NAO Seasonal Forecast Using a Multivariate Air–Sea Coupled Deep Learning Model Combined with Causal Discovery

• Published in: Atmosphere
• Main Authors: Bin Mu, Xin Jiang, Shijin Yuan, Yuehan Cui, Bo Qin
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: North Atlantic Oscillation; causal discovery; air–sea coupling; deep learning; Meteorology. Climatology; QC851-999; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.3390/atmos14050792; https://doaj.org/article/0e82117722184135b89e4ebe9c6c36a8

The North Atlantic Oscillation (NAO) is a major climatic phenomenon in the Northern Hemisphere, but the underlying air–sea interaction and physical mechanisms remain elusive. Despite successful short-term forecasts using physics-based numerical models, longer-term forecasts of NAO continue to pose a challenge. In this study, we employ advanced data-driven causal discovery techniques to explore the causality between multiple ocean–atmosphere processes and NAO. We identify the best NAO predictors based on this causality analysis and develop NAO-MCD, a multivariate air–sea coupled model that incorporates causal discovery to provide 1–6 month lead seasonal forecasts of NAO. Our results demonstrate that the selected predictors are strongly associated with NAO development, enabling accurate forecasts of NAO. NAO-MCD significantly outperforms conventional numerical models and provides reliable seasonal forecasts of NAO, particularly for winter events. Moreover, our model extends the range of accurate forecasts, surpassing state-of-the-art performance at 2- to 6-month lead-time NAO forecasts, substantially outperforming conventional numerical models.