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arXiv · 2409.06161

Deciphering Super El Niño: Development of a Novel Predictive Model Integrating Local and Global Climatic Signals

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 Niño 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 Niño events, introducing the Super El Niño Index (SEI), which value of 80 or higher defines a Super El Niño event. Our analysis shows that the SEI accurately reflects past Super El Niño events, including those from 1982-83, 1997-98, and 2015-16, with SEI values for these periods containing 80 within the 2-sigma standard deviation. Using data up to 2022, our model predicted an SEI of around 80 for 2023, indicating a Super El Niño for the 2023-24 period. Recent observations confirm that the 2023-24 El Niño is among the five strongest recorded Super El Niño events in history. An analysis of SEI trends from 1982 to 2023 reveals a gradual increase, with recent El Niño events consistently exceeding SEI values of 70. This trend suggests that El Niño events are increasingly approaching Super El Niño intensity, potentially due to more favorable conditions in the equatorial Pacific. This increase in SEI values and the frequency of stronger El Niño events may be attributed to the ongoing effects of global warming. These findings emphasize the need for heightened preparedness and strategic planning to mitigate the impacts of future Super El Niño events, which are likely to become more frequent in the coming decades.

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BibTeXRIS

Chae-Hyun Yoon, Jubin Park, Myung-Ki Cheoun. 2024-09-10. Deciphering Super El Niño: Development of a Novel Predictive Model Integrating Local and Global Climatic Signals. https://arxiv.org/abs/2409.06161

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