http://197.159.135.214/jspui/handle/123456789/980 2026-08-06T01:35:33Z 2026-08-06T01:35:33Z Kolotioloma, Yeo http://197.159.135.214/jspui/handle/123456789/1260 2026-06-25T12:01:40Z 2025-07-01T00:00:00Z

Impact of Climate Variability on Atmospheric Drivers of major Dust Storms over North Africa Kolotioloma, Yeo Dust storms over North Africa represent a critical component of the regional and global climate system, influencing radiation balance, precipitation processes, ecosystem functioning, and human health. Despite their importance, recent studies suggest a multidecadal decline in dust activity across the region, though the driving mechanisms remain debated. This study investigates the spatiotemporal trends in North African dust storm frequency over the past four decades and explores the meteorological and climate drivers underlying these changes using a combination of observational datasets, reanalysis products, and statistical and machine learning methods. Seasonal and annual dust frequency trends derived from surface visibility records reveal a pronounced decrease in dust activity, particularly across the Sahel and central Sahara. Using Theil-Sen trend estimation and Mann-Kendall significance testing, we detect statistically significant declines in surface wind speeds and increases in vegetation cover (leaf area index) and precipitation, especially between 10°N and 15°N. Concurrently, the Saharan Heat Low (SHL) shows signs of intensification and expansion, suggesting possible suppression of dust uplift due to modifications in regional circulation and thermodynamic stability. Correlation analyses further highlight strong seasonal associations between dust storm frequency and drivers such as 10-m wind speed, precipitation, SHL strength, and climate indices (Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO)). A Self-Organizing Map (SOM) classification of sea level pressure and 925 hPa wind patterns during dust storm days reveals dominant atmospheric configurations associated with dust generation, with a clear seasonality and regional preference in SOM node activation. Additionally, Long Short-Term Memory (LSTM) models using climatic and environmental predictors demonstrate skill in reconstructing the historical evolution of dust storms, reinforcing the predictability of dust activity based on climate variability. Overall, the results support the hypothesis that recent declines in North African dust storm activity are linked to a combination of decreased surface wind stress, increased vegetation and rainfall, and changes in the SHL and large-scale climate drivers influence. These findings provide an updated understanding of dust-climate interactions and underscore the importance of land-atmosphere coupling and climate teleconnections in shaping dust variability. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-07-01T00:00:00Z Yamoula, Dametoti http://197.159.135.214/jspui/handle/123456789/1259 2026-06-25T11:49:33Z 2025-06-01T00:00:00Z

Ocean-Atmosphere interactions in Northwest African and Gulf of Guinea Coastal Upwelling Systems Yamoula, Dametoti The Ocean and Atmosphere interaction is a complex system that requires particular attention in coastal upwelling systems due to their nutrient supply, biological productivity, influence on local weather patterns and regional climate variability, and socio-economic importance. The present study investigates the behaviour of ocean, atmosphere, and their interactions in the coastal upwelling systems of Northwest Africa and Gulf of Guinea during ocean cooling and warming episodes, with a focus on the dynamical and interannual variability of Senegal-Mauritania and Gulf of Guinea upwelling regions. Multiple datasets including different physical and biogeochemical variables, several upwelling indices and statistical tools were used. Results highlighted the seasonal dynamic and interannual variability of two upwelling systems modulated by both local and remote forcings such as wind stress-driven Ekman dynamics, mesoscale and cyclonic eddies, regional ocean ocean currents, Equatorial dynamics including Kelvin and Rossby waves and coastal trapped waves, and large-scale climate drivers. The results also underlined the influence of other geographical and environmental factors such as coastal shape and orientation, the geographical position of upwelling system, the motion of Intertropical convergence zone, and Azores and Saint Helena high pression systems that modulated pression and temperature gradients. The study identified marine heat waves, which are characterized by weakened Ekman transport and suppressed vertical mixing, as emerging stressors of coastal upwelling dynamics. These events significantly impact coastal marine ecosystem productivity, surface air temperature, and precipitation, underscoring the importance of addressing them in local weather predictions. The findings also demonstrate the ongoing limitations of the latest generations of the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) in capturing coastal processes and upwelling features, and call for the use of more powerful artificial intelligence predictive models, such as long short-term memory and convolutional neural networks (LSTM-CNN), to improve weather predictions and reduce uncertainties in climate model simulations in coastal upwelling regions. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-06-01T00:00:00Z Akum, Robert Ayueboning http://197.159.135.214/jspui/handle/123456789/1258 2026-06-25T10:52:06Z 2025-07-01T00:00:00Z

