Exceedance probability model for predicting the frequency of summer hot day patterns and temperature variability in Florida

Abstract

Global warming presents an urgent environmental challenge, marked by disrupted climate patterns, increased flooding and droughts, reduced biodiversity, and accelerated species extinction rates. Our study offers a detailed analysis and estimation of hot summer days (HD) patterns and examines their association with Summer Daily Maximum Temperature (SDMT). Employing a estimation model grounded in the normal distribution of temperature records, the exceedance probability of HD occurrences was estimated. The study also applies the K-means clustering algorithm to categorize meteorological stations, enabling a deeper understanding of regional variances and warming trends. To show the applicability of the proposed methodology, 28 meteorological stations in the State of Florida, USA, were selected for the period from 1959 to 2022. The results revealed a significant increase of approximately 0.12 °C in Florida's average Maximum temperature over the past decades, coupled with an average rise of 2.5 HD per decade. Geographical analysis identifies the north and some central as the most affected regions with the highest rise in SDMT, while the parts of central and western show the most substantial increase in HD during summer. The data conclusively indicates that as average SDMTs increase, the frequency of HD escalates dramatically. Projections up to the year 2050 suggest a continued rise in HD across Florida, classified into three severity categories: severe, moderate, and mild. These findings underscore the critical implications of global warming on the frequency of hot days in Florida, necessitating urgent and effective climate change mitigation strategies.