Precipitation in the Congo Basin was examined using a version of the National Center for Atmospheric Research Community Earth System Model (CESM) with water tagging capability. Using regionally defined water tracers, or tags, the moisture contribution from different source regions to Congo Basin precipitation was investigated. We found that the Indian Ocean and evaporation from the Congo Basin were the dominant moisture sources and that the Atlantic Ocean was a comparatively small source of moisture. In both rainy seasons the southwestern Indian Ocean contributed about 21% of the moisture, while the recycling ratio for moisture from the Congo Basin was about 25%. Near the surface, a great deal of moisture is transported from the Atlantic into the Congo Basin, but much of this moisture is recirculated back over the Atlantic in the lower troposphere. Although the southwestern Indian Ocean is a major source of Indian Ocean moisture, it is not associated with the bulk of the variability in precipitation over the Congo Basin. In wet years, more of the precipitation in the Congo Basin is derived from Indian Ocean moisture, but the spatial distribution of the dominant sources is shifted, reflecting changes in the midtropospheric circulation over the Indian Ocean. During wet years there is increased transport of moisture from the equatorial and eastern Indian Ocean. Our results suggest that reliably capturing the linkages between the large‐scale circulation patterns over the Indian Ocean and the local circulation over the Congo Basin is critical for future projections of Congo Basin precipitation.

Rapid growth of agriculture, industries and urbanization within the Awash basin, Ethiopia, as well as population growth is placing increasing demands on the basin’s water resources. In a basin known for high climate variability involving droughts and floods, climate change will likely intensify the existing challenges. To quantify the potential impact of climate change on water availability of the Awash basin in different seasons we have used three climate models from Coupled Models Inter-comparison Project phase 5 (CMIP5) and for three future periods (2006–2030, 2031–2055, and 2056–2080). The models were selected based on their performance in capturing historical precipitation characteristics. The baseline period used for comparison is 1981–2005. The future water availability was estimated as the difference between precipitation and potential evapotranspiration projections using the representative concentration pathway (RCP8.5) emission scenarios after the climate change signals from the climate models are transferred to the observed data. The projections for the future three periods show an increase in water deficiency in all seasons and for parts of the basin, due to a projected increase in temperature and decrease in precipitation. This decrease in water availability will increase water stress in the basin, further threatening water security for different sectors, which are currently increasing their investments in the basin such as irrigation. This calls for an enhanced water management strategy that is inclusive of all sectors that considers the equity for different users.

Abstract. Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a).

The interannual variability, trends, and the mean climatology of East African long rains are difficult for models to simulate. This is in part because long rains do not respond in a simple way to large scale modes of variability such as ENSO, and because of interactions with complex topography. Here we focus on the Kenyan regional climate in the ERA-Interim reanalysis during the long rains to create a set of atmospheric diagnostics which can be applied to the evaluation of climate models. Sub-seasonal observed rainfall and reanalysis reveals that very wet/dry seasons develop differently at the beginning of the season. Sub-seasonal aggregation periods (days 60-80, 80-100,90-120,120-150) highlight local (e.g. mid-tropospheric ascent, moisture flux convergence in the lower to mid-troposphere, and mid-tropospheric moisture) and large scale (e.g. mid-tropospheric zonal winds over central Africa, upper tropospheric velocity potential) diagnostics which are useful to evaluate model atmospheric circulation affecting Kenyan rainfall in mean and wet/dry extremes.