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, May 2008

Problematic of Downscaling Problematic of Downscaling Why use a statistical approach? Why use a statistical approach? Methodology Methodology Statistical Downscaling & Weather Types Statistical Downscaling & Weather Types Principles & Hypothesis Principles & Hypothesis Validation Validation Application Application Impact of climate change on France watersheds Impact of climate change on France watersheds Validation Validation Comparison against quantile-quantile and perturbation methods Comparison against quantile-quantile and perturbation methods Summary & Future Summary & Future Outline CMOS Kelowna, May

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 CMOS Kelowna, May

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 thatR – F(L, β) ~ Min F based on Weather Typing CMOS Kelowna, May

Statistical downscaling: Current methodology Based on: NCEP re-analyses (weather typing) 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 CMOS Kelowna, May

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. 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, May

Statistical downscaling: Validation Precipitation mm/day Period: Downscaling: MSLP ARPEGE A1B Scenario Regional Simulation TSO from CNRM-CM3 model DJF JJA Safran Downscaling

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

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, May

Precipitation change: ARPEGE-VR, in 2050, A1B GHG Scenario (in % of mean) Application: Impact of climate change on France watersheds DJFJJA Downscaled Simulated

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 CMOS Kelowna, May

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 CMOS Kelowna, May

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

Application: France watersheds: Uncertainties ~ Correlation Weather Type Occurrence Precipitation 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 Future Present Aug Jul

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, May

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, May

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, May

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

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 !