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Climate Change Action Fund (CCAF) Call for proposals on Climate Change; Variability and Extremes A first evaluation of the strength and weaknesses of statistical.

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Presentation on theme: "Climate Change Action Fund (CCAF) Call for proposals on Climate Change; Variability and Extremes A first evaluation of the strength and weaknesses of statistical."— Presentation transcript:

1 Climate Change Action Fund (CCAF) Call for proposals on Climate Change; Variability and Extremes A first evaluation of the strength and weaknesses of statistical downscaling methods for simulating extremes over various regions of eastern Canada Alain Bourque, Ouranos René Roy, Hydro-Québec Guenther Pacher, Hydro-Québec Charles Lin, McGill Van TV Nguyen, McGill André St-Hilaire, INRS-ETE Bernard Bobée, INRS-ETE Jennifer Milton, Environment Canada Jeanna Goldstein, Environment Canada Georges-É. Desrochers, Hydro-Québec Elaine Barrow & Philippe Gachon, CCIS Victoria Slonosky, Ouranos Taha Ouarda, INRS-ETE Tan-Danh Nguyen, McGill Diane Chaumont, Ouranos Marie-Claude Simard, Ouranos Massoud Hessami, INRS-ETE Mohammed Abul Kashem, INRS-ETE

2 Method to simulate climate scenarios: Use of the Empirical Statistical Downscaling Models Tests to evaluate model performance (explained variance, RMSE, RRMSE, skill scores, extremes indexes) Validation Calibration Datasets Datasets: raw, standardized by means and standard deviation (NCEP, GCMs) Validation methods: simple, cross, bootstrap Treatment of «unexplained» part of variance: inflation, randomization

3 Empirical Statistical Downscaling (is based on empirical relationships between local-scale predictands and regional-scale predictors; circulation types; extreme value analysis etc. ) SDSM - regression based downscaling model with stochastic weather generator LARS-WG - stochastic weather generator seasonal definitions the choice of transformation functions ( fourth root, natural log, inverse normal ) the value of the conditional model parameters ( variance inflation, bias correction ) the chosen period of time and its length the local knowledge to define combination of predictors SENSITIVITY TO:

4 Calibration step: SDSM structure. Different variants of the transfer function variables ( multiple regressions, linear and non-linear, combined with stochastic weather generator) (*) predictor variables shall be accurately simulated by GCMs (normalisation reduces systematic biases in the mean and variance of GCMs predictors) Calibration period: 1961-1975 (*) Threshold for Precipitation: 1mm/day Seasonal definition: Monthly

5 1 3 6 5 2 4 Quebec (Canada) Regions of Statistical Downscaling Robustness Study

6 Candidate predictor variables to form optimum predictor set (Fourth root is chosen as transformation function) Free atmosphere parameters, large-scale surface circulation parameters, moisture are recommended for statistical downscaling (Beckmann and Buishand, 2002; Hewitson, 2001; Huth, 1999; Huth et al., 2001; Huth, 2002; Trigo and Palutikof, 1999; Wilby et al., 2001; Wilby and Wigley, 2000).

7 Inflation Montr.-Kuujuar.Moos. Winter7 - 127-15 Spring7 - 1215 Summer12 - 15 7 - 15 Autumn157 - 97 - 12 Inflation parameter = 3 Bias correlation parameter = 0.85 Inflation parameter = 12 Bias correlation parameter = 0.85 Inflation parameter adjustment for SDSM precipitation simulation Montreal-Dorval region 1976-1990 Autumn %tile-%tile plot of SDSM –WG downscaled precipitation vs observations Simple Validation step Average till 90%tile 25 5

