Abstract:
The Cloud Area Fraction (CAF) and Cloud Radiative Effect (CRE) are among the key metrics for understanding Earth’s climate, as they modulate shortwave and longwave radiation. This study, therefore, analyzes the vertical distribution of CAF and their associated CRE over East Africa (EA) using the Modern-Era Retrospective Analysis for Research and Applications Version (MERRA-2) reanalysis and Clouds and the Earth’s Radiant Energy System (CERES) observational data from 2000 to 2025. The vertical CAFs for low, middle, and high clouds are computed at pressure levels and spatially averaged over EA. These are used to derive shortwave CRE (CREin Wm− 2), longwave CRE (CREin Wm− 2), and net CRE (in Wm− 2) from all-sky and clear-sky radiative fluxes. The validation of MERRA-2 CAF against CERES observations showed it to be reliable (r = 0.749), supporting its use for analyzing cloud-radiation patterns, including verti
cal cloud distribution. The study of vertical CAF and CRE over EA using MERRA-2 reveals a strongly stratified cloud climatology shaped by topography, lake impacts, and large-scale circulation. The vertical CAFs at Low, mid, and high clouds show distinct spatial and temporal patterns, with high clouds intensifying the most with more than 60% of the total CAF (0.256), while the contribution of the low CAF against the total covers 18% (0. 076) is the least. Further, the spatiotemporal CRE is dominated by shortwave cooling, ranging from − 14.71 to -54.36 W m−2, which moderates regional temperatures. Cloud layers interact to influence rainfall: low clouds enhance local convection, mid clouds regulate
moisture transport, and high clouds mark mature convective systems. This collectively reinforces EA’s bimodal rainfall pattern and highlights the critical role of clouds in regional climate dynamics. This study recommends future analyses that integrate long-term model outputs with satellite and ground-based observations to validate cloud patterns. Such emphasis should focus on the CREs of low, middle, and high clouds, their links to rainfall, and model uncertainties to improve understanding of the role of cloud dynamics over EA’s climate
