Deciphering Super El Ni\~no: Development of a Novel Predictive Model Integrating Local and Global Climatic Signals
Chae-Hyun Yoon, Jubin Park, and Myung-Ki Cheoun

TL;DR
This paper introduces a new predictive model for Super El Niño events that combines local and global climate signals, using the Super El Niño Index (SEI), and demonstrates its effectiveness in forecasting and analyzing trends related to these extreme weather phenomena.
Contribution
The study develops the Super El Niño Index (SEI) and a predictive model that accurately forecasts Super El Niño events by integrating multiple climatic signals, highlighting increasing trends linked to global warming.
Findings
SEI accurately reflects past Super El Niño events.
Model predicted a Super El Niño for 2023-24, confirmed by recent observations.
SEI trend shows increasing intensity and frequency of Super El Niño events.
Abstract
In recent years, extreme weather events have surged, highlighting the urgent need for action on the climate emergency. The year 2023 saw record-breaking global temperatures, unprecedented heatwaves in Europe, devastating floods in Asia, and severe wildfires in North America and Australia. Super El Ni\~no events, known for their profound impact on global weather, play a critical role in these changes, causing severe economic and environmental damage. This study presents a novel predictive model that integrates systematically local and global climatic signals to forecast Super El Ni\~no events, introducing the Super El Ni\~no Index (SEI), which value of 80 or higher defines a Super El Ni\~no event. Our analysis shows that the SEI accurately reflects past Super El Ni\~no events, including those from 1982-83, 1997-98, and 2015-16, with SEI values for these periods containing 80 within the…
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Taxonomy
TopicsNeural Networks and Applications · Advanced Computational Techniques and Applications
