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The study investigates how the tropical Atlantic influences the predictability of ENSO, focusing on seasonal variations.

Summer Tropical Atlantic Influence on ENSO Predictability Revealed by Deep Learning and Dynamical Pacemaker Simulations

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The study investigates how the tropical Atlantic influences the predictability of ENSO, focusing on seasonal variations.
Utilized a convolutional neural network (CNN) to analyze relationships between tropical Atlantic and ENSO.
Conducted pacemaker simulations to understand mechanisms behind CNN results.
Assessed summer, autumn, winter, and spring sea surface temperature (SST) anomalies related to ENSO predictions.
Summer tropical Atlantic SST anomalies enhance ENSO predictability, particularly for winter La Niña development.
Spring SST anomalies do not significantly affect predictability due to their distance from the Intertropical Convergence Zone (ITCZ).
Highlight a critical seasonal pathway connecting the tropical Atlantic and Pacific regions for ENSO predictions.

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Convolutional neural network analyzes the tropical Atlantic's impact on ENSO predictability, suggesting new seasonal linkages.

Huang et al. (2026) studied this question.

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