High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation of the ALADIN-Climate model in Morocco

Abstract

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of one of these high-resolution simulations in a 1800 km 2 catchment located in North Morocco. Different approaches are compared to analyze the climate change impacts on the hydrology of this catchment using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984-2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantilematching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30 to −57 % in surface runoff for the mid-term (2041-2062), when for the same period the projections for precipitation are ranging between −15 and −19 % and for temperature between +1.3 and +1.9 • C.

Benefits of explicit urban parameterization in regional climate modeling to study climate and city interactions

“…ALADIN-Climate version 5 (Colin et al 2010) has been widely used in the CORDEX initiative over the Med-CORDEX, Euro-CORDEX and CORDEX Africa domains (Tramblay et al 2013;Jacob et al 2014;Nabat et al 2014Nabat et al , 2015Bador et al 2017;Kjellström et al 2017;Nikulin et al 2018). In this study, we apply for the first time the most recent version of ALADIN-Climate, that is to say its version 6.…”

Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

“…Quantiles are computed considering all the days (rainy days and dry days), thus allowing for a comparison of precipitation quantiles between the different RCM-GCM pairs Schär et al, 2016). However, it should be noted that when precipitation is below 0.1 mm day −1 , the precipitation is set to 0 so that the cumulative precipitation is not affected by overrepresentation of light rainy days in the models (Harader et al, 2015;Tramblay et al, 2013, Paxian et al, 2015.…”

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

“…Bias adjustments of the whole distribution through quantilemapping techniques have been quite popular since it allows for adjusting not only the mean and variance but also any quantile of the variable of interest. Hence, many variants have been proposed (e.g., Déqué, 2007;Michelangeli et al, 2009;Kallache et al, 2011;Tramblay et al, 2013;Vrac et al, 2016) and applied in different studies (e.g., Oettli et al, 2011;Colette et al, 2012;Tisseuil et al, 2012;Vigaud et al, 2013). Nevertheless, usually, those approaches only work in a univariate context, which means that they are designed to independently correct one variable at a time, for one location (e.g., grid cell) at a time.…”