http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/21 SST 2026-07-21T18:00:37Z http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/671 Vertical Distribution of Cloud Area Fraction and Associated Cloud Radiative Effects over East Africa Using MERRA-2 Reanalysis and CERES (2000–2025) Data Geoffrey W. Khamala; John W. Makokha; Churchill Wanyera; Kanike Raghavendra Kumar; Mansour Almazroui; Pelati Althaf; Aqil Tariq 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 2026-05-20T00:00:00Z http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/669 Long-term characterisation and discrimination of aerosol types across Africa using AERONET observations (1998–2024 Yonah Situma; John W. Makokha; Geoffrey W. Khamala; Kanike Raghavendra Kumar; Richard Boiyo; Geoffrey W. Khamala, Geoffrey W. Khamala Accurate identification and classification of atmospheric aerosols remain a significant challenge across Africa, limiting the precise assessment of their climatic effects. This study presents a comprehensive analysis of aerosol characteristics and aerosol types using ground-based observations from twelve selected AErosol RObotic NETwork (AERONET) sites across Africa during 1998–2024. Key aerosol optical properties, including aerosol optical depth (AOD), Ångstr¨ om exponent (AE), fine-mode fraction (FMF), and single scattering albedo (SSA), were examined to characterize their temporal variability and identify dominant aerosol types. A multivariate scattering-based classification approach was employed to improve aerosol discrimination. The results indicate that dust, biomass-burning, and urban/industrial aerosols are the predominant aerosol categories across the continent. Fine- and coarse-mode absorbing aerosols were particularly prevalent in rural tropical regions, highlighting the coexistence of natural and anthropogenic emission sources. Moreover, extensive biomass- burning activity in tropical Africa contributes substantially to regional aerosol loading and complexity. These findings provide valuable insights for improving the representation of aerosol processes in climate models and enhancing assessments of aerosol–climate interactions over Africa. The proposed multivariate framework advances aerosol classification and source attribution, supporting a better understanding of aerosol-related environmental and climatic impacts across the continent. 2026-01-19T00:00:00Z http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/668 Long-term characterisation and discrimination of aerosol types across Africa using AERONET observations (1998–2024 Yonah Situma; John W. Makokha; Peter N. Khakina; Geoffrey W. Khamala; Kanike Raghavendra Kumar; Richard Boiyo Accurate identification and classification of atmospheric aerosols remain a significant challenge across Africa, limiting the precise assessment of their climatic effects. This study presents a comprehensive analysis of aerosol characteristics and aerosol types using ground-based observations from twelve selected AErosol RObotic NETwork (AERONET) sites across Africa during 1998–2024. Key aerosol optical properties, including aerosoloptical depth (AOD), Ångstr¨ om exponent (AE), fine-mode fraction (FMF), and single scattering albedo (SSA), were examined to characterize their temporal variability and identify dominant aerosol types. A multivariate scattering-based classification approach was employed to improve aerosol discrimination. The results indicate that dust, biomass-burning, and urban/industrial aerosols are the predominant aerosol categories across the continent. Fine- and coarse-mode absorbing aerosols were particularly prevalent in rural tropical regions, highlighting the coexistence of natural and anthropogenic emission sources. Moreover, extensive biomass- burning activity in tropical Africa contributes substantially to regional aerosol loading and complexity. These findings provide valuable insights for improving the representation of aerosol processes in climate models and enhancing assessments of aerosol–climate interactions over Africa. The proposed multivariate framework advances aerosol classification and source attribution, supporting a better understanding of aerosol-related environmental and climatic impacts across the continent. 2026-06-19T00:00:00Z http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/625 Classification, Characterisation, and Use of Small Wetlands in East Africa Nomé Sakané & Miguel Alvarez & Mathias Becker & Beate Böhme & Collins Handa & Hellen W. Kamiri & Matthias Langensiepen & Gunter Menz & Salome Misana & Neema G. Mogha & Bodo Maria Möseler & Emiliana J. Mwita & Helida A. Oyieke & Mark T. van Wijk Small wetlands in Kenya and Tanzania cover about 12 million ha and are increasingly converted for agricultural production. There is a need to provide guidelines for their future protection or use, requiring their systematic classification and characterisation. Fifty-one wetlands were inventoried in 2008 in four contrasting sites, covering a surveyed total area of 484 km2 . Each wetland was subdivided into sub-units of 0.5–458 ha based on the predominant land use. The biophysical and socio-economic attributes of the resulting 157 wetland sub-units were determined. The wetland sub-units were categorized using multivariate analyses into five major cluster groups. The main wetland categories comprised: (1) narrow permanently flooded inland valleys that are largely unused; (2) wide permanently flooded inland valleys and highlands floodplains under extensive use; (3) large inland valleys and lowland floodplains with seasonal flooding under medium use intensity; (4) completely drained wide inland valleys and highlands floodplains under intensive food crop production; and (5) narrow drained inland valleys under permanent horticultural production. The wetland types were associated with specific vegetation forms and soil attributes 2012-08-20T00:00:00Z