1. Current issues with climate change projections Ian Smith May 21 2009

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

3. 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. River Murray Inflows “…an appalling start to 2009…lowest 3 months of water flows on record…rain in the northwest were not enough to offset climate extremes in the southern regions…extreme temperatures.. The correlation between inflows and rainfall is now broken…algal blooms…critical human needs not guaranteed…”

7. Rainfall Percent difference (1997-2006 relative to 1895-2006 ) Rainfall Runoff Understanding observed changes in runoff

8. January to March 2009 rainfall deciles

9. April 2006 to March 2009 36-month rainfall deciles

10. Matching observed trends Role of: GH gases ? Aerosols ? Ozone ? Land cover change ? Natural variability ? Other ? SUMMER AUTUMN

11. Weaker trade winds Expansion of tropics Shift in the Sub-Tropical Ridge Weaker westerlies Regional climate drivers

12. High-Resolution Climate Projections and Impacts

13. Climate projections Ranking GCMs Rankings based on simulations of present day climate 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

14. 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

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

16. Getting from GCM coarse scale results (100 to 200 km**2) to catchment scales. 100km

17. GCMs cannot represent regional scale features that drive local climate

18. Solution 1: Statistical downscaling. TEMPERATURE (T) WINDS (U,V) RELATIVE HUMIDITY (RH) GEOPOTENTIAL HEIGHT (Z) Rainfall = f(T,RH,Z,U,….)

19. Solution 2: Dynamical downscaling.

20. Downscaling can be complicated…

21. 200819002100 Sample obs Natural variability 2050 climate projection rainfall Currently rainfall 200819002100 PDF for natural variability and model greenhouse signal uncertainty We need Integration of historical climate data with projection information