Prediction of regional sea level change and its components using machine learning methods

Regional sea level prediction plays a vital role in understanding the impacts of climate change and guiding the design of coastal infrastructure. Sea level rise is mainly driven by two primary factors: barystatic sea level change, caused by the melting of ice sheets, glaciers, and run-off of terrestrial water, and by steric sea level change, resulting from the expansion of seawater due to temperature and salinity changes. The former can be monitored from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, and the latter is commonly calculated based on ocean salinity and temperature models. In this study, satellite altimetry was used to observe relative sea level changes spanning from May 2002 to April 2023. Specifically, barystatic sea level changes were derived using Mass Concentration (Mascon) solutions, while the steric height was estimated through the Ocean Physics Reanalysis model. According to the sea level budget equation, the total sea level change aligns closely with the combined contributions of barystatic and steric sea level components, validating the consistency of the data and methodology.

Machine learning has become increasingly significant in climate research in recent years. It enables the analysis of large and complex datasets that exceed human processing capabilities. Among the machine learning techniques, the Long Short-Term Memory (LSTM) model is particularly effective for the time series prediction due to its ability to capture long-term dependencies and patterns through its gated mechanisms. LSTM models excel at handling trends, seasonality, and noise in data, making them ideal for understanding the temporal dynamics of sea level changes and predicting future values.

In this research, we applied a CNN-LSTM model to predict total, barystatic, and steric sea level changes. The model leverages the feature extraction capabilities of convolutional neural networks (CNNs) combined with the sequential learning strengths of LSTM. The results of this study provide valuable insights into the contributions of mass and steric components to regional sea level changes. By predicting these signals, this research enhances our understanding of the mechanisms driving the sea level rise, offering the critical information for climate change mitigation and coastal adaptation planning.