Climatology, annual cycle and interannual variability of precipitation and temperature in CORDEX simulations over West Africa

Abstract

We examine the ability of an ensemble of 10 Regional Climate Models (RCMs), driven by ERA-Interim reanalysis, in skillfully reproducing key features of present-day precipitation and temperature (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) over West Africa. We explore a wide range of time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. We find that the RCMs show acceptable performance in simulating the spatial distribution of the main precipitation and temperature features. The occurrence of the West African Monsoon jump, the intensification and northward shift of the Saharan Heat Low (SHL), during the course of the year, are shown to be realistic in most RCMs. They also capture the mean annual cycle of precipitation and temperature, including, single and double-peaked rainy seasons, in terms of timing and amplitude over the homogeneous sub-regions. However, we should emphasize that the RCMs exhibit some biases, which vary considerably in both magnitude and spatial extent from model to model. The interannual variability of seasonal anomalies is best reproduced in temperature rather than precipitation. The ensemble mean considerably improves the skill of most of the individual RCMs. This highlights the importance of performing multi-model assessment in properly estimating the response of the West African climate to global warming at seasonal, annual and interannual time scales.

Simulation of the West African monsoon onset using the HadGEM3-RA regional climate model

“…Diallo et al 2012Diallo et al , 2013aHernán-dez-Díaz et al 2013;Nikulin et al 2012;Gbobaniyi et al 2013;Sylla et al 2013b). …”

West African monsoon intraseasonal activity and its daily precipitation indices in regional climate models: diagnostics and challenges

“…Even though this study showed the high potential of RegCM3 to handle very subtle interactions between the AEWs and the AEJ, it was quite limited in terms of quantitative evidence, such as the extent to which this synoptic-scale activity determines the monsoon precipitation. The recent studies by Gbobaniyi et al (2013), Diallo et al (2014) and Mounkaila et al (2015) broadly show a good representation of the WAM onset by RCMs participating in COR-DEX. Mariotti et al (2014) have shown better performance of RCMs in comparison with GCMs in reproducing the AEW activity, but their analysis is qualitative.…”

“…Vellinga et al 2013;Fitzpatrick et al 2015), two main features can be used for detecting the onset: (1) the large-scale onset that focuses on the shift (or jump) of the sustained rainy band from its Guinean position (~5°N) to a Sudanese position (~10°N), (2) the local onsets are related to more agricultural purposes and represent the effective start of sustained rainfall at a specific location. The large-scale precipitation onset has been quite widely analyzed, in particular in CORDEX simulations (among others, Gbobaniyi et al 2013;Nikulin et al 2012;Diaconescu et al 2015) and most RCMs tend to improve the onset occurrence in comparison with ERAI and GCMs. The local onset is more complicated to reproduce since precipitation structures are more affected by smaller scale processes in this case (Marteau et al 2011).…”

“…From these simulations, Nikulin et al (2012) and Gbobaniyi et al (2013) have shown, for time scales ranging from interannual to seasonal, an added value of RCMs when they are driven by ERA-Interim reanalysis (ERAI; Dee et al 2011) rather than by coarse-mesh driving boundary conditions.…”

Simulating the Impacts of Tree, C3, and C4 Plant Functional Types on the Future Climate of West Africa

“…The magnitude of temperature and precipitation biases in all the seasons was not horizontally homogeneous, but appeared to be more intense over areas with complex terrains (e.g., Guinean Highlands, Cameroon Mountains, and Jos plateau in Nigeria). Previous regional climate model simulations over West Africa reported similar biases [18,30,[60][61][62][63][64]. The model biases were associated with different factors, such as the absence of sufficient observing stations in the mountainous region, the influence of land/sea boundary conditions, physics configuration of the model, inherited biases from the driving global circulation models (GCMs), or the choice of cumulus convective parameterization [39,[65][66][67].…”