Ambrosino, C.; (2011) Rainfall variability in southern Africa: drivers, climate change impacts and implications for agriculture. Doctoral thesis , UCL (University College London).

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Abstract

Southern Africa is characterised by a high degree of rainfall variability aff�ecting agriculture among other sectors. The focus of this study is to investigate such variability and to identify stable relationships with its potential drivers in the climate system. These relationships are used as the basis for the statistical downscaling of climate model (GCM) outputs. From the simulated rainfall, indices representative of growing season characteristics are computed with the fi�nal purpose of studying the implications on maize cropping under a future climate change scenario. The analysis uses generalized linear models (GLMs), which allow the investigation of the relationships between diff�erent components of the climate system (geographical and climatic drivers) simultaneously. Initially, the e�ffects of various climate indicators upon monthly regional (for all southern Africa) precipitation occurrences and amounts are characterised. Six climate factors are found to drive part of the rainfall variability in the region and their modelled e�ffect upon rainfall occurrences and amounts agrees broadly with previous studies. Among the retained indices, relative humidity and El Niño accounted for the highest degree of explained variability. The location and intensity of the jet stream is also found to have a statistically signi�ficant and physically meaningful e�ffect upon rainfall variability. Although e�ffective for the analysis of monthly regional precipitation, and used to investigate future regional projections, the models do not perform adequately at more local spatial scales such as station locations or few km grids. The same methodology is, therefore, applied to characterise daily precipitation variability at multiple locations within a smaller region. The small scale statistical models capture adequately the seasonal and annual rainfall structure in the area. Indeed, the observations can not be distinguished from the simulated time series. However, the simulated rainfall values tend to be slightly too high throughout the seasons, possibly due to the spatial correlation structure not completely appropriate for such a complex region. From the simulated rainfall sequences, seven growing season indices (including the onset and length of the growing season, proportion of rainy days and total precipitation during the growing season) are derived and their projected change investigated under a climate change scenario. There is little consensus between the 18 selected GCMs, regarding changes in growing season indices between two investigated periods in the 20th and 21st centuries. For the next couple of decades the dominant source of variation in the indices appears to be the natural rainfall variability. Such information should therefore be taken into account when planning adaptation and mitigation strategies. The research presented here emerges as the fi�rst comprehensive assessment of di�fferent climatic factors linked to southern Africa rainfall variability as well as the fi�rst attempt to evaluate the GLMs suitability for the generation of rainfall sequences for agricultural impact studies.

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