Climate change effects on extreme
precipitation in Morocco
Yves Tramblay, Luc Neppel, Eric Servat
HydroSciences Montpellier, UMR 5569 (CNRS-IRD-UM1-UM2), France
Salah El Adlouni
Institut National de Statistique Appliquée, Rabat, Maroc
Département de Mathématique et Statistique, Université de Moncton, Canada
Wafae Badi, Fatima Driouech
Direction de la Météorologie Nationale, Centre National de Recherche Météorologique, Casablanca, Maroc
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 (www.ensembles-eu.org)
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
Extreme Value Distribution (GEV) with 3
parameters (µ,α,κ):
1/ 
  
 
F ( x)  exp 1  ( x   )  
 
  
κ≠0

 x 
F ( x)  exp exp  (
)




κ=0



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 (MannKendall 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 (m0) with a non-stationary model (m1):

 (D ~ Chi² with ʋ degrees of freedom)
*
*
D

2
l
(
M
)

l
(
M
)
n 1
n
0
Stationary vs. Non-stationary models
Stationary models
Non-stationary models
Station
Shape
Scale
Location
Nll
Nll
Shape
Scale
Location
Casablanca
-0.06
10.72
31.16
186.80
182.94
-0.09
9.12
μ=33.33-24.79NAOw
Fes
-0.08
8.59
32.80
177.18
-
-
-
Al Hoceima
-0.21
12.21
30.45
178.30
-
-
-
Ifrance
-0.15
18.33
55.69
215.52
211.55
-0.09
16.90
μ =51.857.61NAO+180.4NAOw²
Larache
-0.07
12.97
47.11
187.82
184.51
-0.10
11.70
μ =45.99-22.3NAO+56.65NAOw²
Nador
-0.13
17.61
34.96
140.58
-
-
-
Oujda
-0.17
11.51
26.23
194.86
-
-
-
Rabat
-0.11
12.96
39.09
197.07
193.11
-0.13
11.30
Tetouan
-0.14
17.26
49.44
176.12
-
-
-
Tanger
-0.10
14.04
49.42
200.86
197.72
-0.16
12.40
μ =41.53a-27.66NAOw
μ =55.49+101.6MO+234.33MOw²
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
Institute
Scenario
Driving GCM
Model
Resolution
Acronym
C4I
A1B
HadCM3Q16
RCA3
25km
C4I_H16
CNRM
A1B
ARPEGE
Aladin
25km
CNR_A
A1B
ARPEGE
HIRHAM
25km
DMI_A
A1B
ECHAM5-r3
DMI-HIRHAM5
25km
DMI_E
A1B
BCM
DMI-HIRHAM5
25km
DMI_B
A1B
HadCM3Q0
CLM
25km
ETH_H0
A1B
HadCM3Q0
HadRM3Q0
25km
HC_H0
A1B
HadCM3Q3
HadRM3Q3
25km
HC_H3
A1B
HadCM3Q16
HadRM3Q16
25km
HC_H16
ICTP
A1B
ECHAM5-r3
RegCM
25km
ICT_E
KNMI
A1B
ECHAM5-r3
RACMO
25km
KNM_E
MPI
A1B
ECHAM5-r3
REMO
25km
MPI_E
A1B
BCM
RCA
25km
SMH_B
A1B
ECHAM5-r3
RCA
25km
SMH_E
A1B
HadCM3Q3
RCA
25km
SMH_H3
DMI
ETHZ
HC
SMHI
Daily precipitation between 1950 and 2100
Monthly distribution of precipitation 1961-2007
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
= Quadratic distance between two distributions (specified or not)
Goodness-of-fit
² 

 F ( x)  F ( x, )² dF ( x)
n

Distance
between two
empirical
distributions

D  NM /( N  M )  Fn ( x)  Gm ( x)² dH n  m ( x)

F(x,θ) = fitted
Fn(x) = empirical
Fn(x) = empirical
Gm(x) = empirical
The statistical significance of the differences can be computed by bootstrap
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
1961-2007
2020-2050
2070-2099
GEV fit
GEV fit
GEV fit
Quantile Qo
Quantile Qp1
Quantile Qp2
Scaling factor for 2020-2050 = Qp1 / Qo
Scaling factor for 2070-2099 = Qp2 / Qo
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 2070-2099, and a great variability and lower convergence
between the models for the projection period 2020-2050
4. 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: ytramblay@gmail.com
Reference :
Tramblay, Y., Badi, W., Driouech, F., El Adlouni, S., Neppel, L., Servat, E., Global and Planetary
Change, 2011, submitted