1. The South East Australia Climate Initiative ACRE workshop, April2, 2009 Brief description Summary of themes Issues Spatial problem (downscaling) Temporal problem (synthetic time series) Acknowledgements: SEACI colleagues, Wendy Craik, Bryson Bates, QCCCE colleagues

2. The Murray-Darling Basin 14% of Australia Over 2 million people 1million sq. km

3. Snapshot of the MDB Major river systems  Murray River 2530 km  Darling River 2740 km O’Reilly’s

4. Basin characteristics Length3,370km Basin size1,050,116 km² Population2 million Population density2 people/km² Key economic activityagriculture, tourism, mining, manufacturing Key issuesrisks to shared water resources, overallocation

5. Average yearly rainfall in the MDB

6. Distribution of surface run-off

7. http://www.mdbc.gov.au August 2008

8. http://www.mdbc.gov.au/__data/page/1366/Drought_Update_Issue_14_-_July_2008.pdf

9. Key Features SEACI (Phase 1) 2006-2008 SEACI2 (Phase 2) July, 2009 - June, 2012 Further extension (2 years) subject to review Investigating the causes and impacts of climate change and variability across south eastern Australia, and developing improved short-term predictions for hydrological and agricultural applications Research themes: 1. Understanding past hydroclimate variability and change in SEA 2.Long-term hydroclimate projections in SEA 3.Seasonal hydroclimate prediction in SEA

10. SEACI STAKEHOLDERS SCIENCE MDBA DCC VDSEMCVP CAWCR

11. 1. Understanding past hydroclimate variability and change in SEA

12. Detection and attribution: Observed trends Role of: GH gases ? Aerosols ? Ozone ? Land cover change ? Natural variability ? Other ?

13. Observed trends (in rainfall, mm per 10 yrs)

14. Probable causes: sub-tropical ridge intensity Adapted from Timbal (2007)

15. Timbal, 2007 (SEACI)

16. 2.Long-term hydroclimate projections in SEA

17. Rainfall Percent difference (1997-2006 relative to 1895-2006) Rainfall Runoff 1997–2006 rainfall and runoff Understanding observed changes in runoff

18. Climate and runoff projections

19. GCM IDWeighted failure rate (%) (Table 2) UKMO-HadCM3 0 MIROC3.2(hires) 8 GFDL-CM2.1 13 GFDL-CM2.0 20 MIROC3.2(medres) 25 ECHO-G 33 UKMO-HadGEM1 33 ECHAM5/MPI 38 MRI-CGCM2.3.2 40 CCSM3 44 CGCM3.1(T63) 50 GISS-AOM 58 INM-CM3.0 59 CGCM3.1(T47) 63 FGOALS-G1.0 63 CSIRO-Mk3.0 73 CNRM-CM3 75 IPSL-CM4 75 BCCR-BCM2.0 88 GISS-ER 88 PCM 89 GISS-EH 100 Ranking of (AR4) GCM performance to improve of regional climate change projections and impacts. There are models which consistently perform relatively well, and also models which consistently underperform Provides a basis for better weighting, if not excluding, some model results when forming projections There is (but not always) evidence of clustering in the projected changes from better performing models Assessment of GCMs

20. Downscaling Relating local-scale weather & climate to large-scale atmospheric variables (modelled or observed)

21. Downscaling Applications Investigations of interannual and multidecadal climate variability at regional scales Climate change scenarios at local and/or regional scales Detection & attribution of climate change at regional scales Seasonal prediction at local &/or regional scales

22. Spatial problem: Getting from GCM coarse scale results (100kmx100km) to catchment scales. 200km

23. Spatial problem: GCMs cannot represent regional scale features that drive local climate Sea level

24. Spatial problem : Statistical downscaling T U RH Z Rainfall=f 2 (T,RH,Z,U,….) c.f.Antonio Cofino Rainfall=f 1 (T,RH,Z,U,….)

25. Murrumbidge Weather States

26. Atmospheric Predictors

27. Spatial problem: Dynamical downscaling? c.f.Antonio Cofino Still only ~ 10km Expensive 200km

28. Downscaling can be complicated…

29. 200819002100 Sample obs PDF for natural varib. 2050 climate projection rainfall Currently rainfall 200819002100 PDF for natural varib. and model greenhouse signal uncertainty We need Temporal problem: Integration of historical climate data with projection information Current (2008) climate and future climate can be estimated the same way: model signal plus natural variability Climate envelope will be modified as time goes by based on model improvement and evaluation, and assimilation of the observed trend by some means (Penny Whetton)

30. One option for generating synthetic weather series which capture climate change signals Assume the climate at site A is projected to resemble the present-day climate at site B by 2100. A feasible synthetic weather series may be Past to the present: site A as observed 2100: Site B as observed Present to 2100: Weighted between A and B (preserves correlation between variables) A B Warmer and drier

31. SEACI is tackling the following problems: Better understanding of the drivers of observed climate change over SEA Improving projections of climate change Improving estimates of impacts on runoff, water storages which can inform medium term management practice and long term policy Developing seasonal prediction products which can inform agriculture Quality reanalysis products are essential for: Assessing GCMs Interpreting the outputs from climate models via statistical downscaling Summary

32. Climate change projections PQPQ PQPQ  P = median Q t – Q P t – P RAINFALL RUNOFF ET Estimation of impact on runoff < 2 2 – 2.5 2.5 – 3 > 3 < 2 2 – 2.5 2.5 – 3 > 3 Hydrologic sensitivity to runoff Runoff projections Water Availability