Diagnosis and predictability of intraseasonal characteristics of wet and dry spells over equatorial east Africa
Most of Eastern Africa has arid and semi-arid climate with high space-time variability in rainfall. The droughts are very common in this region, and often persist for several years, preceded or followed by extreme floods. Most of the livelihoods and socio-economic activities however remain rain-dependent leading to severe negative impacts during the periods of occurrence of climate extremes. It has been noted that one extreme event was capable of reversing national economic growth made over a period of several years. Thus no sustainable development can be attained in eastern Africa without effective mainstreaming of climate information in the development policies, plans and programmes. Many past studies in the region have focused on rainfall variability at seasonal, annual and decadal scales. Very little work has been done at intraseasonal timescale that is paramount to most agricultural applications. This study aims at filling this research gap, by investigating the structure of rainfall season in terms of the distribution of wet and dry spells and how this distribution varies in space and time at interannual time scale over Equatorial Eastern Africa. Prediction models for use in the early warning systems aimed at climate risk reduction were finally developed. The specific objectives of the study include, delineate and diagnose the some aspects of the distribution of the wet and dry spells at interannual timescale; investigate the linkages between the aspects of the distribution of wet and dry spells identified and dominant large scale climate fields that drive the global climate; and assess the predictability of the various aspects of wet and dry spells for the improvement of the use in the early warning systems of the region.Several datasets spanning a period of 40 years (1961 – 2000) were used. The data included gauged daily rainfall amount for the three Eastern Africa countries namely Kenya, Uganda, and Tanzania; Hadley Centre Sea Surface Temperature (SST); re-analysis data and radiosonde observations from Nairobi (Kenya) and Bangui (Central Africa Republic) upper air stations. The indices of El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole and SST gradients which constituted the predefined predictors were also used [...]