Physics captured by data-based methods in El Niño prediction

On average once every four years, the Tropical Pacific warms considerably during events called El Niño, leading to weather disruptions over many regions on Earth. Recent machine-learning approaches to El Niño prediction, in particular Convolutional Neural Networks (CNNs), have shown a surprisingly h...

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Bibliographic Details
Published inarXiv.org
Main Authors Lancia, G, Goede, I J, Spitoni, C, Dijkstra, H A
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.06.2022
Subjects
Artificial neural networks
El Nino
Machine learning
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Summary:On average once every four years, the Tropical Pacific warms considerably during events called El Niño, leading to weather disruptions over many regions on Earth. Recent machine-learning approaches to El Niño prediction, in particular Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times. In an attempt to understand this high skill, we here use data from distorted physics simulations with an intermediate complexity El Niño model to determine what aspects of El Niño physics are represented in a specific CNN-based classification method. We find that the CNN can adequately correct for distortions in the ocean adjustment processes, but that the machine-learning method has far more trouble to deal with distortions in upwelling feedback strength.
ISSN:2331-8422
DOI:10.48550/arxiv.2206.03110