A Deeper Understanding of Climate Variability Improves Mitigation Efforts, Climate Services, Food Security, and Development Initiatives in Sub-Saharan Africa

Abstract

This research utilized the bagging machine learning algorithm along with the Thornthwaite moisture index (TMI) to enhance the understanding of climate variability and change, with the objective of identifying the most efficient climate service pathways in Sub-Saharan Africa (SSA). Monthly datasets at a 0.5° resolution (1960–2020) were collected and analyzed using R 4.2.2 software and spreadsheets. The results indicate significant changes in climatic conditions in Sudan, with aridity escalation at a rate of 0.37% per year. The bagging algorithm illustrated that actual water use was mainly influenced by rainfall and runoff management, showing an inverse relationship with increasing air temperatures. Consequently, sustainable strategies focusing on runoff and temperature control, such as rainwater harvesting, agroforestry and plant breeding were identified as the most effective climate services to mitigate and adapt to climate variability in SSA. The findings suggest that runoff management (e.g., rainwater harvesting) could potentially offset up to 22% of the adverse impacts of climate variability, while temperature control strategies (e.g., agroforestry) could account for the remaining 78%. Without these interventions, climate variability will continue to pose serious challenges to food security, livelihood generations, and regional stability. The research calls for further in-depth studies on the attributions of climate variability using finer datasets.