| Summary: | <p>Extreme weather risks arise due to the intersection of a meteorological hazard with the exposure of people and assets, and their vulnerability, to this hazard. Anthropogenic climate change (ACC) is driving changes in the likelihood and intensity of many such hazards, at different rates in different regions, and with feedbacks to exposure and vulnerability. With warming of 1.2 °C, the impacts of ACC are growing cumulatively, but the array of drivers in any given event means that its influence often remains enigmatic.</p>
<p>Extreme event attribution (EEA) encompasses a range of approaches to detect the influence of ACC on a given event. Since its conception, the field has matured and expanded, with hundreds of events studied. I first conduct a broad review of EEA to study climate change impacts. This shows the stark juxtaposition between the impacts of climate change with efforts to mitigate and adapt to it. It also highlights the gaps in present knowledge of both attribution and impacts. There are three recommendations for improving this: documenting extremes and their impacts systematically, improving the spatial and hazard-wise coverage of attribution studies, and integrating attribution with risk analysis.</p>
<p>The subsequent thesis chapters each explore one approach to each of these. First, I construct an inventory framework for documenting extremes and impacts. This has implications for risk and adaptation planning and loss and damage assessment. Second, I show how attribution studies in some regions are unnecessary based on the plethora of existing evidence. This implies that the limited resources for attribution could be concentrated more efficiently. Third, I combine attribution analysis with direct risk assessment for a recent destructive event, showing how both approaches are complementary. The thesis concludes with broader recommendations that emerge from the research, including a path towards a global part-operational-part-scientific service for understanding ACC risks and impacts.</p>
|
|---|