Title: Dynamical downscaling of CMIP5 1 Global Circulation Models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value.
Authors: DOSIO AlessandroPANITZ Hans-JuergenSCHUBERT-FRISIUS MartinaLUETHI Daniel
Citation: CLIMATE DYNAMICS p. 25
Publisher: SPRINGER
Publication Year: 2014
JRC N°: JRC90019
ISSN: 0930-7575
URI: http://link.springer.com/article/10.1007%2Fs00382-014-2262-x#
http://publications.jrc.ec.europa.eu/repository/handle/JRC90019
DOI: 10.1007/s00382-014-2262-x
Type: Articles in periodicals and books
Abstract: In this work we present the results of the application 8 of the Consor- tium for Small-scale Modeling (COSMO) Regional Climate Model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX). An ensemble of climate change projections has been created by downscaling the simulations of four Global ClimateModels (GCM), namely:MPI-ESM-LR, HadGEM2- ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa Monsoon (WAM) area. By performing a Singular Spectrum Analysis (SSA) it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the Probability Distribution Function (PDF) of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.
JRC Directorate:Sustainable Resources

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