Predicting ENSO dynamics with network and complexity analyses

Area of Science:

• Climate Science
• Oceanography
• Meteorology

Background:

• The El Niño Southern Oscillation (ENSO) comprises El Niño, La Niña, and neutral phases.
• Accurate forecasting of ENSO phases is crucial for predicting global climate patterns.

Purpose of the Study:

• To develop advanced methods for forecasting ENSO events, including their onset and magnitude.
• To enable probabilistic forecasting for all three ENSO phases (El Niño, La Niña, neutral).

Main Methods:

• Utilized a climate network approach for forecasting El Niño onset.
• Employed a complexity-based approach for predicting El Niño onset and magnitude.
• Introduced the interannual Oceanic Niño Index relationship as a predictor for La Niña and neutral events.

Main Results:

• Successfully forecasted the absence of an El Niño event in 2025 with 91.4% probability.
• Predicted a neutral ENSO event as the most likely outcome for 2025 with 69.6% probability.
• Anticipated a temporary decrease in global mean temperature due to the forecasted ENSO conditions.

Conclusions:

• The combination of climate network, complexity-based, and Oceanic Niño Index approaches provides a robust framework for probabilistic ENSO forecasting.
• These integrated methods enhance the ability to predict all ENSO phases a year in advance.
• Accurate ENSO predictions aid in anticipating associated global temperature variations.