1. Climate Modelling in Australia Michael Manton Bureau of Meteorology Research Centre APN Symposium, 23 March 2004

2. Climate models capture the complexity of the climate system

3. Why is climate modelling important? ● World Climate Research Programme (WCRP) in 1980 recognised the climate model as the tool to – Simulate climate system and its components – Test understanding of climate system – Combine observations in a consistent manner – Simulate past climate variations and changes – Predict future climate variations and changes

4. Australia has a long history of involvement in climate modelling ● Universities – Macquarie – Land surface modelling – Melbourne – Southern hemisphere phenomena – Monash – Detection of climate change – NSW – Ocean modelling – Tasmania – Sea ice modelling ● Government agencies – ANSTO – Isotopes & land surface – CSIRO – Weather and climate – BMRC – Weather and climate

5. There is substantial collaboration between groups ● Cooperative Research Centres – Antarctic Climate & Ecosystems ● Collaborative projects – CSIRO & BMRC with AGO – CSIRO & BMRC with WA Government ● Australian Academy of Science – NCESS workshop ● Australian Research Council – Network on ESM

6. Some Examples ● Model validation of land surface schemes – Macquarie University and BMRC ● Use of isotopic data to validate models – ANSTO ● Coupled modelling for inter-annual prediction – BMRC and CSIRO Marine Research ● Coupled modelling for climate change – CSIRO Atmospheric Research

7. Surface Energy Complexity Does it matter in climate models? ● Macquarie University and BMRC ● AMIP-2 result analysis ● Using CHASM (captures various levels of surface energy balance [SEB] complexity) ● See Pitman et al., GRL, 2004, in press

8. Colour = various modes of CHASM Thick black line = observed Thin black lines = AMIP-2 model results No systematic differences: SEB does not explain AMIP-2 differences Zonal differences in simulated temperature variance Results give confidence in climate model projections of basic values

9. Maximum temperature variance Most complex mode – Includes tiling … Tiling leads to significantly higher maximum temperatures Results imply SEB complexity affects extreme values

10. AMIP2 Analysis ● Prediction of land surface climate evolved over time. ● Not always forwards ● Schemes capture a wide range of behaviours. ● Not all schemes equally good. No canopy ‘SiB’lings others Henderson-Sellers et al. 2003 (Geophys. Res. Lett. 30,1777 )

11. Isotope model studies ● Emerging area for model studies ● Independent validation tool ● ARC Linkage & other funding agencies 1980s  18 O in 1960s & 1980s Weakened signal at Manaus means more water-recycling.Weakened signal at Manaus means more water-recycling. Other indicators say more non-fractionating sources.Other indicators say more non-fractionating sources. H-S, McGuffie & Zhang, J. Clim., 2002, 15, 2664 ANSTO

12. POAMA Predictive Ocean Atmosphere Model for Australia ● Global coupled model GCM seasonal forecasting system ● Joint project between BMRC and CSIRO Marine Research ● Partly funded by the Climate Variability in Agriculture Program (CVAP) ● Run in real-time by Bureau operational section since 1 October 2002 ● Operational products issued by the Bureau National Climate Centre (NCC) ● Experimental products available on the POAMA web site www.bom.gov.au/bmrc/ocean/JAFOOS/POAMA

13. Introduction- POAMA operational system Ocean assimilation - Temp. OI every 3 days + current corrections Daily NWP Atmos. IC Atmos. Model T47 BAM (unified) Ocean Model ACOM2 (~MOM2) Coupler: OASIS Atmospheric observations Ocean observations Real-time ocean assimilation latest ocean/ atmos obs 9-month forecast once per day Ensemble forecasts Observing networkObs/data AssimilationModelForecast/products

14. Skill of SST Predictions Green - model, red - anomaly persistence Hind-casts: one forecast per month, 1987-2001 (180 cases) Anomaly correlation

15. Anomaly Correlation 2 months 6 months 4 months

16. Decay of 2002 El Nino POAMA Real-time forecasts correctly predict decay Prepared P. Reid NCC

17. Sample OLR intra-seasonal forecast from POAMA-1 5-member ensemble starting 10 Dec 2003 MJO Days 1-5 Days 6-10 Days 11-15 Days 16-20 Days 20-30 Days 30-40

18. CSIRO Atmospheric Research CSIRO Mark 3 model 3-dimensional global model 18 levels in atmosphere 31 levels in ocean including sea-ice 6 soil levels, 9 soil types, 13 vegetation types 3 snow levels 180 km between grid-points (100 km in tropics to better simulate El Nino) Data for 100 climate variables computed in 30-minute time-steps for a series of months, years decades or centuries Models adequately simulate observed daily weather and average climate patterns A one-year simulation takes 1 day of computer time

19. Improved simulation of El Nino Southern Oscillation Observed sea surface temperature anomaly CSIRO Mark 2 model CSIRO Mark 3 model

20. CSIRO Mark 3 simulation 1870-2020+ Global surface air temperature change

21. Model hierarchy Global climate model (grid: 180 km by 180 km) Regional climate model (grid: e.g. 70 km by 70 km) Regional climate model (grid: e.g. 14 km by 14 km) Statistical downscaling (local sites: e.g. Perth) PC software, e.g. MAGICC, OzClim ComplexSimple

22. Modelled and Observed Mean Winter and Spring Rainfall, years 1961-1975 CSIRO Cubic Conformal Atmospheric Model – stretched grid

23. OzClim PC software Database includes: Observed and simulated monthly-average data on 25 km grid 10 climate models 6 IPCC emission scenarios 3 climate sensitivities 9 climate variables Functions: Plot maps and global warming curves Save regional average data Run simple impact models Package is used for impact studies and education

24. New components developed and tested separately, then coupled in the model and tested again Land surface Ocean IPCC 2001

25. Future Directions ● Enhanced complexity ● Improved parameterisations ● Improved representation of external forcings ● Improved understanding of predictability ● Analysis of extreme events ● Use of ensembles to represent uncertainty ● Coupling of economic and climate models