An Interpretable NAO Daily Prediction Model Considering Weighted Causal Effects of Physical Processes

The North Atlantic Oscillation (NAO) is a major atmospheric mode in the Northern Hemisphere, characterized by frequent fluctuations in sea level pressure (SLP) across the North Atlantic sector. In the development and evolution of the NAO, various dynamic physical processes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) influence it to different extents. Previous studies using numerical models or deep learning models for daily NAO forecasts have not accounted for the impact of these dynamic physical processes, making accurate and stable NAO forecasting still a challenge. In this study, the Varimax-Rotation Principal Component Analysis (PCA) and data-driven causal inference are used to identify key dynamic physical processes linked to the NAO. Based on these, a deep learning model called the NAO-Causal Weighted Model (NAO-CWM) is developed, which incorporates causal relationships to assign different weights to these processes, providing effective daily forecasts with a lead time of 1–14 days. Evaluation results show that NAO-CWM outperforms the advanced numerical models, offering reliable NAO forecasts and a better capturing of NAO variation trends.