Regional Characteristics and Variability of Extreme Precipitation and Atmospheric Rivers in Past, Present, and Future Climates Over the Contiguous United States

Abstract

This dissertation examines the regional and seasonal variability of extreme precipitation and atmospheric rivers (ARs) across the contiguous United States (CONUS) in past, present, and future climates. An extreme precipitation categorization scheme, designed to monitor and track the multi-scale variability of extreme precipitation, is applied to a range of precipitation measurement products as an assessment of observational uncertainty. To investigate the importance of ARs across the CONUS, an objective AR identification algorithm is applied to global reanalysis to identify and characterize AR characteristics regionally over the observational record. Projected change in AR day frequency, geometry, intensity, and associated precipitation is quantified in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the Shared Socioeconomic Pathway 585 (SSP 585) high-end emissions warming scenario. Extreme precipitation most commonly occurs across the mountains of the western US in the winter and over the southeastern US in the summer and fall, associated with ARs and tropical systems, respectively. Observational uncertainty assessment results reveal historical precipitation measurement approaches, including in situ, satellite-derived, gridded in situ, and reanalysis, capture the principal spatial patterns of extreme precipitation climatology, with considerable variability in event frequency, spatial extent, and magnitude. Higher native resolution products most closely resemble in-situ observations, capturing a greater frequency of high-end multi-day totals relative to lower resolution products, even after rescaling, implying a systematic resolution-related bias. Within the observational record, ARs are most frequent in the fall and winter in the West, spring in the Great Plains, and fall in the Midwest and Northeast, showing regional and seasonal variability in basic geometry and IVT. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events are associated with ARs over many parts of the CONUS, including the eastern US, characterized by seasonally-varying moisture transport patterns and lifting mechanisms. Analysis of change between five CMIP6 model historical simulations and future projections, under the SSP585 warming scenario, suggests notable increases in AR day frequency, intensity, and geometry by the end of the 21st century (2071-2100). Projections indicate ARs will comprise a greater share of the total climatological precipitation that falls CONUS-wide, as well as an increasing percentage of the occurrence of the top 5% of multi-day extremes. The findings from this dissertation aim to identify and quantify uncertainty in the regional-scale variability of extreme precipitation and associated meteorological mechanisms among observations and global climate model projections. Future climate change impacts studies require an improved dynamical