A purely data-driven transformer model for real-time predictions of the 2023-24 climate condition in the tropical Pacific

Abstract

The tropical Pacific experienced triple La Nina conditions during 2020-22, and the future evolution of the climate condition in the region has received extensive attention. Recent observations and studies indicate that an El Nino condition is developing with its peak stage in late 2023, but large uncertainties still exist. Here, a transformer-based deep learning model is adopted to make predictions of the 2023-24 climate condition in the tropical Pacific. This purely data driven model is configured in such a way that upper-ocean temperature at seven depths and zonal and meridional wind stress fields are used as input predictors and output predictands, representing ocean-atmosphere interactions that participate in the form of the Bjerknes feedback and providing physical basis for predictability. In the same way as dynamical models, the prediction procedure is executed in a rolling manner; multi-month 3D temperature fields as well as surface winds are simultaneously preconditioned as input predictors in the prediction. This transformer model has been demonstrated to outperform other state-of-the-art dynamical models in retrospective prediction cases. Real-time predictions indicate that El Nino conditions in the tropical Pacific peak in late 2023. The underlying processes are further analyzed by conducting sensitivity experiments using this transformer model, in which initial fields of surface winds and upper-ocean temperature fields can be purposely adjusted to illustrate the changes to prediction skills. A comparison with other dynamical coupled model is also made.