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Related Experiment Videos

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms.

Manali Pal¹, Rajib Maity², J V Ratnam³

¹Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.

Scientific Reports

|January 17, 2020

Summary

Machine Learning (ML) algorithms, Support Vector Regression (SVR) and Random Forest (RF), can forecast the El Niño (La Niña) Modoki index (EMI) phase up to 12 months ahead. SVR demonstrated superior performance over RF in these long-lead predictions.

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Area of Science:

Climate Science
Machine Learning
Oceanography

Background:

El Niño (La Niña) Modoki (ENSO Modoki) index (EMI) is crucial for climate prediction.
Accurate long-lead prediction of EMI is challenging but vital for climate anomaly forecasting.
Machine Learning (ML) offers potential for improving ENSO prediction.

Purpose of the Study:

To evaluate the efficacy of non-linear ML algorithms for long-lead EMI prediction.
To compare the performance of Support Vector Regression (SVR) and Random Forest (RF) for EMI forecasting.
To identify key climate variables for predicting EMI using ML.

Main Methods:

Utilized Support Vector Regression (SVR) and Random Forest (RF) algorithms.
Forecasted the El Niño (La Niña) Modoki index (EMI) at 6, 12, 18, and 24-month lead times.
Identified predictors using Kendall's tau correlation and evaluated importance via Supervised Principal Component Analysis (SPCA) on sea surface temperature (SST), sea surface height (SSH), and soil moisture content (SMC).

Main Results:

Both SVR and RF accurately forecast the phase of the EMI at 6 and 12-month lead times.
The amplitude of strong EMI events was underestimated by both ML models.
Support Vector Regression (SVR) outperformed Random Forest (RF) in EMI forecasting accuracy.

Conclusions:

Machine Learning algorithms, particularly SVR, show significant promise for long-lead ENSO Modoki index prediction.
The study highlights the capability of SVR for reliable EMI phase forecasting up to a year in advance.
Further refinement is needed to improve the amplitude prediction of extreme EMI events.