This study analyses uncertainties associated with the annual cycle of West African rainfall characteristics in 15 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5) over the Sahel and Guinean regions. Indices based on daily rainfall such as the frequency and the ntensity of wet days, the consecutive dry days (CDD) and wet days (CWD), the 95th percentile of daily rainfall (R95) and its contribution to the umulative monsoon rainfall (R95PTOT) have been assessed. Over both regions, TRMM, GPCP and CHIRPS observational datasets provide very consistent results on the annual cycle of precipitation but less so on the frequency of wet days. Conversely, higher uncertainties are noted on the intensity of wet days over both study areas, particularly over the Guinean region. Overall, CMIP5 simulations present much higher uncertainties in the representation of the mean precipitation climatology, often provide too early (late) onset dates over the Sahel (the Guinean region) and overestimate rainfall during the early and late monsoon phases. These errors do not compensate at the annual scale nor when considering West Africa as a hole. Results also reveal that over the Guinean region, the difficulty of models to represent the annual structure of the mean precipitation strongly involves biases in the representation of the annual cycle of the frequency of wet days. We found strong uncertainties in the simulation of the CWD and he CDD over both areas. Conversely for R95 and R95PTOT, the ncertainties in CMIP5 models appear somewhat weaker, but the magnitude of R95 is largely underestimated in most models.

This study analyses uncertainties associated with the main features of the annual cycle of West African rainfall (amplitude, timing, duration) in 15 CMIP5 simulations over the Sahelian and Guinean regions with satellite daily precipitation estimates. The annual cycle of indices based on daily rainfall such as the frequency and the intensity of wet days, the consecutive dry (CDD) and wet days (CWD), the 95th percentile of daily rainfall (R95) have been assessed. Over both regions, satellite datasets provide more consistent results on the annual cycle of monthly precipitation than on higher-frequency rainfall indices, especially over the Guinean region. CMIP5 simulations display much higher uncertainties in both the mean precipitation climatology and higher-frequency indices. Over both regions, most of them overestimate the frequency of wet days. Over the Guinean region, the difficulty of models to represent the bimodality of the annual cycle of precipitation involves systematic biases the frequency of wet days. Likewise, we found strong uncertainties in the simulation of the CWD and the CDD over both areas. Finally, models generally provide too early (late) onset dates over the Sahel (the Guinean region) and overestimate rainfall during the early and late monsoon phases. These errors are strongly coupled errors in the latitudinal position of the ITCZ and do not compensate at the annual scale nor when considering West Africa as a whole. wet days. We found strong uncertainties in the simulation of the CWD and he CDD over both areas. Conversely for R95p and R95PTOT, the ncertainties in CMIP5 models appear somewhat weaker, but the magnitude f R95 is largely underestimated in most models.

This study focuses on the ability of the global land data assimilation system LDAS-Monde to improve the representation of land surface variables (LSVs) over Burkina Faso through the joint assimilation of satellite derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5, leading to a 0.25° x 0.25° spatial resolution reanalysis of the LSVs. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF. Results of two experiments are compared: open-loop simulation (i.e. no assimilation) and analysis (i.e. joint assimilation of SSM and LAI). After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below). The assimilation is able to improve the simulation of both SSM and LAI. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.