Spatial Distribution of Cordex Regional Climate Models Biases over West Africa

Abstract

The objective of this work is to analyze the spatial distribution of biases of nine (9) regional climate models (RCMs) and their ensemble average used under the framework of COordinated Regional climate Downscaling EXperiment (CORDEX) project over West Africa during the summer period. We assessed the ability of RCMs to represent adequately West African summer rainfall by analyzing some statistical parameters such as the relative bias, the standard deviation, the root mean square error (RMSE) and the correlation coefficient between observation data (GPCP used as reference) and regional climate models outputs. We first analyzed the relative bias between GPCP climatology and the other available observed data (CRU, CMAP, UDEL, GPCC, TRMM and their ensemble mean). This analysis highlights the big uncertainty on the quality of these observed rainfall data over West Africa which may be largely due to the rarity of in situ measurement data over this region. The statistical analysis with respect to GPCP rainfall shows the presence of large relative bias values over most part of West Africa for engaged RCMs. However their ensemble mean outperforms individual RCMs by exhibiting the weakest relative change. The RMSE values are weak over West Africa except over and off the Guinea highlands for RCMs and the Era-interim reanalysis. The spatial distribution of the coefficient of correlation between the observation data and RCMs shows that all models (except HIRHAM) present positive values over the Northern Sahel and the Gulf of Guinea. The model of the DMI exhibits the weakest values of correlation coefficient. This study shows that RCMs simulate West African climate in a satisfactory way despite the fact that they exhibit systematic biases.

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

“…CORDEX-Africa RCMs have been extensively evaluated in the past not only for mean climatology, but also for extreme events (temperature and precipitation), land-atmosphere coupling, circulation patterns, and added value of downscaling. Several previous works investigated the ERA-interim driven runs (e.g., Nikulin et al 2012;Dosio et al 2015 and references within;Akinsanola et al 2015;Shongwe et al 2015;Sarr et al 2015;Klutse et al 2016;Favre et al 2016;Kisembe et al 2018;Careto et al 2018; Warnatzsch and Reay 2019), as well as GCM-driven runs (e.g., Laprise et al 2013;Teichmann et al 2013;Dosio et al 2015;Endris et al 2016;Pinto et al 2016;Dosio 2017;Fotso-Nguemo et al 2017;Gibba et al 2018;Pinto et al 2018;Tamoffo et al 2019a).…”

Assessment of Future Water Resources Availability under Climate Change Scenarios in the Mékrou Basin, Benin

“…These models are chosen because many studies (Akinsola, yacouba, Alioune, etc.) [13][14][15] The second are the simulated data. They are the historical and future projections (RCP4.5 and RCP8.5 scenarios) of rainfall and PET.…”

“…These models are chosen because many studies (Akinsola, yacouba, Alioune, etc.) [13][14][15] in the West African region have recognized their ability to reproduce the rainfall regime. Their characteristics are shown in Table 1.…”

Multi-model Analysis of the West African Monsoon: Seasonal Evolution and the Monsoon Onset

“…These reveal a dry bias over Guinea and a positive rainfall bias over most part of the Sahel. Thus, biases of the RCMs do not depend only on the forcing data because internal variability of models may play a strong role too [37].…”