Climate change effects on extreme precipitation in Morocco Yves Tramblay, Luc Neppel, Eric Servat HydroSciences Montpellier, UMR 5569 (CNRS-IRD-UM1-UM2), France Wafae Badi, Fatima Driouech Direction de la Météorologie Nationale, Centre National de Recherche Météorologique, Casablanca, Maroc Salah El Adlouni Institut National de Statistique Appliquée, Rabat, Maroc Département de Mathématique et Statistique, Université de Moncton, Canada

Objectives Morocco is often hot by intense rainfall events, causing human losses and economic damages (ex. Ourika, 1995, Rabat, Casablanca, Tanger, 2009) Goals of the study: 1- Evaluate the past trends in the observed extreme precipitation records and the dependences with circulation indexes (NAO, MO) 2- Evaluate the possible future trends with outputs of different Regional Climate Models (RCM) from the ENSEMBLE project (

1- Datasets and methods

Meteorological stations Daily precipitation between 1960 and 2007

Global and Regional Climate Models Higher spatial resolution = better description of orography Responds in a physically consistent way to different forcings Better reproduce extreme events than GCMs (Frei, 2006, Fowler, 2007) Dependent on the boundary conditions imposed by the parent GCM Requires advanced computing resources

Extreme value models κ=0 κ≠0 Extreme Value Distribution (GEV) with 3 parameters (µ,α,κ): Generalized Maximum Likelihood (GML) estimation method [Martins & Stedinger 2000], with a prior distribution for κ:

2- Past trends

Winter (Sep-April) extreme precipitation No trends in the observed extreme precipitation (Mann- Kendall test at the 5% level)

Non-stationary GEV The GML approach has been adapted for the non-stationary context by [El Adlouni et al. 2009], with linear or quadratic dependences of the scale and location parameters The Deviance test, based on the likelihood, allows to compare a stationary model (m 0 ) with a non-stationary model (m 1 ): (D ~ Chi² with ʋ degrees of freedom)

Stationary vs. Non-stationary models Stationary modelsNon-stationary models StationShapeScaleLocationNll ShapeScaleLocation Casablanca μ= NAO w Fes Al Hoceima Ifrance μ = NAO+180.4NAO w ² Larache μ = NAO+56.65NAO w ² Nador Oujda Rabat μ =41.53a-27.66NAO w Tetouan Tanger μ = MO MO w ² Dependance with winter NAO and MO for some stations

Non-stationary quantiles dependant on winter NAO

3- Evaluation of RCM outputs

ENSEMBLE Regional climate models InstituteScenarioDriving GCMModelResolutionAcronym C4I A1BHadCM3Q16RCA325kmC4I_H16 CNRM A1BARPEGEAladin25kmCNR_A DMI A1BARPEGEHIRHAM25kmDMI_A A1BECHAM5-r3DMI-HIRHAM525kmDMI_E A1BBCMDMI-HIRHAM525kmDMI_B ETHZ A1BHadCM3Q0CLM25kmETH_H0 HC A1BHadCM3Q0HadRM3Q025kmHC_H0 A1BHadCM3Q3HadRM3Q325kmHC_H3 A1BHadCM3Q16HadRM3Q1625kmHC_H16 ICTP A1BECHAM5-r3RegCM25kmICT_E KNMI A1BECHAM5-r3RACMO25kmKNM_E MPI A1BECHAM5-r3REMO25kmMPI_E SMHI A1BBCMRCA25kmSMH_B A1BECHAM5-r3RCA25kmSMH_E A1BHadCM3Q3RCA25kmSMH_H3 Daily precipitation between 1950 and 2100

Monthly distribution of precipitation Wrong representation of seasonality for RCM driven by HadCM

Extreme precipitation distribution The distribution of observed extremes is compared with the distribution simulated by the different RCMs Similar distributions, but RCMs underestimate extreme precipitation

Cramér-von Mises test Goodness-of-fit F(x,θ) = fitted F n (x) = empirical Distance between two empirical distributions F n (x) = empirical G m (x) = empirical The statistical significance of the differences can be computed by bootstrap = Quadratic distance between two distributions (specified or not)

Cramér-von Mises (CM) statistic The CM statistic is computed between observed and RCM distributions Can provide weights to combine the different model outputs

5- Future trends

Methodology GEV fit Scaling factor for = Qp 1 / Qo Scaling factor for = Qp 2 / Qo Quantile QoQuantile Qp 1 Quantile Qp 2

Scaling factors on extreme quantiles

Multi-model averaged climate change signal Multi model ensemble: 1) Arithmetic mean of the scaling factors obtained with the different RCMs 2) Weighted mean of the scaling factors obtained with the different RCMs, weights = the inverse of the CM statistic

Conclusions 1.No trend identified during the observation period. Dependences between precipitation extremes with NAO and MO indexes, in particular for the Atlantic stations 2.Great variability in the RCM performances to reproduce the annual cycles and the extreme precipitation distributions. Some models have good skills, with simulated and observed extreme distributions not statistically different 3.The climate change signal in the RCM simulations indicate a decrease in extreme precipitation in particular for the projection period , and a great variability and lower convergence between the models for the projection period Good model convergence towards a decrease for the Atlantic stations. For the Mediterranean stations, the projected changes are difficult to assess due to the great variability. 5.The two weighting schemes tested for model outputs provide similar results

Thanks for your attention Contact: Reference : Tramblay, Y., Badi, W., Driouech, F., El Adlouni, S., Neppel, L., Servat, E., Global and Planetary Change, 2011, submitted