ALBERT

Description: This study assesses the changes in precipitation (P) and in evapotranspiration (ET) under 1.5 °C and 2.0 °C global warming levels (GWLs) over Senegal in West Africa. A set of twenty Regional Climate Model (RCM) simulations within the Coordinated Regional Downscaling Experiment (CORDEX) following the Representative Concentration Pathways (RCP) 4.5 emission scenario is used. Annual and seasonal changes are computed between climate simulations under 1.5 °C and 2.0 °C warming, with respect to 0.5 °C warming, compared to pre-industrial levels. The results show that annual precipitation is likely to decrease under both magnitudes of warming; this decrease is also found during the main rainy season (July, August, September) only and is more pronounced under 2 °C warming. All reference evapotranspiration calculations, from Penman, Hamon, and Hargreaves formulations, show an increase in the future under the two GWLs, except annual Penman evapotranspiration under the 1.5 °C warming scenario. Furthermore, seasonal and annual water balances (P-ET) generally exhibit a water deficit. This water deficit (up to 180 mm) is more substantial with Penman and Hamon under 2 °C. In addition, analyses of changes in extreme precipitation reveal an increase in dry spells and a decrease in the number of wet days. However, Senegal may face a slight increase in very wet days (95th percentile), extremely wet days (99th), and rainfall intensity in the coming decades. Therefore, in the future, Senegal may experience a decline in precipitation, an increase of evapotranspiration, and a slight increase in heavy rainfall. Such changes could have serious consequences (e.g., drought, flood, etc.) for socioeconomic activities. Thus, strong governmental politics are needed to restrict the global mean temperature to avoid irreversible negative climate change impacts over the country. The findings of this study have contributed to a better understanding of local patterns of the Senegal hydroclimate under the two considered global warming scenarios.

Description: 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 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) 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 based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. 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), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. 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 and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.