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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. 14% of Australia.

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  • Acknowledgements:

  • SEACI colleagues, Wendy Craik, Bryson Bates, QCCCE colleagues


  • The murray darling basin

    14% of Australia

    Over 2 million people

    The Murray-Darling Basin

    1million sq. km


    Snapshot of the mdb
    Snapshot of the MDB

    O’Reilly’s

    • Major river systems

      • Murray River 2530 km

      • Darling River 2740 km





    August 2008

    http://www.mdbc.gov.au


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


    Key features
    Key Featureshttp://www.mdbc.gov.au/__data/page/1366/Drought_Update_Issue_14_-_July_2008.pdf

    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

    • Seasonal hydroclimate prediction in SEA


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

    VDSE

    MCVP

    DCC

    STAKEHOLDERS

    SEACI

    CAWCR

    SCIENCE



    Detection and attribution observed trends
    Detection and attribution: SEAObserved trends

    Role of:

    GH gases ?

    Aerosols ?

    Ozone ?

    Land cover change ?

    Natural variability ?

    Other ?


    Observed trends in rainfall mm per 10 yrs
    Observed trends SEA(in rainfall, mm per 10 yrs)


    Probable causes sub tropical ridge intensity
    Probable causes: sub-tropical ridge intensity SEA

    Adapted from Timbal (2007)




    Understanding observed changes in runoff SEA

    1997–2006 rainfall and runoff

    Percent difference (1997-2006 relative to 1895-2006)

    Rainfall

    Runoff

    Rainfall



    Assessment of GCMs SEA

    • 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


    Downscaling
    Downscaling SEA

    Relating local-scaleweather & climate to large-scaleatmospheric variables(modelled or observed)


    Downscaling applications
    Downscaling Applications SEA

    • Investigations of interannual and multidecadalclimate 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
    Spatial problem: SEAGetting from GCM coarse scale results (100kmx100km)to catchment scales.

    200km


    Spatial problem gcms cannot represent regional scale features that drive local climate
    Spatial problem: SEAGCMs cannot represent regional scale features that drive local climate

    Sea level


    Spatial problem statistical downscaling
    Spatial problem SEA:Statistical downscaling

    c.f.Antonio Cofino

    U

    Z

    RH

    Rainfall=f2(T,RH,Z,U,….)

    T

    Rainfall=f1(T,RH,Z,U,….)




    Spatial problem dynamical downscaling
    Spatial problem: SEADynamical downscaling?

    c.f.Antonio Cofino

    Still only ~ 10km

    Expensive

    200km



    Temporal problem integration of historical climate data with projection information

    We need SEA

    PDF for natural varib. and model greenhouse signal uncertainty

    rainfall

    1900

    2008

    2100

    Temporal problem:Integration of historical climate data with projection information

    Currently

    2050 climate projection

    PDF for natural varib.

    Sample obs

    rainfall

    1900

    2008

    2100

    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
    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)

    Warmer and drier

    B

    A


    Summary capture climate change signals

    • 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


    RAINFALL capture climate change signals

    ET

    ET

    RUNOFF

    Water Availability

    Hydrologic sensitivity to runoff

    Climate change projections

    < 2

    2 – 2.5

    2.5 – 3

    > 3

    Estimation of impact on runoff

    Runoff projections

    P

    Q

    Qt – Q

    eP = median

    Pt – P


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