Climate change has already increased the frequency, intensity, and duration of heat waves around the world. In the coming decades, this trend will continue and likely accelerate, exposing much of the world’s population to historically unprecedented conditions. In some regions, extreme temperatures (as indexed by the annual maximum temperature) are projected to increase at a faster rate than mean daily maximum temperatures. This dissertation shows that under a high emissions scenario, by 2060 – 2080 models project that the most extreme temperatures could warm by 1 – 2°C more than the warm season average in some regions. This amplified warming of the most extreme temperatures is most pronounced in the eastern U.S., Europe, eastern China, and parts of the Amazon rainforest, and may have substantial implications for heat risk in these regions. This dissertation explores the physical mechanisms driving the projected amplified warming of extremes in climate models and assesses the associated uncertainty. It shows that the amplification is linked to reductions in cloud cover, increased net surface shortwave radiation, and general surface drying as represented by declines in the evaporative fraction.
In addition to rising temperatures, atmospheric humidity has been observed to increase in recent decades and models project this trend to continue. As a result, joint heat-humidity metrics indicating heat stress are likely to rapidly increase in the future. This dissertation explores how extreme wet bulb temperatures may change throughout the century and assesses the risk of exceeding a fundamental human heat tolerance limit that has been proposed in prior research. It then combines climate data with spatially explicit population projections to estimate the future population exposure to unprecedented wet bulb temperatures. Several regions stand out as being at particular risk: India, the coastal Middle East, and parts of West Africa are likely to experience extremely high wet bulb temperatures in the future, and rapidly growing populations in these regions will result in large increases in exposure to dangerous heat stress. In some areas, it is possible that wet bulb temperatures could occasionally exceed the proposed human tolerance limit by 2080 under a high emissions scenario, but limiting emissions to a moderate trajectory eliminates this risk. Nevertheless, even with emissions reductions, large portions of the world’s population are projected to experience unprecedented heat and humidity in the future.
The projected changes in extreme temperatures will have a variety of impacts on infrastructure and other human systems. This dissertation explores how more frequent and severe hot conditions will impact aircraft takeoff performance by reducing air density and limiting the payload capacity of commercial aircraft. It uses performance models constructed for a variety of aircraft types and projected temperatures to assess the payload reductions that may be required in the future. These payload limits, along with sea level rise, changes in storm patterns, increased atmospheric turbulence, and other effects of climate change, stand to have significant economic and operational impacts on the aviation industry.
Finally, this dissertation discusses evidence-based adaptation strategies to reduce the impacts of extreme heat in urban areas. It reviews a body of literature showing that effective strategies exist to both lower urban temperatures on a large scale and drastically reduce heat-related mortality during heat waves. Many adaptation techniques are not costly, but have yet to be widely implemented. Given the rapid increases in climate impacts that are projected in the coming decades, it will be essential to rigorously assess the cost-effectiveness of adaptation techniques and implement the most efficient strategies in both high- and low-income areas.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D88358JX |
| Date | January 2018 |
| Creators | Coffel, Ethan |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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