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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 downscaling methods for simulating extremes over various regions of eastern Canada Georges-É. Desrochers, Hydro-Québec

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slide1

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

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

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

slide2

Datasets

Calibration

Validation

Tests to evaluate

model performance

(explained variance,

RMSE,

RRMSE, skill scores,

extremes indexes)

Method to simulate climate scenarios: Use of the Empirical Statistical Downscaling Models

Datasets: raw, standardized by means and standard deviation (NCEP, GCMs)

Validation methods: simple, cross, bootstrap

Treatment of «unexplained» part of variance: inflation, randomization

slide3
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

Empirical Statistical Downscaling(is based on empirical relationships between local-scale predictands and regional-scale predictors; circulation types; extreme value analysis etc. )

SENSITIVITY TO:

slide4

Calibration step: SDSM structure. Different variantsof the transfer function variables (multiple regressions, linear and non-linear, combined with stochastic weather generator)

Seasonal definition: Monthly

(*)

Calibration period: 1961-1975

Threshold for

Precipitation: 1mm/day

(*) predictor variables shall be accurately simulated by GCMs (normalisation

reduces systematic biases in the mean and variance of GCMs predictors)

slide6
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).

slide7

Inflation parameter adjustmentfor SDSM precipitation simulation Montreal-Dorval region 1976-1990

Autumn %tile-%tile plot of SDSM –WG downscaled precipitation vs observations

Simple Validation step

Inflation parameter = 3

Bias correlation parameter = 0.85

25

till 90%tile

Average

Inflation parameter = 12

Bias correlation parameter = 0.85

25

5

slide8

Uncertainty associated with the use of GCM data

Simple Validation step

till 90 %-tile

Autumn %tile-%tile plots for Montreal-Dorval region

1976-1990 of simulated

precipitation vs observations

SDSM-Generator:

CGCM1 data

CGCM1 GHG+A1

SDSM-WG:

NCEP data

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

slide9

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 / -1.9Kuujjuarapic -0.6 / 0.3 4.8 / -4.3 8.2 / 2.1Moosonee -0.5 / 1.0 5.5 / -3.1 7.3 / 0.3Percentiles Bias (deg C)

Montreal-Dorval -0.5 / 1.1 3.8 / -0.6 3.4 / -1.9Kuujjuarapic -0.6 / 0.3 4.8 / -4.3 8.2 / 2.0Moosonee -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 / 4.9Kuujjuarapic 3.7 / 4.5 10.6 / 9.9 11.8 / 7.4Moosonee 3.5 / 3.7 11.4 / 8.4 11.3 / 6.3Percentiles RMSE (deg C)

Montreal-Dorval 0.8 / 1.2 3.9 / 1.3 6.1 / 2.1Kuujjuarapic 0.8 / 1.4 5.1 / 4.4 8.8 / 4.1Moosonee 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

slide10
Relevant indices to the field of user demand (derived from downscaled series and compared with observed)

Software STARDEX

( STatistical and Regional dynamical Downscaling of

Extremes for European regions) Diagnostic Extremes Indices graph:

  • 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

http://www.cru.uea.ac.uk/cru/projects/stardex/

results recommendations and conclusions
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
future plans
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