A Statistical Analysis to Improve Development of a Heat Wave Definition in the Southern Great Plains

Climate Projections have signaled an increase in frequency, longevity, and intensity of future extreme heat events due to global climate change. Heat waves continue to be defined by a variety of methods. This study aims to use a statistical methodology to generate a heat wave definition specific for the Southern Great Plains (SGP) to allow for climate variability, with respect to time and space. The heat wave methodology in this study connects previous heat wave definitions, but incorporates an enhanced statistical standpoint. This study utilizes two reanalyses (ERA-5 and MERRA-2) and one re-gridded observational (Daymet) dataset from 1980 through 2020. The derived daily maximum and minimum standardized temperature anomalies are fitted to gamma, normal, and skewed normal distributions. This process is completed for each grid point split into seasonal (i.e., Winter, Spring, Summer, and Fall) time periods from 1980 through 2020. By executing the KS-test, each grid point distribution is examined for goodness of fit, before assigning a percentile threshold for SGP heat wave events. Our results suggest that the normal distribution is sufficient for both variables across most of the SGP. From this heat wave definition, a climatology of heat wave events across the SGP was created to further understand forcing mechanisms for heat waves within this region. Furthermore, this heat wave definition proposed in this study aids toward the progress of increasing the predictability and impacts of extreme heat events.