Impact of climate change on France watersheds in 2050 :

A comparison of dynamical and multivariate statistical

methodologiesBy :By :

Christian Pagé, CERFACSChristian Pagé, CERFACSJulien Boé, CERFACSJulien Boé, CERFACS

Laurent Terray, CERFACSLaurent Terray, CERFACSFlorence 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 0.5 577

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, CERFACS

christian.page@cerfacs.fr

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 !