Transcript Constructing scenarios of extremes and climate variability in Quebec

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
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
Datasets
Calibration
Validation
Tests to evaluate
model performance
(explained variance,
RMSE,
RRMSE, skill scores,
extremes indexes)
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
SENSITIVITY TO:
• 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
Calibration step: SDSM structure. Different variants of
the transfer function variables (multiple regressions, linear and
non-linear, combined with stochastic weather generator)
Seasonal definition:
monthly
seasonal
annual
(*)
Seasonal definition: Monthly
Adjustment
of the predictor
variables*
Lenth of the calibration series
and data transformation
Function form
or
model type
unconditional
none
Choice
of
the predictors
variables
Calibration period: 1961-1975
A choice
of
threshold
Threshold for
Precipitation: 1mm/day
conditional
none
fourth root
natural log
inverse normal
(*) predictor variables shall be accurately simulated by GCMs (normalisation
reduces systematic biases in the mean and variance of GCMs predictors)
Quebec (Canada) Regions of Statistical Downscaling Robustness Study
1
2
4
3
6
5
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 parameter adjustment
for SDSM precipitation simulation
Montreal-Dorval region 1976-1990
Inflation
Autumn %tile-%tile plot of SDSM –WG
downscaled precipitation vs observations
5
4 .5
4
mm/day
m m / d ay
3 .5
3
2 .5
2
Inflation parameter = 3
Bias correlation parameter = 0.85
1 .5
25
23
21
19
17
15
13
11
9
7
5
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
Simple Validation step
25
1
1
1 .5
2
2 .5
3
3 .5
4
4 .5
5 23
25
m m / da y
5
7
9
11
13
15
17
19
21
23
25
mm/day
till 90%tile
21
Average
4.5
5
Obs
19
4
17
mm/day
mm/day
3.5
15
3
13
2.5
11
Inflation parameter = 12
Bias correlation parameter = 0.85
2
1.5
1
9
7
5
5
1
1.5
2
2.5
3
m m /day
3.5
4
4.5
5
7
5
9
11
13
15
mm/day
17
19
21
23
25
25
Uncertainty associated with the use of GCM data
25
Simple Validation step
23
Obs
CGCM1 GHG+A1
21
till 90 %-tile
19
mm/day
17
15
SDSM-Generator:
13
CGCM1 data
Autumn %tile-%tile plots for
Montreal-Dorval region
1976-1990 of simulated
precipitation vs observations
11
9
CGCM1 GHG+A1
7
5
25
jjjj
5
10
15
23
20
25
Obs
m m /day
21
mm/day
19
17
SDSM-WG:
15
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
13
11
9
RMSE
8.7 6.8/7.8 7.1/8.0
7.2 /8.2
7.5/8.4
7.7/8
7
RMSE %til. 4.9 6.4 / 5.5 5.0/4.3
5
5
7
9
11
13
15
mm/day
17
19
21
23
25
4.3/3.5
3.1/2.8
2 .2/1.2
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
40
Obs
SDSM WG
SDSM (CGCM1 GHG+A1)
LARS-WG (CGCM1 GHG+A1)
CGCM1 GHG+A1
35
30
25
deg C
20
15
10
5
0
-15
-10
-5
0
-5
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
5
10
15
20
25
30
35
deg C
-10
-15
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 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/
Software STARDEX
( STatistical and Regional
dynamical Downscaling of
Extremes for European
regions) Diagnostic
Extremes Indices graph:
Results, Recommendations and
Conclusions:
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•
•
•
•
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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