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Physics captured by data-based methods in El Niño prediction.
G Lancia1, I J Goede2, C Spitoni1
1Department of Mathematics, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, Netherlands.
Chaos (Woodbury, N.Y.)
|November 1, 2022
Summary
Machine learning models like Convolutional Neural Networks (CNNs) show high skill in predicting El Niño events. This study reveals CNNs can correct ocean physics distortions but struggle with upwelling feedback strength inaccuracies.
Area of Science:
- Climate Science
- Machine Learning
- Oceanography
Background:
- El Niño, a recurring tropical Pacific warming event, causes global weather disruptions.
- Machine learning, particularly Convolutional Neural Networks (CNNs), demonstrates high skill in predicting El Niño at extended lead times.
Purpose of the Study:
- To investigate which aspects of El Niño physics are captured by a specific CNN classification method.
- To understand the high predictive skill of CNNs in El Niño forecasting.
Main Methods:
- Utilizing distorted physics simulations from the Zebiak-Cane model.
- Applying a specific CNN-based classification method to analyze model data.
Main Results:
- The CNN effectively corrects for distortions in ocean adjustment processes.
- The CNN exhibits significant challenges in handling distortions related to upwelling feedback strength.
Conclusions:
- CNNs show promise in correcting certain physical process distortions for El Niño prediction.
- Further research is needed to improve CNN performance in representing complex feedback mechanisms like upwelling strength for enhanced climate prediction.
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