Impact of climate change on France watersheds in 2050 : A comparison of dynamical and multivariate statistical methodologies By : By : Christian Pagé, CERFACS Christian Pagé, CERFACS Julien Boé, CERFACS Julien Boé, CERFACS Laurent Terray, CERFACS Laurent Terray, CERFACS Florence Habets, UMR Sisyphe Florence Habets, UMR Sisyphe Éric Martin, CNRM, Météo-France Éric Martin, CNRM, Météo-France Ouranos, 20 May 2008 Ouranos, 20 May 2008
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Impact of climate change on France watersheds in 2050 : A comparison of dynamical and multivariate statistical methodologies By : Christian Pagé, CERFACS.
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Impact of climate change on France watersheds in 2050 :
A comparison of dynamical and multivariate statistical
methodologies
By :By :
Christian Pagé, CERFACSChristian Pagé, CERFACSJulien Boé, CERFACSJulien Boé, CERFACS
Laurent Terray, CERFACSLaurent Terray, CERFACS
Florence Habets, UMR SisypheFlorence Habets, UMR Sisyphe
• France Coverage• 1970-2005• 8 km spatial resolution from coherent climatic zones• 7 parameters
• Precipitation (liquid and solid)• Temperature• Wind Module• Infra-Red and Visible Radiation• Specific Humidity
SAFRAN 8-km resolution orography
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Statistical downscaling: Current methodology
Boe J., L. Terray, F. Habets and E. Martin, 2006: A simple statistical-dynamical downscaling scheme based on weather types and conditional resampling J. Geophys. Res., 111, D23106.
For a given day j in which we know the Large-Scale Circulation
1. Find closest weather type (daily data)• Euclidian distance over first ten principal components• Select all Ri days of this type• MSLP and Temperature index
2. Reconstruct precipitation index: using regression of learning period and MSLP of climate model
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Statistical downscaling: Current methodology
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• Look for analogs (15 days) among all Ri days
• Closest in terms of precipitation and temperature index
► Belonging to the same decile
• Randomly choose one day
• Use SAFRAN data for the chosen day
• Apply temperature correction if Tindex - TNCEP > 2 C
• Correct precipitation (solid/liquid) and IR radiation
• Applicable if having long enough observed data time series
Statistical downscaling: Validation
Is Climate Model simulating
correctly Weather Types ? YES
Precipitation mm/day
Period: 1981-2005
Downscaling:MSLP ARPEGE
A1B ScenarioRegional Simulation
SST fromCNRM-CM3 model
DJF
JJA
Safran Downscaling
0.6 7 0.6 7
0.5 5 0.5 5
1414
Statistical downscaling: Validation: Hypothesis
3 Main Hypothesis 1.Predictors
• Strong link with regional climate• Simulated correctly by model
2.Statistical relationship F still valid for perturbed climate.
• Cannot be validated or invalidated formally. Also true for physical parameterisations and bias correction.
3.Predictors encompass completely the climate change signal.
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Hypothesis 1: predictors has strong link with regional climate
Precipitation: 8 weather types
Example for 2 winter type
MSLP AnomalyNDJFM
MSLP AnomalyNDJFM
-16 +16-16 +16
RatioPr(reg)/Pr(moy)
RatioPr(reg)/Pr(moy)
WT1 WT2
0 +3.5 0 +3.51616
Data courtesy of Météo-France
Hypothesis 1: predictors simulated correctly by model
Winter types 1950-1999:
WT5 (MSLP, composite anomaly in hPa)
NCEP Reanalyses ARPEGE GCM-VR
Spatial correlation > 0.96for all weather types
1717
Hypothesis 2 & 3: Predictors encompass completely climate change signalStatistical relationship still valid for perturbed climate
Perfect Model Validation
Precipitation mean over France
Reconstructed
Precipitation amountchange in %of current mean
(2100_2050) – (2000_1970)
A1B Scenario, Spring
-0.35 +0.35
SPRING
Pre
cip
itat
ion
mm
/day
1818
Tendencies ΣPr 1951-2000
Observationsvs
Reconstruction
Color: station latitudeSouthSouth North
Changes of weather type occurrence ► Precipitation Tendencies spatial structures (r=0.92)
Statistical downscaling: Validation
Precipitation
1919
Statistical downscaling: Validation
• Weather Type Occurrence changes cannot explain observed temperature tendencies► Mandatory to take into account temperature as a predictor
RATIO Temperature Tendencies [Reconstructed] / [Observed]
1951-2000 Period
Temperature
Data courtesy of Météo-France
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Statistical downscaling: Validation: Hydrology
Flow Validation
Winter MeanOBSNCEP (0.85)SAFRAN (0.97)
Annual CycleOBSNCEP ARPEGE-VR
CDFOBSNCEP ARPEGE-VR
Jan to Dec Jan to Dec Jan to Dec
0 to 1 0 to 1 0 to 1
ARIEGE (Foix)
ARIEGE (Foix)
LOIRE(Blois)
LOIRE (Blois)
SEINE (Poses)
SEINE (Poses)
VIENNE (Ingrandes
0
2500
000
0 0
1200
2500250
150 800
20101960
500
0
Statistical downscaling: Validation: Summary
Predictors Strong link with regional climate Simulated correctly by model
Predictors encompass completely the climate change signal
Need to use Temperature as a predictor
Watersheds flows are correctly reproduced Annual Cycle Annual Variability Cumulative Density Function
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Application: Impact of climate change on France watersheds
Florence Habets, UMR SisypheFlorence Habets, UMR SisypheÉric Martin, CNRM, Météo-FranceÉric Martin, CNRM, Météo-France
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Application: France watersheds: Snow Cover
• Water Equivalent (mm) of Snow Cover• Pyrenees• 2055• Grayed zones: min/max
FuturePresent
Aug Aug
AugAug
Jul Jul
JulJul
5 30
500250
3030
Application: France watersheds: Uncertainties
Winter Weather Type occurrence changes IPCC (2081/2100 - 1961/2000)
-20
-15
-10
-5
0
5
10
15
20
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5
Models
Num
ber o
f day
s in
win
ter
Atl. Ridge Blocking NAO+ NAO-
~0+
+ -
-0.5 +0.5
3131
20 days
-20 days Models
Atlantic Ridge
NAO+ NAO-
Blocking
Correlation Weather Type Occurrence Precipitation
Application: Impact of climate change on France watersheds
Relative change watershed flows2046/2065 vs 1970/1999 Perturbation method
WinterCorr 0.92
SpringCorr 0.38
SummerCorr 0.86
AutumnCorr 0.72
3232
-0.5 +0.5
Application: Impact of climate change on France watersheds
Relative change precipitation2046/2065 vs 1970/1999 in Summer
Statistical downscaling
DynamicalQuantile-Quantile
downscaling
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-0.6 +0.6
Application: Impact of climate change on France watersheds
Relative change watershed flows2046/2065 vs 1970/1999 in Summer
Statistical downscaling
DynamicalQuantile-Quantile
downscaling
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-0.5 +0.5
Régimes de temps et hydrologie (H1)
Domaine classification MSLP (D1)
* 310 stations pour les précipitations
• Définition de régimes/types de temps discriminants pour les précipitations en France
• Variable de circulation de grande échelle: Pression (MSLP), provenant du projet EMULATE (1850-2000, journalier, 5°x5°), précipitations SQR (Météo-France)
• Classification multi-variée Précipitations & MSLP, pas de temps journalier, espace EOF. On conserve ensuite uniquement la partie MSLP pour définir les types de temps.