8 Uncertainty associated with the use of GCM data Autumn %tile-%tile plots for Montreal-Dorval region 1976-1990 of simulated precipitation vs observations SDSM-Generator: CGCM1 data SDSM-WG: NCEP data CGCM1 GHG+A1 Estimation statistic SDSM WG/Gen GCM inf. 3 inf. 7 inf. 9 inf. 12 inf. 15 bias -3.6 -1.0/-1.3 -0.8/-1.2 -0.8/-1.1 -0.6/-1.0 -0.52/-0.8 RMSE 8.7 6.8/7.8 7.1/8.0 7.2 /8.2 7.5/8.4 7.7/8 RMSE %til. 4.9 6.4 / 5.5 5.0/4.3 4.3/3.5 3.1/2.8 2.2/1.2 Simple Validation step till 90 %-tile

9 Simple Validation step: test of the accuracy of the winter/summer maximum temperature simulated series for 1976-1990. Estimation of uncertainty associated with the use of GCMs Winter / Summer SDSM-WG SDSM-GEN CGCM1 GHG+A1 Bias (deg C) Montreal-Dorval -0.5 / 1.1 3.8 / -0.6 3.5 / -1.9 Kuujjuarapic -0.6 / 0.3 4.8 / -4.3 8.2 / 2.1 Moosonee -0.5 / 1.0 5.5 / -3.1 7.3 / 0.3 Percentiles Bias (deg C) Montreal-Dorval -0.5 / 1.1 3.8 / -0.6 3.4 / -1.9 Kuujjuarapic -0.6 / 0.3 4.8 / -4.3 8.2 / 2.0 Moosonee -0.3 / 1.0 5.5 / -3.1 7.2 / 0.3 Winter / Summer SDSM-WG SDSM-GEN CGCM1 GHG+A1 RMSE (deg C) Montreal-Dorval 2.9 / 2.4 9.8 / 5.9 8.0 / 4.9 Kuujjuarapic 3.7 / 4.5 10.6 / 9.9 11.8 / 7.4 Moosonee 3.5 / 3.7 11.4 / 8.4 11.3 / 6.3 Percentiles RMSE (deg C) Montreal-Dorval 0.8 / 1.2 3.9 / 1.3 6.1 / 2.1 Kuujjuarapic 0.8 / 1.4 5.1 / 4.4 8.8 / 4.1 Moosonee 0.4 / 1.3 5.8 / 3.2 8.4 / 2.6 Spring %tile-%tile plot of SDS models and GCM Tmax vs observations for Montreal region 1976-1990

10 Relevant indices to the field of user demand (derived from downscaled series and compared with observed) Agronomical relevant indices for Spain (Winkler et al., 1997): the Julian date of first and last frost the first occurance of Tmax > 25 deg C the frequency of days with Tmax > 35deg C Water resources relevant indices ( Goldstein and Milton, 2003): Max number of consecutive dry days Max number of consecutive wet days 90th percent. of rainday amounts Greatest 5-day total rainfall 90th Tmax percent Software STARDEX ( STatistical and Regional dynamical Downscaling of Extremes for European regions) Diagnostic Extremes Indices graph:

11 Results, Recommendations and Conclusions: The step of the SDSM validation shall be executed with the different set of predictors and settings parameters with verification by seasons or months SDSM-WG simulates adequately Tmax for all seasons. Local climate (Tmax simulation) is represented with higher accuracy for winter by SDSM-GEN than by CGCM1 GHG+A1 for the north of Quebec Estimation statistic reports less discrepancy values between Tmax downscaled simulated data (SDSM-GEN) and observations in the north region for autumn Precipitation are simulated less accurately for summer and autumn SDS models shall use output of the different GCMs which forced by different type of the greenhouse gases values to treat uncertainties SDSM simulated scenarios shall be treated individually. It is not plausible to average simulated scenarios daily STARDEX software shall be used to define extremes indices - a measure of similarity between observed and simulated time series The first version of the Ouranos SDSM validation tool is created

12 Future Plans Definition of the transfer functions variants for different Quebec regions and analysis of their similarity Use of a stepwise multiple linear regression technique Use of the CGCM2 - SRES «A2», «B2» output Further verification of the ability of the Statistical DownScaling models to catch extremes events Use of STARDEX software to define extremes indices Thank you to CCAF

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