Super El Niño: 451,000 deaths expected, which regions will be most affected?

Over 451,000 deaths could be attributed to the additional heat wave caused by the upcoming super El Niño, according to estimates published by a group of climate scientists. This projection, covering the next six months, places the severity of the phenomenon on par with recent worst climate disasters, raising global alerts among health services and meteorological forecasting agencies.

Super El Niño: Expected intensity and most affected zones

The El Niño phenomenon, the warm phase of the Pacific ocean-atmosphere cycle, is characterized by a massive release of heat stored in tropical waters into the atmosphere. When it reaches a "super" intensity, sea surface temperature anomalies exceed 2°C above average, amplifying global heat waves. Predictive models from ECMWF and Copernicus indicate that South Asia, Sub-Saharan Africa, and central parts of the Americas will be most exposed, with maximum temperatures regularly exceeding 40°C for several weeks.

Satellite data, combined with neural networks from GraphCast and Pangu-Weather, show reduced prediction uncertainty due to machine learning, but also highlight that the exact intensity of the super El Niño remains sensitive to atmospheric circulation variations. Thus, some coastal regions of the South Pacific could experience extreme precipitation, while continental interiors will see increased drought. This duality underscores the complexity of the phenomenon and the need for continuous monitoring via real-time observation systems.

Mechanism of super El Niño strengthening and role of AI

The strengthening of the super El Niño results from a nonlinear interaction between oceanic heat and Kelvin waves crossing the equator. This dynamics creates a positive feedback loop: as the ocean surface warms, air above rises, releasing more heat into the troposphere. Traditional climate models based on Navier-Stokes equations struggle to capture these high-resolution processes. Therefore, research teams have integrated deep neural networks capable of assimilating Copernicus atmospheric data and adjusting predictions in real-time.

These AI models, trained on decades of observations, use machine learning techniques to reduce prediction uncertainty, notably by correlating sea surface temperature anomalies with water vapor fluxes. The result is a predictive model that anticipates not only the trajectory of the super El Niño but also its thermal impact on populations. However, scientists remind us that predictions remain subject to margins