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Probabilistic use of climate change projections

Probabilistic use of climate change projections. Chris Bretherton Department of Atmospheric Science University of Washington. Luo et al. 2005 Climate Research reading.

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Probabilistic use of climate change projections

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  1. Probabilistic use of climate change projections Chris Bretherton Department of Atmospheric Science University of Washington

  2. Luo et al. 2005 Climate Research reading • Decisionmakers in many fields are trying to assess their vulnerability to climate change and what type of adaptation strategies may be needed. • Given emissions/physical modeling uncertainty, it is logical to use probabilistic tools. • However, this has challenges • Lack of standard pdfs for global GHG forcings • Uncertainty of climate sensitivity of models • Regional rainfall change more uncertain than temperature change. • Uncertain response of system of interest to climate change • How to present the information meaningfully • Luo et al. is an example of tackling these challenges.

  3. One challenge: Correlated variables • Markoff and Cullen (2006, Clim Res) PNW

  4. Question: How frequently will SE Australian wheat crop fail to be profitable? • Uncertainties considered and disaggregated: pCO2: 530-786 ppm Climate sens: 0.33-0.57 K/(W m-2) Tloc/Tglob: 0.77-1.08 Rainfall (GS): -7%-3% per 1K global DT (NGS):-7%-11% • Uniform pdfs assumed based on extrema of the available 9 models run on 6 SRES scenarios [Is this a good idea?] • Correlations between uncertainties neglected. • APSIM wheat model assumed (no uncertainty there!)

  5. Presentation of results • Current predicted crop failure rate: 27% • Expected 2080 failure rate: 45% (change based on DT, uncertainty based on rainfall change). [Another wheat model gives the opposite result!]

  6. For discussion • When should we aggregate socioeconomic uncertainty (theoretically under human control) with physical model uncertainty? • Are probabilistic tools effective given the large level of uncertainty we have about climate change? • How should we construct pdfs from model outputs • ‘Surprise’ • weight all models equally? • scenario weighting?

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