Using newly available satellite observations of Sea Surface Salinity (SSS), we provide, for the first time, a detailed and synoptic view of the spatiotemporal variability of SSS in the South China Sea (SCS). The results depict the SCS as a very dynamic region exhibiting variability over a broad range of time scales, from intra‐seasonal to inter‐annual, with the seasonal cycle dominating (∼47% of the total SSS variance). The seasonal distribution of SSS has considerable latitudinal variations: the strongest variance across the southern SCS (∼5‐12^oN), weaker in the northern part of the sea (north of ∼18^oN), with the weakest seasonal SSS variability in between. The factors controlling patterns of seasonal SSS distribution are closely related to both the external freshwater forcing and ocean processes over the entire SCS monsoon system. The most active inter‐annual SSS variability is found in the northeastern and eastern parts of the SCS, as well as the adjacent western Pacific. A significant basin‐wide salinification began in summer of 2015, peaked in spring of 2016 with the averaged amplitude of up to 0.5 PSU, and maintained until the fall of 2016. Such persistent salinification during 2015‐2016 following a strong El Nino event can be largely modulated by El Nino‐related atmospheric and oceanic dynamics. The intra‐seasonal variability was found to be surprisingly weak throughout the SCS (the standard deviation < 0.2 PSU), except for a few regions near the coast where it is likely related to the intra‐seasonal variability in the monsoon rainfall and subsequent variations in river runoff.

The decadal variability of the North Pacific gyre oscillation (NPGO) over the 20^th century is examined from a long‐term integration of the Simple Ocean Data Assimilation (SODA) reanalysis. The NPGO is reflected by the second dominant pattern of sea surface height (SSH) variability in SODA, with a north‐south dipole structure over the northeast Pacific. SSH anomalies in this region exhibit distinct decadal variability with a significant spectrum peak at approximately 18 years. The upper‐ocean heat budget reveals that this dipole structure associated with the NPGO is predominantly due to the anomalous Ekman pumping and Ekman advection induced by the surface wind. The NPGO mode in SODA reanalysis originates from atmosphere stochastic noise (North Pacific Oscillation) which has a meridional dipole pattern but no preferred time scale. The oceanic planetary wave, particularly the advective baroclinic mode, integration of atmospheric stochastic noise leads to a spatial resonance with preferred decadal time scale. The limitation of current study is also discussed.

The area of Arctic winter sea ice growth (WSIG) has expanded dramatically since winter 2008. Yet the thermodynamic and dynamic contributions to the abrupt increase in WSIG remain unclear. Here using an ice concentration budget, we characterized quantitatively the increasing WSIG and revealed the relative contributions of dynamics during 1985–2021. Ice dynamics related to ice convergence/divergence are compared in two representative regions. The northern Laptev Sea is a freezing-dominated ice growth region and is competitively driven by the ice convergence. While in northwest Beaufort Gyre (BG), the combined effects of freezing and ice divergence have both enhanced since 2008, and the dynamics contribute 84% to the significant WSIG intensification since 2008. Comparison of thermodynamic and dynamic contributions emphasized that the winter sea-ice expansion is influenced not only by winter freeze, but also by convergence/divergence relative to newly formed thinner and mobile ice. Furthermore, the amplified summer Beaufort High in the mid-2000s and its long-lasting memory of the wind-driven strengthened BG are partially attributed to the abrupt increased WSIG since 2008.

In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, and evaluating their impacts on ecosystems. While previous studies have largely concentrated on the effects of various modeling approaches on reconstructing oceanic variables, limited attention has been paid to the role of surface waves in reconstruction. This study, based on sea surface data, employs a deep learning-based neural network model, U-Net, to reconstruct 3D temperature and salinity across the North Pacific and Equatorial Pacific within the upper 200 m. The input of wave information includes the significant wave height (SWH), Langmuir number (La), and Langmuir enhancement factor (ε); the latter two indicate the strength of Langmuir turbulence, which promotes vertical mixing in the ocean surface layer and thereby affects profiles of temperature and salinity. The results indicate that incorporating wave information, particularly the La and ε, significantly enhances the model’s ability to reconstruct ocean temperature and salinity. This highlights the critical role of surface waves in enhancing the reconstruction of 3D ocean temperature and salinity.

The Kara–Laptev Seas (KLS), known as the ‘Ice Factory of the Arctic’, witnesses rising instead of falling winter sea ice growth (WSIG) under the shrinkage of Arctic ice. However, knowledge of the large year-to-year variation is still unclear. Combining a seasonal ice concentration budget, a composite analysis, and a typical case study, we study both the interannual variability of WSIG in the KLS and the associated air-sea forcings during 1985–2021. Results quantitatively reveal that, during 1985–2021, thermodynamic melt in the melting season (April–August) contributed 80.3% to the interannual ice loss difference and promoted the subsequent WSIG by the recovery mechanism in the KLS. This consistent thermodynamic melt is caused by the strengthened summer Beaufort High, transporting heat and introducing a locally positive ice-albedo feedback. However, since 2010, the dynamic growth during the freezing season (October–February) has increasingly stimulated the WSIG. Typical cases in 2013 and 2017 indicate that the overlying anticyclonic atmospheric regime restricts the ice drift from the KLS and contributes to the dynamic growth of 41.6% of the WSIG difference, while the turbulent-heat-induced thermodynamic growth in winter is down to 58.4%. In short, we reveal an unstable relationship between the summer ice loss and the subsequent WSIG under the background of Arctic warming. Our study points out that the distinct dynamic ice growth driven by surface winds or ocean currents during the freezing season is likely to increase in the near future, with thinner and more mobile seasonal ice predominating in the Arctic.

Ocean temperature and salinity are core elements influencing ocean dynamics and biogeochemical cycles, critical to climate change and ocean process studies. In recent years, Argo floats and satellite remote sensing data have provided key support for observing and reconstructing three-dimensional (3D) ocean temperature and salinity. However, due to the challenges and high costs of in situ observations and the limitation of satellite measurements to surface data, effectively combining multi-source data to enhance the reconstruction accuracy of 3D temperature and salinity remains a significant challenge. In this study, we propose a VI-UNet model that incorporates a Vision Transformer module into UNet model and apply it to reconstruct 3D temperature and salinity in the equatorial oceans (20°S–20°N, 20°E–60°W) at depths from 1 to 6000 m using sea surface data acquired by satellites. In addition, we also investigate the impact of incorporating significant wave height (SWH) on the reconstruction of temperature and salinity. The results demonstrate that the VI-UNet model performs remarkably well in reconstructing temperature and salinity, achieving maximum reductions in root mean square error (RMSE) of up to 40% and 100%, respectively. Additionally, incorporating SWH enhances model accuracy, particularly in the upper 1000 m.