Climate extremes: informing local heat-health action plans, impact of megaregions on heatwaves, and predictability of precipitation in global scale.

• Online Access: http://hdl.handle.net/2047/D20316352

The problem of climate extremes has had detrimental effects on human life and environment and It is known that climate change is going to make these hazards more frequent, higher in intensity, and longer in durations. On the other hand, the grow in population, and the complexities of modern life are going to add to the vulnerabilities against these hazards. Effective solutions to this problem need to consider different interconnected and complex nature of them. Both in science and engineering, also in policy making phases. While the major source of the problem is in global scale and, there are many local stressors that need to be considered for local planning, such as city-wide planning and decision making. Considering this sophisticated anatomy, different organizations with different focus levels are created and actively working to address the situation. The Intergovernmental Panel on Climate Change (IPCC), The Global Goals universal call for action, and C40 cities are some samples of scientific, policy making, engineering and decision-making groups created to understand and quantify the problems, design solutions, interact them and make decisions. Reaching these goals requires interdisciplinary approaches in many ways. This dissertation has such an interdisciplinary approach around this problem. First looks into the temperature extreme, known as heatwave, problem in small city of Brookline, MA; by assessing the vulnerability, exposure, and hazard levels in different parts of the town and to develop high level adaptation and mitigation strategies for the town officials and its climate action group. In the chapter following that the focus is zoomed out from local into regional scale and is on temperature extremes in US megaregions. It is the first study of such kind focused on megaregions. Then as the last subject of study for this dissertation a statistical model is tested for its robustness in predicting extreme precipitations from mean values. Because of its statistical nature, this method might be used for other variables, too.