Long Lead ENSO Forecast Using an Adaptive Graph Convolutional Recurrent Neural Network

Abstract

El Niño-Southern Oscillation (ENSO), a natural phenomenon in the Pacific Ocean, is caused by cyclic changes in sea-surface temperature (SST) and the overlying atmosphere in the tropical Pacific. The impact of ENSO varies, ranging from slightly warmer or colder temperatures to extreme weather events such as flash floods, droughts, and hurricanes, affecting various regions around the globe. Therefore, ENSO forecasting has paramount importance in the atmospheric and oceanic sciences. The Oceanic Niño Index (ONI), a three-month running mean of SST anomalies over the east–central equatorial Pacific region, is the commonly used metric for measuring ENSO events. However, the literature shows that the forecasting accuracy of ONI for lead times exceeding one year is low. This study aims to improve the forecast accuracy of ONI for up to 18 months lead time by applying an Adaptive Graph Convolutional Recurrent Neural Network (AGCRNN). The graph-learning module adaptively learns the spatial structure of features during training, while the graph convolution in hidden layers of the recurrent neural network captures the temporal relationships of features with ONI. Experiments conducted on simulation and reanalysis datasets demonstrate that AGCRNN outperforms state-of-art statistical and eight dynamical models for forecasting ONI with up to 18 months’ lead time.

Keywords

• graph networks
• deep learning
• climate anomalies
• spatial-temporal data mining