Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet-bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures (SST) by atmospheric convection and wave dynamics (see Zhang, Held, and Fueglistaler, 2021 on my publications page for details). Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TWmax. In Zhang et al. (2023), we develop a multiple linear regression model that explains 80% of the variance in tropical mean TWmax and significant regional TWmax variances. Forecast of the annual maximum wet-bulb temperature TWmax averaged over land between 30°S and 30°N and over four regions listed in the menu below. Place your mouse on the interactive chart to read the probability of setting new records. The Oceanic Niño Index for December (NDJ) 2023 is 2.0 based on NOAA.
Map of the root mean squared error (RMSE) of the model and the four focus regions in red boxes.
References
2024
Forecasting Tropical Annual Maximum Wet-Bulb Temperatures Months in Advance from the Current State of El Niño
Yi Zhang, William R Boos, Isaac Held, and 2 more authors
Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet-bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong-to-very-strong El Niño at the end of 2023, with an Oceanic Niño Index of ∼2.0, suggests a 2024 tropical land mean TWmax of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TWmax in specific areas.
