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A machine learning based prediction system for the Indian Ocean Dipole.

J V Ratnam¹, H A Dijkstra², Swadhin K Behera³

¹Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan. jvratnam@jamstec.go.jp.

Scientific Reports

|January 16, 2020

Summary

This summary is machine-generated.

Machine learning, specifically artificial neural networks (ANNs), can now forecast the Indian Ocean Dipole (IOD) months in advance. These ANNs show superior skill compared to traditional methods and existing climate models.

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Area of Science:

Climate Science
Machine Learning
Artificial Intelligence

Background:

The Indian Ocean Dipole (IOD) significantly impacts global climate, influencing regions like East Africa, Australia, and India.
Predicting the IOD is crucial due to its widespread climatic effects.
Previous IOD prediction studies relied on complex coupled climate models.

Purpose of the Study:

To introduce and evaluate artificial neural networks (ANNs) for Indian Ocean Dipole (IOD) prediction.
To assess the forecasting skill of ANNs for the IOD index.
To compare ANN performance against persistence forecasts and established climate models.

Main Methods:

Utilized artificial neural networks (ANNs) for IOD index forecasting.
Derived predictive attributes from sea surface temperature and geopotential height anomalies (850 hPa and 200 hPa) from 1949-2018.
Generated an ensemble of 500 ANN forecasts using a jackknife resampling approach.

Main Results:

ANN models demonstrated high skill in forecasting the IOD index well in advance (May-November from February-April conditions).
Forecast accuracy significantly surpassed persistence-based predictions.
ANN models outperformed the North American Multi-Model Ensemble (NMME) in terms of correlation coefficients and RMSE.

Conclusions:

Machine learning, particularly ANNs, offers a powerful new approach for skillful IOD prediction.
ANNs provide superior forecasting capabilities compared to existing methods, enabling earlier climate impact assessments.
This study highlights the potential of ANNs to enhance climate variability predictions.