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

Abstract

Machine learning methods have now become an optional technique in Earth science research, and such data-driven solutions have also made tremendous progress in weather forecasting and climate prediction in recent years. Since climate data are typically time series, the neural network layers, which can identify the intrinsic connections between the points of the sequence and features in two-dimensional data, perform particularly well for climate prediction. The North Atlantic Oscillation (NAO) is a prominent atmospherical mode in the northern hemisphere, with the frequency change characteristic of sea level pressure (SLP) in the North Atlantic sector. One of the reasons why NAO prediction is still challenging is that NAO is also proven to be influenced by other climate circulations, the most significant of which is the interaction between El Niño-Southern Oscillation (ENSO) and NAO. Therefore, sea surface temperature (SST) in the Pacific Ocean used to characterize ENSO is also one of the factors that contribute to the evolution of NAO and can be used as an input factor to predict the NAO. In this paper, the seasonal lag correlation between ENSO and NAO is explored and analyzed. The interaction has been considered in both short-term forecasting and midterm prediction of the NAO variability. The monthly NAO index (NAOI) fluctuation is predicted using the Niño indices based on the RF-Var model, and the accuracy achieves 68% when the lead time is about three months. In addition, integrating multiple physical variables directly related to the NAO and Pacific SST, the short-term NAO forecasting is conducted using a multi-channel neural network named AccNet with trajectory gated recursive unit (TrajGRU) layer. AccNet has the ability to identify the mechanism of the high-frequency variation in several days, and the NAO variability is indicated by SLP. The loss function of AccNet is set to anomaly correlation coefficient (ACC), which is the indicator that verifies spatial correlation in geoscience. Forecasting extreme events of NAO between 2010 and 2021, AccNet presents higher flexibility compared against other structures that can capture spatial-temporal features.

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

“…Surprisingly, the forecast capability of NAO-MCD remains strong over time, even rebounding at a 6-month lead time. This rebound is not a coincidence; in our previous study using machine learning methods to predict NAO variability, there was a rebound in prediction accuracy at both the 6-month and 12-month lead times [16]. A possible explanation is that the most salient changes in Atlantic SLP values occur at 6-month and 1-year intervals [58].…”

“…Javier et al [15] trained various autoregressive and supervised models, including the integrated moving average model (ARIMA) and LSTM, to predict the NAO for the next week. Mu et al used the RF-Var model to predict the monthly NAO variability based on the Niño indices, and the AccNet model to forecast the short-term SLP based on the NAO, the SST, and other physical variables [16]. However, these models have the following limitations: limited to a single (or few) predictor, ignoring significant multivariate predictors related to the complex air-sea coupling mechanisms underpinning NAO [14,15]; low confidence in the model, with little involvement in predictor interactions and physical explanations [16]; given the short forecast time of the models, it is not capable of making long-term forecasts [17].…”