The Earth's climate is projected to change significantly in the future, which will greatly impact many natural and human systems. Climate projections are important components of climate change impact, vulnerability, and adaptation assessments, and this research aims to examine several issues related to climate models and model projections and the use of these projections in impact assessment studies. The research is composed of three individual studies. The first study compares various methods in generating climate change projections for use in agriculture assessment studies at several lake-modified sites in the Great Lakes region of the United States. By producing climate change projections using different data sources and methods and comparing their similarities and differences, the study hopes to inform impact researchers and decision makers about the various choices for generating climate change projections and their advantages/disadvantages. The second study assesses the skill of regional climate models (RCMs) in simulating low-level wind maxima, often referred to as low-level jets (LLJs). As a pronounced climate feature in central United States, the LLJs have their impacts ranging from wind energy, to precipitation, and to bird migration. Knowing how well RCMs simulate the climatology of LLJ is a necessary first step towards a better understanding of RCMs as a powerful tool for generating regional climate change projections through dynamical downscaling for central US and other regions affected by LLJs. Finally, the third study applies RCM projections to assess the potential risk of extreme wildfires in the United States. Climate change is expected to alter the frequency and severity of atmospheric conditions conducive to wildfires. Using outputs from a suite of RCMs, this study examines the changes of an operational fire weather index, the Haines Index, between the current climate and the projected future climate. The results are expected to be used to inform fire managers that future summers might be more conducive to extreme and erratic wildfires.
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