Sea surface temperature (SST) is a vital component of the climate system, and its spatiotemporal variations significantly influence global climate and ecological equilibrium. Unlike most existing univariate SST prediction models that neglect atmospheric forcing information, this study proposes a machine learning-driven multivariate framework integrating key meteorological variables to learn data-driven, nonlinear relationships...

Interannual variability of sea level anomalies (SLA) in the South China Sea (SCS) is significantly influenced by large-scale climate modes; however, their temporal evolution and interdecadal modulation mechanisms remain insufficiently understood. Based on observational records and ERA5 reanalysis data spanning 1980–2022, this study employs a Bayesian Dynamic Linear Model (DLM) to quantify the time-varying impacts of El Niño-So...