perturbations from Coupled Ensemble Initialisation Scheme (CEIS) Hindcast 30 members on the 1 st of the month out to 9 months ( ) 33 members on the 1 st, 11 th and 21 st of the month out to 120 days ( ) Operational 30 members on the 1 st and 15 th of the month out to 9 months 33 members every 0z Thursday out to 120 days POAMA-2 Systems

Activities in sub-seasonal Documenting the skill of the current POAMA system Understanding predictability of Australian climate on sub- seasonal 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)

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 POAMA-2 (seasonal system) Ensemble from lagged atmospheric initial conditions Ensemble from ocean perturbations only Why do the seasonal and sub-seasonal systems differ in their ensemble generation strategy?

Coupled Model integrations 1 day Bred vectors are rescaled and centred to the central analyses Central unperturbed analyses: PEODAS and ALI Coupled Ensemble Initialisation System Coupled Model integrations 1 day Bred vectors are rescaled and centred to the central analyses Central unperturbed analyses: PEODAS and ALI Coupled Ensemble Initialisation System Generates coupled perturbations of the atmosphere and ocean based on a breeding method Initial Conditions for the Sub-Seasonal System Towards Coupled Assimilation... Based on the PEODAS infrastructure

POAMA-1.5POAMA-2 (seas) POAMA-2 (intra) POAMA-1.5 POAMA-2 (seas) POAMA-2 (intra) u10 anom; SHEM 20º- 60ºS; JAN/JUL/AUG RMSE Ensemble spread Correlation

Fortnight 1 Fortnight 2 Australian RAINFALL above upper tercile: all forecast start months POAMA-1.5 POAMA-2 (seas) POAMA-2 (intraseas)

POAMA has good skill in predicting rainfall and TMAX over eastern Australia in the second fortnight of the forecast, particularly during spring forecast months. 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, ). ROC areas significantly more skilful than climatology are shaded (5% significance level). Precipitation TMAX

MJO SAM Blocking Evaluating key drivers of Australian intra- seasonal climate variability (Risbey et al. 2009)

Climate drivers operating on timescales longer than intraseasonal influence prediction skill (Hudson et al 2011) El Nino / La Nina (n=48) Neutral (n=60) IOD small (n=30) For rainfall forecast in the 2nd fortnight, there is higher skill when the IOD is strong and when ENSO is in an extreme (JJASON) Rainfall correlation skill IOD large (n=30) ENSO in neutral state

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) Research Approach: MJO, SAM, and blocking

MJO – simulation of rainfall (Marshall et al 2011)

RMSE & correlation between observed and POAMA RMM indices (over all start months) Climatological forecast skill CORRMSE POAMA-2 skill exceeds POAMA-1.5 MJO – Prediction of Index Wheeler and Hendon (2004) RMM Index (Rashid et al 2010, Marshall et al 2011)

ROC area: rainfall in the upper tercile (POAMA-2) MJO – prediction of rainfall in weeks 3-4 (Nov-Apr)

Real-time products on POAMA web Operational configuration: 33 member ensemble Updated once per week 120 day forecast

Products for hindcasts also available Also include skill MJO Example

Plans (2012) 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)