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By : Christian Pagé, CERFACS Julien Boé, CERFACS Laurent Terray, CERFACS

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. Outline.

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By : Christian Pagé, CERFACS Julien Boé, CERFACS Laurent Terray, CERFACS

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  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. Outline • 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 Ouranos, 20 May 2008 2

  3. Climate model Meteorological forcings <10km Precipitations (mm/day) Precipitations (mm/day) Perturbed climate meteorological fields ~ 250 km Downscaling Impact model Problematic: Generalities How can we evaluate impacts of climate change? Ouranos, 20 May 2008 3

  4. Statistical relationship: Local fields & Large-scale forcings Resolve dynamics and physics: Numerical model Statistical downscaling Dynamicaldownscaling Problematic: Generalities Downscaling Two main methodologies Better representation physical processes Much less CPU! Can be used separately or in combination Ouranos, 20 May 2008 4

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

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

  7. Dynamical downscaling Climate Change 1) Bias Correction Several Methodologies (Déqué, 2007) ► Perturbation ►Quantile-Quantile Δ Obs. Scenario Probability Density Functions Ouranos, 20 May 2008 7

  8. Dynamical downscaling 2) Bias Correction Several Methodologies (Déqué, 2007) ► Perturbation ►Quantile-Quantile Model Present Model Future OBS. Probability Density Functions Ouranos, 20 May 2008 8

  9. Dynamical downscaling 3) Bias Correction Several Methodologies (Déqué, 2007) ► Perturbation ►Quantile-Quantile Corrected Model Present Corrected Model Future Probability Density Functions Ouranos, 20 May 2008 9

  10. Statistical downscaling: General methodology Local Geographical Characteristicstopography, land-use, turbulence Global ScaleClimate Variable L (predictors) MSLP, geopotential, upper-level wind Local ScaleClimate Variable R 10m wind, precipitation, temperature R = F (L, β) β such that║R – F(L, β)║ ~ Min F based on Weather Typing Ouranos, 20 May 2008 10

  11. Statistical downscaling: Current methodology • Based on: • NCEP re-analyses • Weather typing • Mean Sea-Level Pressure • 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 SAFRAN 8-km resolution orography Ouranos, 20 May 2008 11

  12. Statistical downscaling: Current methodology • For a given day j in which we know the Large-Scale Circulation • Find closest weather type (daily data) • Euclidian distance over first ten principal components • Select all Ri days of this type • MSLP and Temperature index • Reconstruct precipitation index: using regression of learning period and MSLP of climate model 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. Ouranos, 20 May 2008 12

  13. Statistical downscaling: Current methodology • 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 Ouranos, 20 May 2008 13

  14. 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 Safran Downscaling DJF 0.6 0.6 7 7 JJA 0.5 5 0.5 5 14

  15. Statistical downscaling: Validation: Hypothesis 3 Main Hypothesis • 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. Ouranos, 20 May 2008 15

  16. Hypothesis 1: predictors has strong link with regional climate Precipitation: 8 weather types Example for 2 winter type WT1 WT2 MSLP Anomaly NDJFM MSLP Anomaly NDJFM +16 +16 -16 -16 Data courtesy of Météo-France Ratio Pr(reg)/ Pr(moy) Ratio Pr(reg)/ Pr(moy) 0 0 +3.5 +3.5

  17. Hypothesis 1: predictors simulated correctly by model Winter types 1950-1999: WT5 (MSLP, composite anomaly in hPa) Spatial correlation > 0.96for all weather types NCEP Reanalyses ARPEGE GCM-VR

  18. Hypothesis 2 & 3: Predictors encompass completely climate change signal Statistical relationship still valid for perturbed climate SPRING Perfect Model Validation Precipitation mean over France Precipitationmm/day Reconstructed Precipitation amount change in % of current mean (2100_2050) – (2000_1970) A1B Scenario, Spring -0.35 +0.35 18

  19. Statistical downscaling: Validation Tendencies ΣPr 1951-2000ObservationsvsReconstruction Color: station latitudeSouthNorth Changes of weather type occurrence ►Precipitation Tendencies spatial structures (r=0.92) Precipitation 19

  20. Statistical downscaling: Validation Temperature RATIO Temperature Tendencies [Reconstructed] / [Observed] 1951-2000 Period Data courtesy of Météo-France • Weather Type Occurrence changes cannot explain observed temperature tendencies • ► Mandatory to take into account temperature as a predictor Ouranos, 20 May 2008 20

  21. Statistical downscaling: Validation: Hydrology 1200 150 800 Flow Validation LOIRE(Blois) SEINE (Poses) ARIEGE (Foix) Annual Cycle OBS NCEP ARPEGE-VR 0 0 0 Jan to Dec Jan to Dec Jan to Dec 2500 2500 LOIRE (Blois) 250 SEINE (Poses) ARIEGE (Foix) CDF OBS NCEP ARPEGE-VR 0 0 0 0 to 1 0 to 1 0 to 1 VIENNE (Ingrandes 500 Winter Mean OBS NCEP (0.85) SAFRAN (0.97) 0 1960 2010

  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 2008 22

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

  24. Application: Impact of climate change on France watersheds Relative change precipitation 2046/2065 vs 1970/1999 in Winter -0.6 +0.6 Statistical downscaling Dynamical Quantile-Quantile downscaling Ouranos, 20 May 2008 24

  25. 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 Ouranos, 20 May 2008 25

  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 2008 26

  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 2008 27

  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 2008 28

  29. Merci de votre attention!  Christian Pagé, CERFACS christian.page@cerfacs.fr Julien Boé, CERFACS Laurent Terray, CERFACS Florence Habets, UMR Sisyphe Éric Martin, CNRM, Météo-France Ouranos, 20 May 2008 29

  30. Application: France watersheds: Snow Cover 5 30 Future Present Aug Jul Aug Jul 500 250 Aug Aug Jul Jul • Water Equivalent (mm) of Snow Cover • Pyrenees • 2055 • Grayed zones: min/max 30

  31. Application: France watersheds: Uncertainties Atlantic Ridge Blocking + ~0 20 days NAO+ NAO- + - Models -20 days Correlation Weather Type Occurrence Precipitation -0.5 +0.5 31

  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 +0.5 -0.5 Summer Corr 0.86 Autumn Corr 0.72 32

  33. Application: Impact of climate change on France watersheds Relative change precipitation 2046/2065 vs 1970/1999 in Summer -0.6 +0.6 Statistical downscaling Dynamical Quantile-Quantile downscaling Ouranos, 20 May 2008 33

  34. 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 Ouranos, 20 May 2008 34

  35. 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 (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. Domaine classification MSLP (D1) * 310 stations pour les précipitations 8 à 10 régimes de temps !

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