MSPT: A Transformer-Based Model Using Multiscale Periodic Information for 10–30 d Subseasonal Daily Sea Surface Temperature Forecasting

Accurately subseasonal daily sea surface temperature prediction (SSTP) is significant for forecasting and mitigating extreme climate events related to sea surface temperature (SST). However, this scale's forecasting lies at the transitional zone between short-term forecasting and long-term climate prediction, requiring simultaneous consideration of small-scale variations crucial for the former and large-scale variations fundamental to the latter. Thus, achieving precise subseasonal daily SSTPs is challenging. In this study, we introduce a novel multiscale periodic transformer (MSPT) to predict subseasonal daily SST, which can account for temporal variations at various scales. Initially, MSPT integrates fast Fourier transform and multilayer perceptron to extract all potential periodic scales and adaptively identify critical ones. Each periodic scale features an independent branch composed of patch embedding and Transformer encoder, dedicated to specifically learning temporal variations at that scale. Only the outputs of critical branches are weighted and aggregated to obtain effective multiperiodic scale characteristics. This approach effectively decouples complex temporal patterns, enabling the model to capture reliable dependencies that are beneficial for improving subseasonal forecasting. Furthermore, by introducing additional multivariate attention, our improved Transformer encoder can capture the inherent multivariate correlations of SST dynamics, perfecting the representation of temporal variations at specific periodic scales. Extensive subseasonal forecasting experiments conducted at four locations in the South China Sea demonstrate that MSPT achieves state-of-the-art performance in 10–30 d subseasonal daily SSTPs, validating the effectiveness of multiscale periodic information in improving subseasonal forecasting.