Future extreme heat wave events using Bayesian heat wave intensity-persistence day-frequency model and their uncertainty

Heat wave events are occurring more often and over a wider area around the world, and in the coming era of global warming, their intensity will intensify and their persistence days will be longer. In this study, the relationship between the occurrence probability, intensity, and persistency days of future extreme heat wave events is explained using the concept of IDF (rainfall intensity-duration-frequency) curves for extreme rainfall events. The uncertainty of the non-stationary heat wave intensity-persistence day-frequency (HPF) model is analyzed using Bayesian inference. Non-stationary HPF curves were applied to a total of 16 future daily maximum surface air temperature ensembles projected at six major sites in Korea. From the ensemble of future climate information, it was found that the intensity of the extreme heat wave event for persistence day of 2-day estimated in 2050 would be more likely to rise in the range of 1.23-1.69 degrees C (under RCP 4.5) and 1.15-1.96 degrees C (under RCP 8.5) than that estimated in 2010. It was also found that the uncertainty resulting from parameter estimation of the HPF model was greater than the uncertainty resulting from the inter-model variability of various climate model combinations. When reflecting the uncertainty resulting from the estimation of model parameters, the 95% confidence interval of the delta change for the heat wave intensity projected in 2050 from 2010 was estimated to be 0.53-4.94 degrees C (under RCP 4.5) and 0.89-5.57 degrees C (under RCP 8.5).

Automated summary

The study focuses on the increase in frequency and intensity of heat wave events globally, especially in the context of global warming. By analyzing the uncertainty of a non-stationary heat wave model using Bayesian inference, it was found that the intensity of extreme heat waves is expected to rise in the future, with greater uncertainty coming from parameter estimation compared to inter-model variability.