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Examination of Extreme Rainfall Events in Two Regions of the United States since the 19th Century

Atmospheric Science, University of Alabama in Huntsville, ESSC, UAH, 320 Sparkman Ave, Huntsville AL, 35805

A common hypothesis regarding human-induced climate change is that precipitation processes will accelerate leading to an increasing magnitude of rainfall amounts on a daily time scale as the atmosphere warms. This assertion is supported by two physically demonstrable facts, (1) warmer air accommodates more water vapor, and (2) precipitation processes become more efficient as the cloud environment warms. However, by definition, extreme events are rare, and thus statistics of their occurrence and possible long-term changes present difficult challenges, some herein addressed. In any case, the observational datasets on which hypothesis tests may be carried out should cover the longest periods possible because precipitation can naturally vary considerably on even century time scales. In this study we focus on this temporal issue by building long-term daily precipitation datasets for twenty stations, ten along or near the US Pacific Coast (PC) and ten along or near the coast in the US Southeast (SE). Observations for these stations begin between 1840 and 1890 and end in 2018, using the water year (Oct to Sep) to define the annual period. For some metrics, e.g. the annual total precipitation or the number of days per year measuring greater than 25 mm, there is no discernable change over the most recent 145 years (1874–2018). For other metrics, e.g. the magnitude of the wettest day per year or the temporal distribution of the 29 wettest 2-day events in the past 145 years (i.e. nominal 1-in-5-year occurrence), there appears to be an increase in SE and a decrease in PC. Whether these trends are significant for the relatively short climate record of 145 years will be discussed with the conclusion being the limited time frame of analysis does not lead to decisive claims that these changes are outside of the range of natural variability.
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© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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