UC Davis

The significant impacts of climate change on extreme weather events have been widely demonstrated in past decades. As the most expensive climate disaster, the drought and its risks under the warming climate always attract people's attention. However, huge uncertainties exist in climate projections, mainly consisting of scenario uncertainty, model uncertainty, and internal variability. Moreover, drought is more difficult to assess in climate datasets, due to its long duration per event, relative to the length of a typical simulation. Meanwhile, since the infamous extreme drought of the 1960s, the climate of the Northeastern United States (NEUS) has generally trended towards warmer and wetter conditions. Nonetheless, there is mounting evidence that short-term droughts will continue to pose a significant risk for this region. Therefore, there is a growing need for a comprehensive framework with the most advanced climate techniques and datasets to investigate the impacts of climate change on drought over NEUS.

In this thesis, firstly, a comprehensive drought feature-based evaluation system, equipped with statistical hypothesis testing and Principal Feature Analysis, is put forward and is designed to be easily used for any climate datasets. With its help, people can choose climate models with fewer model uncertainties and biases in capturing droughts in the regions of interest. This system is applied to three characteristically distinct regions in the conterminous US and across several commonly employed climate datasets (CMIP5/6, LOCA, and CORDEX). As a result, insights emerge into the underlying drivers of model bias in global climate models, regional climate models, and statistically downscaled models.

Then to reduce the impacts of internal variability on drought projections, 7 large ensemble (LE) models and a novel drought index are employed to examine the changing trends of drought at different temporal scales. We find most LE models indicate the NEUS will experience a long-term wetting trend with more ``extremely wet'' months, but also more frequent short-term extreme droughts. These changes are associated with increasing precipitation, atmospheric water demand, and climate variability. We also conclude that discrepant trends in precipitation and evapotranspiration variability will lead to increasing anti-correlation of these variables, which is relevant to the intensification of rapidly developing drought -- particularly in the spring season. These changes are associated with an increase in evapotranspiration from plants, brought by an earlier emergence of the growing season and denser vegetation.