Download The impact of diabatic heating structures and model

Impact of climate change on France
watersheds in 2050 :
A comparison of dynamical and
multivariate statistical
methodologies
By :
Christian Pagé, CERFACS
Julien Boé, CERFACS
Laurent Terray, CERFACS
Florence Habets, UMR Sisyphe
Éric Martin, CNRM, Météo-France
CMOS Kelowna, 26-29 May 2008
Outline
 Problematic of Downscaling
 Why use a statistical approach?
 Methodology
 Statistical Downscaling & Weather Types
Principles & Hypothesis
Validation
 Application
 Impact of climate change on France watersheds
Validation
Comparison against quantile-quantile and
perturbation methods
 Summary & Future
CMOS Kelowna, 26-29 May 2008
2
Problematic: Generalities
Downscaling
Two main methodologies
Statistical relationship:
Local fields & Large-scale
forcings
Statistical
downscaling
Resolve dynamics and
physics:
Numerical model
Dynamical
downscaling
Can be used separately or in combination
CMOS Kelowna, 26-29 May 2008
3
Statistical downscaling: General methodology
Global Scale
Climate Variable L
(predictors)
MSLP, geopotential,
upper-level wind
Local Geographical Characteristics
topography, land-use, turbulence
Local Scale
Climate Variable R
10m wind, precipitation,
temperature
R = F (L, β)
β such that║R – F(L, β)║ ~ Min
F based on Weather Typing
CMOS Kelowna, 26-29 May 2008
4
Statistical downscaling: Current methodology
Based on:
• NCEP re-analyses (weather typing)
SAFRAN 8-km resolution orography
• Météo-France
Mesoscale 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
CMOS Kelowna, 26-29 May 2008
5
Statistical downscaling: Current methodology
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
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.
CMOS Kelowna, 26-29 May 2008
6
Statistical downscaling: Validation
Downscaling
Precipitation mm/day Safran
Period: 1981-2005
DJF
Downscaling:
MSLP ARPEGE
A1B Scenario
Regional Simulation
0.6
7
0.6
7
TSO from
CNRM-CM3 model
JJA
0.5
5
0.5
5
7
Statistical downscaling: Validation: Hydrology
Flow Validation
150
Annual Cycle
OBS
NCEP
ARPEGE-VR
0
0
Jan
250
CDF
OBS
NCEP
ARPEGE-VR
800 LOIRE(Blois)
ARIEGE (Foix)
to
Dec
ARIEGE (Foix)
500
0
1960
to
Dec
2500 LOIRE (Blois)
Jan
to
Dec
2500 SEINE (Poses)
0
0
0
t o
1
VIENNE (Ingrandes
SEINE (Poses)
0
Jan
0
1200
0
t o
2010
1
0
t o
Winter Mean
OBS
NCEP
(0.85)
SAFRAN (0.97)
1
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 2008
9
Application: Impact of climate change on France watersheds
Precipitation change: ARPEGE-VR, in 2050, A1B GHG Scenario
(in % of 1970-2000 mean)
Downscaled
-0.5
+0.5
DJF
JJA
Simulated
10
Application: Impact of climate change on France watersheds
Relative change watershed flows
2046/2065 vs 1970/1999 in Winter
+0.5
-0.5
Statistical
downscaling
Dynamical
Quantile-Quantile
downscaling
CMOS Kelowna, 26-29 May 2008
11
Application: Impact of climate change on France watersheds
Relative change watershed flows
2046/2065 vs 1970/1999 in Summer
+0.5
-0.5
Statistical
downscaling
Dynamical
Quantile-Quantile
downscaling
CMOS Kelowna, 26-29 May 2008
12
Application: Impact of climate change on France watersheds
Relative change watershed flows
2046/2065 vs 1970/1999 Perturbation method
Winter
Corr 0.92
-0.5
Summer
Corr 0.86
Spring
Corr 0.38
+0.5
Autumn
Corr 0.72
13
Application: France watersheds: Uncertainties
Atlantic Ridge
+
Blocking
~0
Winter Weather Type occurrence changes IPCC (2081/2100 1961/2000)
20 days
Number of days in winter
20
15
10
5
NAO+
0
+
-5
NAO-
-
-10
-15
-20
Models
Models
-20 days
Atl. Ridge
Blocking
NAO+
NAO-
Correlation
Weather Type Occurrence
Precipitation
-0.5
+0.5
14
Application: France watersheds: Snow Cover
5
30
Future
Present
Aug
Jul
Jul
500
250
Aug
Aug
Jul
Aug
• Water Equivalent (mm) of Snow Cover
• Pyrenees
• 2055
• Grayed zones: min/max
Jul
15
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 2008
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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 2008
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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 2008
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Merci de votre attention! 
Christian Pagé, CERFACS
[email protected]
Julien Boé, CERFACS
Laurent Terray, CERFACS
Florence Habets, UMR Sisyphe
Éric Martin, CNRM, Météo-France
CMOS Kelowna, 26-29 May 2008
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Régimes de temps et hydrologie (H1)
•
•
•
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 (MétéoFrance)
Classification multi-variée Précipitations & MSLP, pas de temps journalier,
espace EOF. On conserve ensuite uniquement la partie MSLP pour définir les
types de temps.
Domaine classification
MSLP (D1)
* 310 stations pour les
précipitations
8 à 10 régimes de temps !