Abstract
We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models’ agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result.
Highlights
Africa, the second-largest continent on Earth and with the fastest population growth, is most vulnerable to weather and climate variability (Niang et al 2014)
Previous works (e.g. Dosio et al 2015; Fotso-Nguemo et al 2017) showed that when analyzing precipitation characteristics over Africa, the effect of the errors inherited through the boundary conditions is small compared to the structural bias of the downscaling Regional Climate Models (RCMs), as local effects and model parameterization are the main drivers of the simulated precipitation
This is confirmed by our analysis that shows that the bias of the RCM is scarcely affected by the lateral boundary conditions for all seasons and over most of the continent, even when a large number of global climate models (GCMs) is downscaled; for instance, RCA-RACMO shows a consistent dry bias over equatorial Africa, whereas CCLM shows a dry bias over the eastern coast of the Guinea Gulf
Summary
The second-largest continent on Earth and with the fastest population growth, is most vulnerable to weather and climate variability (Niang et al 2014). Much research has evaluated the ability of the CORDEX Regional Climate Models (RCMs), forced either by the ERA-Interim reanalysis (Dee et al 2011) or GCMs, to reproduce present African climatology (e.g., Nikulin et al 2012; Endris et al 2013; Kalognomou et al 2013; Kim et al 2013; Krähenmann et al 2013; Gbobaniyi et al 2014; Panitz et al 2014; Dosio et al 2015; Favre et al 2016; Endris et al 2016; Klutse et al 2016) This shows that RCMs simulate the precipitation seasonal mean and annual cycle quite accurately, but large differences and biases exist amongst models in some regions and seasons.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
