Abstract/Summary

Extreme weather and climate-related events can have devastating impacts on people's lives and livelihoods in developing countries, particularly in Africa. Understanding the influence of anthropogenic climate change on extremes is key when addressing the impacts of events now and in the future. Probabilistic event attribution aims to quantify the effect on individual events by analysing changes in probabilities of their occurrences. This thesis investigates this technique and its applications in an African context. It first assesses whether robust attribution results can be produced for events in West Africa. A case study of high precipitation in 2012 concludes that the probability of such an event was likely decreased due to anthropogenic climate change. The different climate model ensembles analysed produce complementary results, but the study highlights the importance of correctly modelling the anthropogenic climate change impact on sea surface temperatures if results are to be robust in regions such as this with strong teleconnections. The application of event attribution is then studied in two key contexts relevant to addressing the impacts of extreme weather events in Africa: national adaptation policy, focussing on addressing urban flooding in Senegal, and international loss and damage policy. In both cases there were suggestions for roles these scientific results could play, but there are barriers to their inclusion at present. In Senegal little climate information is currently used in decision-making, and loss and damage policy lacks clarity around what it will address and therefore how scientific information can support this. In both cases stakeholders demonstrated limited awareness of event attribution, highlighting the need to enhance understanding to encourage further dialogue about its relevance. To this end a participatory game focussing on event attribution was developed and is shown here to be a useful tool for stakeholders to consider how results could be relevant for their decisions.