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The NAO Variability Prediction and Forecasting with Multiple Time Scales Driven by ENSO Using Machine Learning

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Area of Science:

  • Earth Science
  • Climate Science
  • Machine Learning

Background:

  • Machine learning, particularly neural networks, shows promise in analyzing time-series climate data for improved weather and climate prediction.
  • The North Atlantic Oscillation (NAO) is a key atmospheric pattern influenced by El Niño-Southern Oscillation (ENSO), making NAO prediction challenging.
  • Sea surface temperature (SST) in the Pacific, a proxy for ENSO, is a significant factor in NAO variability.

Purpose of the Study:

  • To explore and analyze the seasonal lag correlation between ENSO and NAO.
  • To improve both short-term and midterm prediction of NAO variability by incorporating ENSO data.
  • To develop and evaluate advanced machine learning models for accurate NAO forecasting.

Main Methods:

  • Seasonal lag correlation analysis between ENSO (characterized by Niño indices) and NAO.
  • Prediction of monthly NAO index (NAOI) using the RF-Var model with Niño indices.
  • Development of a multi-channel neural network, AccNet with TrajGRU, integrating physical variables and Pacific SST for short-term NAO forecasting.
  • Utilizing anomaly correlation coefficient (ACC) as the loss function for AccNet to verify spatial correlation.

Main Results:

  • The RF-Var model achieved 68% accuracy in predicting NAOI with a three-month lead time.
  • AccNet demonstrated the ability to capture high-frequency variations in NAO over several days, indicated by sea level pressure (SLP).
  • AccNet showed higher flexibility in forecasting extreme NAO events (2010-2021) compared to other models, effectively capturing spatial-temporal features.

Conclusions:

  • The interaction between ENSO and NAO is crucial for improving NAO prediction accuracy.
  • Machine learning models, especially AccNet, offer advanced capabilities for short-term forecasting of complex climate phenomena like NAO.
  • Accurate prediction of NAO variability and extreme events can be enhanced by integrating ENSO data and sophisticated neural network architectures.