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A BAYESIAN ENSEMBLE METHOD FOR CLIMATE CHANGE PROJECTION

A BAYESIAN ENSEMBLE METHOD FOR CLIMATE CHANGE PROJECTION. Sven Kotlarski ETH Zurich. Buser C., Künsch H.R., Lüthi D., Wild M. and Schär C. (2009): Estimating Uncertainties in Predicting Climate Distributions: A Bayesian Ensemble Method. Submitted to Climate Dynamics.

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A BAYESIAN ENSEMBLE METHOD FOR CLIMATE CHANGE PROJECTION

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  1. A BAYESIAN ENSEMBLE METHOD FOR CLIMATE CHANGE PROJECTION Sven Kotlarski ETH Zurich Buser C., Künsch H.R., Lüthi D., Wild M. and Schär C. (2009):Estimating Uncertainties in Predicting Climate Distributions: A Bayesian Ensemble Method. Submitted to Climate Dynamics. Weber A. (2008):Probabilistic Predictions of the Future Seasonal Precipitation and Temperature in the Alps. Master Thesis at ETH Zurich.

  2. OVERVIEW • Bayesian approach to combine projections of different GCM/RCM combinations into an ensemble projection • Extension of Tebaldi et al. (2005) • Method explicitly accounts for (temporally varying) model biases • Output: PDF for future seasonal mean 2m temperature (DJF, JJA) averaged over European Alps • Bivariate extension (precipitation) • Model data: PRUDENCE RCMs (CTRL and SCEN) • 5 models (CHRM, CLM, HIRHAM, RCAO, ARPEGE) • Observations: CRU OVERVIEW METHOD RESULTS

  3. BASICS OVERVIEW Bias: μi =μ0 + βi (bias of mean) σi =σ0 · bi (bias of variability) Obs (detrended)N(μ0,σ02) f METHOD RESULTS RCM i (detrended)N(μi,σi2) apply βiand bito scenarioPDF T assumption: model results represent "true"climate up to an additive and multiplicative bias f allow for small bias changesΔβiwith ΣiΔβi2small obtained by chosing anappropriate prior distributionof bias changes Δβi similar method for bi implicit weighting! T

  4. Winter temperatures (yearly) CTRL (dito) Observations 1961-1990 (quantiles: ordered and detrended model output) Summer temperatures (yearly) CTRL (dito) IS BIAS INDEPENDENT OF CLIMATIC STATE? OVERVIEW METHOD RESULTS Models systematically too cold or too warm Bias ≈ constant Bias ≠ constant Overestimation oftemperature variability Observations 1961-1990 (quantiles: ordered and detrended model output)

  5. TWO APPROACHES TO COPE WITH BIAS OVERVIEW METHOD RESULTS > Assume that bias is essentially constant (temperature shift correctly reproduced) Assume that relationbetween model andobservations is essentiallyconstant (extrapolation of bias)

  6. RESULTS: SUMMER OVERVIEW observations(CTRL) individual RCMs METHOD multi modelprojection (2071-2100) RESULTS +5.4°C +3.4°C

  7. RESULTS: WINTER OVERVIEW METHOD RESULTS only small difference in expected temperature change (reason: models approx. reproduce interannual variabilityof winter temperature in CTRL climate)

  8. BIVARIATE EXTENSION: TEMPERATURE + PRECIP (Weber, 2008) OVERVIEW METHOD RESULTS THANK YOU! CTRL climate • Summer drying, wetter conditions in winter • Temperature shift larger in summer than in winter SCEN climate RCMs

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