Predictive modeling of Super El Niño through integrated local and global climate signals

Abstract

Abstract Recent surges in extreme weather events underscore the critical need for advanced climate prediction models. Super El Niño (SE) events, characterized by unprecedented climatic and economic impacts, have intensified global concerns. This study introduces a novel predictive framework utilizing the Super El Niño Index (SEI), which integrates diverse datasets, including NOAA Sea Surface Temperature indices and regional data from Korea for correlation analysis and model development. Additionally, high-resolution simulations from NASA’s ECCO2 project are utilized to visualize and quantitatively analyze ocean current and SST dynamics, providing a physical basis for understanding SE-induced variability. The SEI identifies SE events with a threshold value of 75 and demonstrates its effectiveness in accurately capturing major historical events, such as those in 1982–83, 1997–98, and 2015–16, while revealing a rising trend in SEI values since 1982, likely tied to global warming. The model successfully predicted an SEI of approximately 80 for 2023, later validated as one of the five most intense SE events on record. These findings highlight the far-reaching influence of SE events beyond the equatorial Pacific, extending to regions like Korea and Japan, and emphasize the necessity for robust predictive tools to mitigate the growing frequency and intensity of SE events in a warming world.

Keywords

• Super El Niño
• Predictive model
• Super El Niño Index (SEI)
• Climatic signals
• Global warming
• 2023-24 El Niño