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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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Presentation on theme: "Impact of climate change on France watersheds in 2050 : A comparison of dynamical and multivariate statistical methodologies By : Christian Pagé, CERFACS."— Presentation transcript:

1 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 Ouranos, 20 May 2008

2  Problematic of Downscaling ►Why use a statistical approach?  Methodology ►Statistical Downscaling & Weather Types ►Principles & Hypothesis ►Validation (also Hydrology)  Application ►Impact of climate change on France watersheds ►Uncertainties ►Comparisons against Quantile-Quantile  Summary & Future Outline Ouranos, 20 May

3 How can we evaluate impacts of climate change? Problematic: Generalities Downscaling Meteorological forcings <10km Impact model Precipitations (mm/day) Perturbed climate meteorological fields ~ 250 km Climate model Precipitations (mm/day) Ouranos, 20 May

4 Statistical downscaling Dynamical downscaling 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 Ouranos, 20 May

5 MCGOA CNRM-CM3 GHG, Aerosols F: Calibration Validation Regional Model ARPEGE-VR Bias correction Spatialisation Predictors Boundary Conditions (also Oceanic) Raw Forcings OBS. Impact Model: ISBA-MODCOU Local Forcing variables Statistical Downscaling Dynamical Downscaling Downscaling Methodologies Predictors 5

6 Weather Type: southerly winds Arrows: 850 hPa Wind Lines: MSLP anomalies Precipitation anomalies (%) Dynamical downscaling Observations 8 km Regional Climate Model 60 km Global Climate Model 280 km 6

7 Bias Correction Several Methodologies (Déqué, 2007) ► Perturbation ► Quantile-Quantile Dynamical downscaling Δ Obs. Scenario Climate Change Probability Density Functions 1) Ouranos, 20 May

8 2) Model Present Model Future OBS. Dynamical downscaling Probability Density Functions Ouranos, 20 May Bias Correction Several Methodologies (Déqué, 2007) ► Perturbation ► Quantile-Quantile

9 Dynamical downscaling Corrected Model Present Corrected Model Future 3) Probability Density Functions Ouranos, 20 May Bias Correction Several Methodologies (Déqué, 2007) ► Perturbation ► Quantile-Quantile

10 Statistical downscaling: General methodology R = F (L, β) Local Scale Climate Variable R 10m wind, precipitation, temperature Local Geographical Characteristics topography, land-use, turbulence Global Scale Climate Variable L (predictors) MSLP, geopotential, upper-level wind β such that║R – F(L, β)║ ~ Min F based on Weather Typing Ouranos, 20 May

11 Statistical downscaling: Current methodology Based on: NCEP re-analyses Weather typing ► Mean Sea-Level Pressure Météo-France Mesoscale Meteorological Analysis (SAFRAN) France Coverage 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 Ouranos, 20 May

12 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, D 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 Ouranos, 20 May

13 Statistical downscaling: Current methodology Ouranos, 20 May 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 T index - T NCEP > 2 C Correct precipitation (solid/liquid) and IR radiation Applicable if having long enough observed data time series

14 Statistical downscaling: Validation Is Climate Model simulating correctly Weather Types ? YES Precipitation mm/day Period: Downscaling: MSLP ARPEGE A1B Scenario Regional Simulation SST from CNRM-CM3 model DJF JJA Safran Downscaling

15 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. Ouranos, 20 May

16 Hypothesis 1: predictors has strong link with regional climate Precipitation: 8 weather types Example for 2 winter type MSLP Anomaly NDJFM MSLP Anomaly NDJFM Ratio Pr(reg)/ Pr(moy) Ratio Pr(reg)/ Pr(moy) WT1 WT Data courtesy of Météo-France

17 Hypothesis 1: predictors simulated correctly by model Winter types : WT5 (MSLP, composite anomaly in hPa) NCEP ReanalysesARPEGE GCM-VR Spatial correlation > 0.96 for all weather types 17

18 Hypothesis 2 & 3: Predictors encompass completely climate change signal Statistical relationship still valid for perturbed climate Perfect Model Validation Precipitation mean over France Reconstructed Precipitation amount change in % of current mean (2100_2050) – (2000_1970) A1B Scenario, Spring SPRING Precipitation mm/day 18

19 Statistical downscaling: Validation Precipitation 19

20 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] Period Temperature Data courtesy of Météo-France Ouranos, 20 May

21 Statistical downscaling: Validation: Hydrology Flow Validation Winter Mean OBS NCEP (0.85) SAFRAN (0.97) Annual Cycle OBS NCEP ARPEGE-VR CDF OBS NCEP ARPEGE-VR Jan to Dec 0 to 1 ARIEGE (Foix) LOIRE(Blois) SEINE (Poses) VIENNE (Ingrandes

22 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 Ouranos, 20 May

23 Application: Impact of climate change on France watersheds Multi-Model relative change of Downscaled Precip. (%), 2046/2065 WINTER: DJF Black-circled: at least 85% models has sign agreement Multi-Model relative change of watershed Flows (%), 2046/2065 Dispersion: Spatial Mean σ = 18% Quantifying Uncertainties Ouranos, 20 May 2008

24 Application: Impact of climate change on France watersheds Relative change precipitation 2046/2065 vs 1970/1999 in Winter Statistical downscaling Dynamical Quantile-Quantile downscaling Ouranos, 20 May

25 Application: Impact of climate change on France watersheds Relative change watershed flows 2046/2065 vs 1970/1999 in Winter Statistical downscaling Dynamical Quantile-Quantile downscaling Ouranos, 20 May

26 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 Ouranos, 20 May

27 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 Ouranos, 20 May

28 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) Ouranos, 20 May

29 Merci de votre attention! Christian Pagé, CERFACS Julien Boé, CERFACS Laurent Terray, CERFACS Florence Habets, UMR Sisyphe Éric Martin, CNRM, Météo-France Ouranos, 20 May

30 Application: France watersheds: Snow Cover Water Equivalent (mm) of Snow Cover Pyrenees 2055 Grayed zones: min/max Future Present Aug Jul

31 Application: France watersheds: Uncertainties ~ days -20 days Models Atlantic Ridge NAO+ NAO- Blocking Correlation Weather Type Occurrence Precipitation

32 Application: Impact of climate change on France watersheds Relative change watershed flows 2046/2065 vs 1970/1999 Perturbation method Winter Corr 0.92 Spring Corr 0.38 Summer Corr 0.86 Autumn Corr

33 Application: Impact of climate change on France watersheds Relative change precipitation 2046/2065 vs 1970/1999 in Summer Statistical downscaling Dynamical Quantile-Quantile downscaling Ouranos, 20 May

34 Application: Impact of climate change on France watersheds Relative change watershed flows 2046/2065 vs 1970/1999 in Summer Statistical downscaling Dynamical Quantile-Quantile downscaling Ouranos, 20 May

35 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 ( , 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 !


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