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

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

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

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

Average yearly rainfall in the MDB

Distribution of surface run-off

August 2008

Key Features SEACI (Phase 1) SEACI2 (Phase 2) July, 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

SEACI STAKEHOLDERS SCIENCE MDBA DCC VDSEMCVP CAWCR

1. Understanding past hydroclimate variability and change in SEA

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

Observed trends (in rainfall, mm per 10 yrs)

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

Timbal, 2007 (SEACI)

2.Long-term hydroclimate projections in SEA

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

Climate and runoff projections

GCM IDWeighted failure rate (%) (Table 2) UKMO-HadCM3 0 MIROC3.2(hires) 8 GFDL-CM GFDL-CM MIROC3.2(medres) 25 ECHO-G 33 UKMO-HadGEM1 33 ECHAM5/MPI 38 MRI-CGCM CCSM3 44 CGCM3.1(T63) 50 GISS-AOM 58 INM-CM CGCM3.1(T47) 63 FGOALS-G CSIRO-Mk CNRM-CM3 75 IPSL-CM4 75 BCCR-BCM 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

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

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

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

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

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,….)

Murrumbidge Weather States

Atmospheric Predictors

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

Downscaling can be complicated…

Sample obs PDF for natural varib climate projection rainfall Currently rainfall 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)

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 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

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

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