The South East Australia Climate InitiativeACRE 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 Australia14% of Australia

Over 2 million peopleOver 2 million people

1million sq. km1million sq. km

Snapshot of the MDB

•Major river systems

Murray River 2530 km

Darling River 2740 km

Canberra

Sydney

Brisbane

NEW SOUTH WALES

QUEENSLAND

VICTORIA

SOUTHAUSTRALIA

Melbourne

Swan Hill

MilduraMorgan

Menindee

MenindeLakes

LakeVi ctoria

Albury

Forbes

Dubbo

Moree

Charleville

Bourke

MurrayMurrumbidgie

Lachlan

Darling Macquarie

Border

Balonne

Barwon

Warrego

Adelaide

200 km

Murray Bridge

O’Reilly’s

Basin characteristics

Length 3,370km

Basin size 1,050,116 km²

Population 2 million

Population density 2 people/km²

Key economic activity agriculture, tourism, mining, manufacturing

Key issues risks to shared water resources, overallocation

Average yearly rainfall in the MDB

Distribution of surface run-off

1.0%

1.7%

0.4%

20.3%

23.3%

1.9%

0%6.1%

0.3%

21.1%

10.6%

13.3%

1.0%

1.7%

0.4%

20.3%

23.3%

1.9%

0%6.1%

0.3%

21.1%

10.6%

13.3%

http://www.mdbc.gov.au

August 2008

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

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

SE

AC

I

STA

KE

HO

LDE

RS

SC

IEN

CE

MDBA

DCC

VDSE

MCVP

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 (1997-2006 relative to 1895-2006)

Rainfall Runoff

1997–2006 rainfall and runoff

Understanding observed changes in runoff

Climate and runoff projections

GCM ID Weighted 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

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.

200k

m

Spatial problem:GCMs cannot represent regional scale

features that drive local climate

Sea level

Spatial problem:

Statistical downscaling

T

U

RH

Z

Rainfall=f2(T,RH,Z,U,….)

c.f.Antonio Cofino

Rainfall=f1(T,RH,Z,U,….)

Murrumbidge Weather States

Atmospheric Predictors

Spatial problem:Dynamical downscaling?

c.f.Antonio Cofino

Still only ~ 10km

Expensive

200k

m

Downscaling can be complicated…

20081900 2100

Sample obs

PDF for natural varib.

2050 climate projection

rain

fall

Currently

rain

fall

20081900 2100

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 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

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

P

Q

P

QP = median P = median

Qt – QQt – Q

Pt – PPt – P

RAINFALL

RUNOFF

ET

ET

Estimation of impact on runoff

< 22 – 2.52.5 – 3> 3

< 22 – 2.52.5 – 3> 3

Hydrologic sensitivity to runoff

Runoff projections

Water Availability