Predictability of Wet Bulb Globe Temperature Heat Waves in the United States Great Plains

Heatwaves 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 temperature, natural wet bulb temperature, and black globe temperature. The WBGT and factors such as clothing and workload can then be combined to account for an individual’s risk of heat exposure. Therefore, understanding the predictability of WBGT may help combat heat related illness effectively and efficiently.

The predictability of WBGT heat waves is evaluated using ERA5 reanalysis and models from the S2S Project Database. North American atmospheric regimes are defined using K-Means clustering of detrended standardized 500 mb geopotential anomalies from ERA5 reanalysis. Additionally, heat wave types (i.e. Hot/dry vs. warm/humid) are defined using the standardized anomalies of temperature and humidity relative to other heat waves during the same season. An analysis of the atmospheric regimes, heat wave types, and seasonality is conducted using these datasets both at analysis time and at lead times up to 5 weeks. Finally, regime statistics are calculated in S2S model forecasts to identify forecast biases.

Each regime has unique heat wave frequency, type, and seasonality characteristics. Heat waves may occur in the US Great Plains with greater than twice the climatological frequency in some regimes, however the increase is often seasonally dependent. Additionally, some skill is shown at discriminating between heat wave types in different regimes. Further, the regimes that are conducive to heat waves at short lead times differ from those that correlate with heat wave occurrence at longer lead times. S2S models show skill in forecasting most regimes up to 5 weeks lead time.