Climate Change Action Fund (CCAF)Call for proposals on “Climate Change; Variability and Extremes”

 A first evaluation of the strength and weaknesses A first evaluation of the strength and weaknesses

of statistical downscaling methods for simulating extremes of statistical downscaling methods for simulating extremes over various regions of eastern Canadaover various regions of eastern Canada

Alain Bourque, OuranosRené Roy, Hydro-QuébecGuenther Pacher, Hydro-QuébecCharles Lin, McGill Van TV Nguyen, McGillAndré St-Hilaire, INRS-ETEBernard Bobée, INRS-ETEJennifer Milton, Environment CanadaJeanna Goldstein, Environment Canada

Georges-É. Desrochers, Hydro-QuébecElaine Barrow & Philippe Gachon, CCISVictoria Slonosky, OuranosTaha Ouarda, INRS-ETETan-Danh Nguyen, McGillDiane Chaumont, OuranosMarie-Claude Simard, OuranosMassoud Hessami, INRS-ETEMohammed Abul Kashem, INRS-ETE

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

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:

Calibration step: SDSM structure. Different variants of

the transfer function variables (multiple regressions, linear and non-linear, combined with stochastic weather generator)

A d ju s tm en tof th e p red ic tor

variab les*

n on e

u n con d ition al

n on e fou rth root n atu ral log in verse n orm al

con d ition al

F u n c tion formor

m od el typ e

C h oiceof

th e p red ic torsvariab les

A ch oiceof

th resh old

L en th of the ca lib ration seriesan d d ata tran sform ation

S eason al d efin it ion :m on th lyseason al

an n u al

(*) 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

1

3 6

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4

Quebec (Canada) Regions of Statistical Downscaling Robustness Study

Candidate predictor variables to form optimum predictor set (Fourth root is chosen as transformation function)

Precipitation Combinations of predictors Montreal-Dorval Kuujuarapik Inukjuak Moosonee

(2) Zonal velocity component, (1) meridional velocity component, (2) meridional velocity component at 500hPa, (4) Geopotential height at 500 hPa, (4) specific humidity at 500 hPa, (4) specific humidity at 850 hPa, (2) specific humidity, (1) vorticity, (3) temperature

Tmean, Tmax, Tmin

Combinations of predictors

Montreal-Dorval Kuujuarapik Inukjuak Moosonee

(4) Mean sea level pressure, (3) Zonal velocity component, (4) Geopotential height at 500 hPa, (4) Geopotential height at 850 hPa, (4) specific humidity at 850 hPa, (1) specific humidity

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).

Inflation Montr.- Kuujuar. Moos.

Winter 7 - 12 7-15 7 - 15

Spring 7 - 12 15 15

Summer 12 - 15 12 - 15 7 - 15

Autumn 15 7 - 9 7 - 12

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ay

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1 1.5 2 2.5 3 3.5 4 4.5 5mm/day

mm

/day

Inflation parameter = 3Bias correlation parameter = 0.85

Inflation parameter = 12Bias correlation parameter = 0.85

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mm/day

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Obs

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Inflation parameter adjustmentfor SDSM precipitation simulation Montreal-Dorval region 1976-1990Autumn %tile-%tile plot of SDSM –WG downscaled precipitation vs observations

Simple Validation step

Average till 90%tile

255

25

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/day

Obs

CGCM1 GHG+A1

jjjj

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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

-15

-10

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-15 -10 -5 0 5 10 15 20 25 30 35

deg C

de

g C

Obs

SDSM WG

SDSM (CGCM1 GHG+A1)

LARS-WG (CGCM1 GHG+A1)

CGCM1 GHG+A1

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+A1Bias (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+A1RMSE (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

Relevant indices to the field of user demand (derived from downscaled series and

compared with observed) Agronomical relevant indices forSpain (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:

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

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

• 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