Abstract ID: 159
Predictability of Wet Bulb Globe Temperature Heat Waves in the United States Plains
Lead Author: Benjamin Davis
University of Oklahoma, United States of America
Keywords: Wet Bulb Globe Temperature, Heat Wave, Great Plains
Abstract: Heatwaves such as those in the summer of 2022 are a leading contributor of weather-related mortality, globally contributing to thousands of deaths each year. The impacts on humans may be direct or indirect through avenues such as heat stress, strained medical capacity, infrastructure breakdown, and reduced crop yields. While extreme heat is often measured by temperature and humidity, Wet Bulb Globe Temperature (WBGT) is commonly used to evaluate real-time heat stress risks in humans and correlates better with heat related illness. WBGT accounts for temperature, humidity, wind speed, and solar radiation through a weighted average of Dry bulb (air) temperature, natural wet bulb temperature, and black globe temperature with adjustments for several individual factors. Further, WBGT is easily calculated empirically from standard meteorological variables. Therefore, understanding the predictability of WBGT may help combat heat related illness effectively and efficiently.
The predictability of WBGT and WBGT heat waves is evaluated in the United States Great Plains (USGP) using ERA5 reanalysis and S2S models from the S2S Project database and SubX. Because WBGT is a function of four meteorological variables the primary driver of each period of high WBGT may differ including by the non-traditional factors of solar radiation and wind speed. Therefore, heat waves are divided into regimes (i.e. Hot/dry vs. warm/humid) based on the underlying conditions that contribute to elevated WBGT. It is hypothesized that heat waves in each regime are driven by different processes and have different sources and levels of predictability. Similarly, heat wave predictors and model forecasts may have different levels of skill in different heat wave regimes. Common WBGT heat wave regimes and sources of predictability are identified, and any differences that exist between regimes are compared in both reanalysis and model data.
Co-authors:
Elinor Martin (University of Oklahoma)