Drivers and Predictive Modelling of Thunderstorms and Precipitation over West Africa Akum, Robert Ayueboning Thunderstorms provide essential precipitation for agriculture and other economic activities in the tropics. Accurate forecasting of thunderstorm variability and precipitation forecasting over West Africa is vital for agricultural planning, water resource management, hydropower generation, and disaster risk reduction. This study aims to identify the trends and drivers of monthly thunderstorm frequency from 1990 to 2013 across 22 synoptic stations in Ghana using a machine learning approach. Additionally, for the first time, monthly thunderstorm frequencies over Ghana were forecast one year in advance using machine learning models to offer actionable insights for planning and disaster preparedness. Moreover, this study pioneers the application of machine learning to predict precipitation a year ahead over West Africa at a spatial resolution of 0.10° × 0.10°, filling a significant gap in long-term seasonal forecasting systems. LightGBM, XGBoost, Random Forest (RF), and ensemble models were developed utilizing ERA5 reanalysis data, climate indices, and geographic coordinates as input features. LightGBM and XGBoost models were selected for precipitation prediction due to their ability to efficiently process large datasets on standard computing infrastructure. To better understand model behaviour and the influence of features on monthly precipitation over West Africa, SHapley Additive exPlanation (SHAP) plots were employed. Recursive feature elimination was used to select the most important features from over 60 candidates to model thunderstorm variability, which the models explained at a rate of 79%. SHAP analysis revealed that convective available potential energy (CAPE) was the primary driver of thunderstorm variability, followed by top-of-atmosphere incident solar radiation (TISR), convective inhibition (CIN), 10 m wind speed (SI10), and longitude. The frequency of thunderstorms in Ghana declined significantly, coinciding with a notable decrease in CAPE and an increase in CIN, providing strong evidence of a causal relationship. The resulting LightGBM, XGBoost, RF, and ensemble models achieved R2 values of 0.74, 0.75, 0.75, and 0.76, respectively. This research introduces a forecasting scheme for thunderstorm frequencies in Ghana, offering a valuable tool for seasonal prediction. The precipitation models developed to forecast rainfall one year ahead across West Africa explained between 77% and 84% of the variance. Effectively capturing temporal and spatial rainfall patterns, including the ‘monsoon jump,’ ‘little dry season,’ and the Ghana-Benin dry zone, highlighting areas with high and low precipitation on a monthly basis. Analysis of model behaviour indicated that different features influence monthly precipitation across various zones of West Africa. These models can be adapted and integrated into the seasonal forecasts of national meteorological agencies to enhance accuracy. Better seasonal rainfall forecasts could assist West African farmers in anticipating rainfall variability, preparing for droughts and floods to safeguard crop yields. Countries within the study area that rely on hydroelectric power or surface water resources can also utilise these precipitation prediction models for planning and mitigating drought and flood risks. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-07-01T00:00:00Z Maiga, Moussa Ibrahim http://197.159.135.214/jspui/handle/123456789/1257 2026-06-25T10:33:10Z 2025-05-01T00:00:00Z

Current and Projected Climate Change Impacts over Inner Niger Delta Wetland of Mali Maiga, Moussa Ibrahim This study aimed to evaluate the ability of selected RCMs and GCMs of CORDEX-CORE in reproducing the spatio-temporal patterns of some climatic variables, analyse the future climate scenarios over West Africa sub-region and assess the projected impact on changes in water density of the Inner Niger Delta (IND) wetland of Mali. A combinational metrics of three RCMs driven by three GCMs was used from 1970 to 2005 for evaluation. Three major climate variables, such as precipitation, air temperature, and evaporation, were evaluated using statistical parameters such as correlation coefficient (R), Mbias and RMSE compared to ERA5 data as observation. Results produced R values of 0.91 for the Sahel, 0.95 for the savannah, and 0.88 for the Guinea Coast for precipitation. Negative Mbias values reveal an underestimation of precipitation by the RCMs compared to ERA5. RMSE values range from 27.34mm to 151.53mm overall. For evaporation, R values are 0.93 for the Sahel, 0.93 for the savannah, and 0.86 for the Guinea Coast, with negative Mbias values except for CCLM5 simulations. RMSE varies from 9.54mm to 61.41mm overall. Air temperature R values are 0.86 for the Sahel, 0.93 for the savannah, and 0.93 for the Guinea Coast, with positive Mbias indicating that RCMs overestimate air temperature. Annual changes in precipitation, evaporation, air temperature, and runoff for West Africa were assessed under RCP 2.6 and RCP 8.5. RCP 2.6 shows moderate, regionally varied changes. Under RCP 8.5, precipitation and runoff decrease sharply, especially in the Sahel and coastal zones, while evaporation and temperature increase significantly, intensifying water stress across the region. Seasonal change in the precipitation, evaporation, air temperature and total runoff for the near and far future periods relative to the historical of the IND under RCP 8.5 and RCP 2.6 scenarios were then analysed. Precipitation shows high inter-model variability. Under RCP 8.5, the decrease is marked in MAM and JJA, with an increase in SON. Evaporation follows complex patterns across models. Temperature increases systematically. Runoff varies across seasons and models, which could affect water availability in the Inner Niger Delta (IND). SWAT hydrological model was used to assess the impacts of climate change on streamflow and runoff at three hydrometric stations of the IND with continuous and reliable data: Mopti, Diré and Tombouctou. The results showed a general increase in streamflow and runoff over the three stations studied. Under RCP2.6, increases were particularly pronounced in the months of September, October, November and December (SOND), reaching up to 90% in some localities. In contrast, under RCP8.5, although increases were still significant, they were more moderate and more concentrated in the wet season (JJA and MAM). These hydrological changes will have major implications for water availability, flood frequency, and water resource management in the Inner Niger Delta, while increased seasonal variability could impact the resilience of ecosystems and socio-economic activities dependent on the Niger River. A Thesis submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Federal University of Technology, Akure, Nigeria, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree in West African Climate Systems

2025-05-01T00:00:00Z