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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. CMOS Kelowna, 26-29 May 2008.

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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 CMOS Kelowna, 26-29 May 2008

  2. 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

  3. Statistical downscaling Dynamicaldownscaling Problematic: Generalities Downscaling Two main methodologies Better representation physical processes Much less CPU! Statistical relationship: Local fields & Large-scale forcings Resolve dynamics and physics: Numerical model Can be used separately or in combination CMOS Kelowna, 26-29 May 2008 3

  4. 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 CMOS Kelowna, 26-29 May 2008 4

  5. 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 2008 5

  6. Statistical downscaling: Current methodology • For a given day j in which we know the Large-Scale Circulation • Closest weather type Ri • Reconstruct precipitation: regression (distance to weather types) • 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

  7. Statistical downscaling: Validation Precipitation mm/day Period: 1981-2005 Downscaling: MSLP ARPEGE A1B ScenarioRegional Simulation TSO fromCNRM-CM3 model Safran Downscaling DJF 0.6 0.6 7 7 JJA 0.5 5 0.5 5 7

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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 13

  14. Application: France watersheds: Uncertainties Atlantic Ridge Blocking + ~0 20 days NAO+ NAO- + - Models -20 days Correlation Weather Type Occurrence Precipitation -0.5 +0.5 14

  15. 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 15

  16. 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 16

  17. 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 17

  18. 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 18

  19. 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 CMOS Kelowna, 26-29 May 2008 19

  20. 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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