impact of climate change on france watersheds in 2050 : a comparison of dynamical and multivariate...
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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
Éric Martin, CNRM, Météo-FranceÉric Martin, CNRM, Météo-France
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008
Problematic Problematic of Downscalingof Downscaling Why use a statistical approach?Why use a statistical approach?
MethodologyMethodology Statistical Downscaling & Weather TypesStatistical Downscaling & Weather Types
Principles & HypothesisPrinciples & Hypothesis ValidationValidation
ApplicationApplication Impact of climate change on France watershedsImpact of climate change on France watersheds
ValidationValidation Comparison against quantile-quantile and Comparison against quantile-quantile and
perturbation methodsperturbation methods
Summary & FutureSummary & Future
Outline
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 22
Statistical downscaling
Dynamicaldownscaling
Two main methodologies
Statistical relationship:
Local fields & Large-scale forcings
Resolve dynamics and physics:
Numerical model
Can be used separately or in combination
Downscaling
Problematic: Generalities
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 33
Statistical downscaling: General methodology
R = F (L, β)
Local ScaleClimate Variable R
10m wind, precipitation, temperature
Local Geographical Characteristicstopography, land-use, turbulence
Global ScaleClimate Variable L
(predictors) MSLP, geopotential,
upper-level wind
β such that║R – F(L, β)║ ~ MinF based on Weather Typing
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 44
Statistical downscaling: Current methodology
Based on:• NCEP re-analyses (weather typing)
• Météo-FranceMesoscale Meteorological Analysis (SAFRAN)
• 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
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 55
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. Closest weather type Ri
2. Reconstruct precipitation: regression (distance to weather types)
3. Look for analogs (days) among all Ri days• Closest in terms of precipitation and temperature
(index)• Randomly choose one day
• Applicable as soon as we have long enough observed data series
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 66
Statistical downscaling: Validation
Precipitation mm/day
Period: 1981-2005
Downscaling:MSLP ARPEGE
A1B ScenarioRegional Simulation
TSO fromCNRM-CM3 model
DJF
JJA
Safran Downscaling
0.6 7 0.6 7
0.5 5 0.5 5
77
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
Statistical relationship F still valid for perturbed climate.Cannot be validated or invalidated formally. Also true for physical parameterisations and bias correction.
Predictors encompass completely the climate change signal Need to use Temperature as a predictor
Watersheds flows are correctly reproduced Annual Cycle CDF
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 99
Precipitation change: ARPEGE-VR, in 2050, A1B GHG Scenario(in % of 1970-2000 mean)
Application: Impact of climate change on France watersheds
DJF JJA
Downscaled
Simulated
-0.5 +0.5
1010
Application: Impact of climate change on France watersheds
Relative change watershed flows2046/2065 vs 1970/1999 in Winter
Statistical downscaling
DynamicalQuantile-Quantile
downscalingCMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May
20082008 1111
-0.5 +0.5
Application: Impact of climate change on France watersheds
Relative change watershed flows2046/2065 vs 1970/1999 in Summer
Statistical downscaling
DynamicalQuantile-Quantile
downscalingCMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May
20082008 1212
-0.5 +0.5
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
1313
-0.5 +0.5
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+
+ -
Correlation Weather Type Occurrence Precipitation
-0.5 +0.5
1414
20 days
-20 days Models
Atlantic Ridge
NAO+ NAO-
Blocking
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
1515
Summary - 1
• Statistical downscaling methodology
• Validation is very good
• Hypothesis of stationarity (regression)
• Weather Typing Approach
• Low CPU demand
• Evaluate uncertainties with many scenarios
• Uncertainties of downscaling method are limited
• Those of numerical models are, in general, greater
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 1616
Summary - 2
• Ensemble Mean of Watershed flows
• Decreases moderately in Winter (except Alps and SE Coast)
• 2050 : important decrease in Summer & Autumn
• Robust results, low uncertainty
• Strong increase of Low Water days
• Heavy flows decrease much less than overall mean
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 1717
Down the Road…
• Whole Code Re-Engineering
• Modular approach
• Implement several statistical methodologies
• Configurable
• End-user parameters
• Core parameters
• Web Portal
• Climate-Change Spaghetti to Climate-Change Distribution
• Probability Density Function
• Re-sampled Ensemble Realisations
• M. Dettinger, U.S. Geological Survey (2004)CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May
20082008 1818
Merci de votre attention!
Christian Pagé, CERFACSChristian Pagé, [email protected]
Julien Boé, CERFACSJulien Boé, CERFACSLaurent Terray, CERFACSLaurent Terray, CERFACS
Florence Habets, UMR SisypheFlorence Habets, UMR SisypheÉric Martin, CNRM, Météo-FranceÉric Martin, CNRM, Météo-France
CMOS Kelowna, 26-29 May CMOS Kelowna, 26-29 May 20082008 1919
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.
8 à 10 régimes de temps !