Forecasting ENSO using convolutional LSTM network with improved attention mechanism and models recombined by genetic algorithm in CMIP5/6

Abstract

El Niño-Southern Oscillation (ENSO) has a profound impact on global climate, and the ability to forecast it effectively over the long term is essential. In recent years, deep learning methods have demonstrated superior prediction outcomes compared to conventional numerical models. However, due to limited observational data, most of these deep learning methods learn information from simulation data derived from physical models, and ensuring the quality of such simulation data can prove challenging. As a result, the models in CMIP5/6 were recombined using genetic algorithms (GAs) to create our training dataset. A deep learning model was then used to learn features from the output of these combined CMIP models so that these physical numerical models can complement each other. To address the issue of inadequate spatiotemporal feature extraction present in many deep learning methods, we devised an ENSO deep learning regression model. An improved self-attention mechanism was introduced into the convolutional LSTM (long short-term memory) network to enable our model to better extract local and global spatiotemporal features. With our method and model, we were able to obtain less erroneous forecast results, and our method surpassed other state-of-the-art deep learning methods in terms of its capability for long-term forecasting.