Predicting the West African Monsoon surface weather variability as it impacts livestock and human health

Livestock and agriculture employ approximately 60% of the population of West Africa and are responsible for 35% of its GDP¹. However, livestock are vulnerable to climate variability, which can impact livestock through changes to forage areas, and limits on water availability. It also has an indirect effect on animal health, with environmental factors such as temperature and humidity linked to animal productivity and disease incidence. Centralization or the sharing of water sources as supplies become scarcer can increase the spread of waterborne disease. Zoonotic behavior, the transfer of pathogens between animals and humans, is thought to be affected by these same environmental factors, with the gut microbiota a current area of research. Environmental dependencies also exist for human disease, with meningococcal meningitis incidence inversely related to relative humidity. In this work, we investigate the predictability of the surface fields of the West African Monsoon (WAM) as they specifically impact the interconnections between livestock and human health. The WAM is the dominant, large-scale seasonal feature of the African continent, establishing itself during the boreal summer. The monsoon's southwest winds advect moisture inland from the Gulf of Guinea, leading to increased humidity and rainfall during the summer. Conversely, during the winter, the Harmattan winds from the northeast blanket the region in hot, dry, dusty air from the Sahara Desert. The annual latitudinal migration of the Intertropical Convergence Zone (ITCZ) drives the monsoon onset and retreat, however ancillary factors such as sea-surface temperatures can have a large influence on monsoon timing and strength. Establishing an understanding of monsoon dynamics can inform what the local environmental conditions will be – temperature, humidity, winds, dust. The interseasonal variability of these environmental factors were examined through historical records. Dividing the year into several periods, K-means cluster analysis in each identified spatially coherent regions of each factor. The cluster-averaged values were then correlated with large-scale dynamical and thermodynamical features using climate reanalysis to identify drivers of variability. Correlating the cluster anomalies with large-scale dynamical and thermodynamical features indicate that the anomalies are most strongly connected to the land-ocean temperature gradient and the corresponding circulation, tropical Atlantic sea surface temperatures (SSTs), and to a somewhat lesser extent SSTs over the tropical Pacific. The above large-scale climate drivers were used to develop predictive models at different lead times of temperature, humidity, the combined thermal-humidity index impacting livestock, as well as human meningitis incidence. Verification of results show that understanding of the variability of environmental factors can provide improved forecasting of disease incidence. It is hoped these results will inform disease mitigation strategies, allow for better targeting of management and prevention strategies and provide insights into methods of improving human and livestock wellbeing in general.

¹ West African Agriculture and Climate Change, International Food Policy Research Institute 2013