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This research presents probabilistic predictions of climate change in Australia, utilizing the Reliability Ensemble Average (REA) derived from CMIP3 model simulations. The study focuses on seasonal temperature and precipitation changes during summer (DJF) and winter (JJA) across various Australian regions, analyzing results in the context of different SRES scenarios. The methodology leverages model reliability to enhance prediction accuracy, providing threshold probabilities and probability density functions (PDFs) that characterize regional climate changes. Supported by the Australian Greenhouse Office, this work contributes valuable insights for climate adaptation strategies.

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  1. Probabilistic Predictions of Climate Change in Australia using the Reliability Ensemble Average (REA) of CMIP3 Model Simulations Dr A.F. Moise & Dr D. Hudson Bureau of Meteorology Research Centre Melbourne, Australia a.moise@bom.gov.au Insert title here

  2. Overview • Methodology: REA • CMIP3 • Areas under study • Results: REA for DJF, JJA Temperature, Precipitation • Changes across SRES scenarios • Methodology: probabilistic projections • Threshold probabilities • PDF’s for Australian regions • Reliability contribution of CMIP3 models • Summary ICCC, Hong Kong, May 2007

  3. Acknowledgement This activity is supported by the Australian Greenhouse Office. References Giorgi, F., and L. Mearns, 2002. Journal of Climate, 15, 1141-1158. Giorgi, F., and L. Mearns, 2003. Geophysical Research Letters, 30 (12), art. no 1629, doi:10.1029/2003GL017130.

  4. Methodology Milestones Weighted ensemble average and RMSD (weighted by model reliability Ri) REA-rmsd REA-mean Model reliability is a function of model bias (B) AND the distance (D) from the REA average RB: performance criterion RD: convergence criterion  = Natural variability εT= Max{30yr-runMean[detrended(20th century observed T time series)]} – Min{[(…..)]} If |BTJ| < εT then RB,I= 1 If |BTJ| < εT then RB,I = 1 Model is “reliable” (Ri=1) when its bias and distance from the REA mean are within natural variability.

  5. CMIP 3 models used Models 1981-2000 and 2081-2100 - NCC high quality monthly data set OBS

  6. Regions of analysis Maps of Australia and southern Africa (1=Gabon, 2=Congo, 3=Dem.Rep. Congo, 4=Tanzania, Rwanda, Burundi, Uganda, 5=Kenya, 6=Angola, 7=Zambia, 8=Malawi, 9=Mozambique, 10=Namibia, 11=Botswana, 12=Zimbabwe, 13=Madagascar, 14=South Africa, Lesotho, Swaziland). Also shown are the regions analysed separately.

  7. Results – DJF Temperature – SRESA2 Rb REA-mean (0.4) (3.9 oC) Rd Simple-mean (0.6) (3.9 oC) R REA-rmsd (0.5) (0.6 oC) NatVar Simple-rmsd (0.3 oC) (0.9 oC)

  8. Results – JJA Temperature – SRESA2 Rb REA-mean (0.3) (3.8 oC) Rd Simple-mean (0.7) (3.7 oC) R REA-rmsd (0.5) (0.4 oC) NatVar Simple-rmsd (0.3 oC) (0.7 oC)

  9. Results – DJF Precipitation – SRESA2 Rb REA-mean (0.7) (0.0 mm/d) Rd Simple-mean (0.9) (0.0 mm/d) R REA-rmsd (0.8) (0.4 mm/d) NatVar Simple-rmsd (0.6 mm/d) (0.4 mm/d)

  10. Results – JJA Precipitation – SRESA2 Rb REA-mean (0.7) (-0.1 mm/d) Rd Simple-mean (0.9) (-0.1 mm/d) R REA-rmsd (0.8) (0.1 mm/d) NatVar Simple-rmsd (0.2 mm/d) (0.2 mm/d)

  11. Averaged changes across scenarios DJF JJA

  12. Predictions and Probabilities - Method Assume: each models’ reliability Ri is an indicator of the likelihood of its simulation  the change simulated by a more reliable model is more likely to occur! Probabilities of regional climate change: Threshold probability = summing over all P(mi) exceeding a given threshold of climate change. where = probability of a temperature change exceeding ΔTth PDFs = derivative of P(mi)

  13. Threshold probability SRESA2 - Precipitation - JJA Example: SWWA

  14. Threshold probability – area averaged

  15. PDF’s for sres-A2

  16. Reliability Contributions (%) - Australia REA mean Normalised contributions (in %) to the overall model reliability for each CGCM. Good model performance and convergence leads to higher contribution. If all models were equal, they would contribute 8.3% each.

  17. Summary REA is a useful tool to determine regional climate change from an ensemble of model simulations. Provides a means of producing probabilistic climate change predictions. Significantly lowers RMSD of mean climate change. Obtain ‘skill measure’ of models through reliability analysis. Summary for Australia: Magnitude of ΔT in winter is similar to summer. No significant rainfall changes in DJF. Significant decreases in rainfall in JJA over SWWA, MDB On average, RD consistently better than RB Resulting PDFs vary in shape depending on region (e.g. bi-modal vs uni- modal, width) Same analysis has been repeated over southern Africa (see coming paper for details).

  18. ACCSP • Australian Climate Change Science Programme • Supported by the AGO • CSIRO Marine and Atmospheric Research • BMRC • Launched in October 2007 at GREENHOUSE 2007 • Australian Climate Change Projections Report • 150 pages + • Website access for projections

  19. Any questions? From: Allen and Ingram, 2002, Nature, 419, 224-232.

  20. Overview Milestones