Enhanced Prediction of Sea Surface Temperature Using Empirical Mode Decomposition–Gated Recurrent Unit

Abstract

Sea surface temperature (SST) is a key variable in understanding air–sea interactions, and changes in SST can have profound effects on the global climate and may lead to extreme events such as marine heat wave and floods. Accurate prediction of SST is, therefore, essential for detecting extreme events and mitigating the negative consequences they cause. Numerical model-based predictions are limited by seafloor topography data, parameterization schemes, comprehensive preparation of input data, and computing resources. Recently, data-driven methods bypassing these limitations have merged, but they still face difficulties with nonlinearities in the data. Many traditional models rely on linear assumptions, making it challenging to capture complex climate patterns and rendering them sensitive to data noise and external disturbances, which can lead to inaccurate predictions. Additionally, these methods often exhibit low robustness when dealing with highly nonstationary time series, struggling to adapt to sudden climate events. In this study, we combine empirical mode decomposition (EMD) and the gated recurrent unit (GRU) network to improve the SST prediction based on daily observations. By decomposing complex data into several intrinsic mode functions, EMD can help identify and analyze the underlying nonlinear dynamics, allowing the GRU to better learn the underlying trends and patterns in the signals. A case study in the Bohai Sea shows that the EMD–GRU model significantly outperforms the GRU model. Furthermore, prediction errors indicate that the EMD–GRU model can improve SST prediction by an average of about 45% compared to the Hybrid Coordinate Ocean Model (HYCOM). This study demonstrates that a deep learning model combined with EMD is highly promising for nonlinear and nonstationary data predictions.