One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpredictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigating the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC.

Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R²) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data.

The Yellow Sea is one of the most productive continental shelves in the world. This large marine ecosystem is experiencing an epochal change in water temperature, stratification, nutrients, and subsequently in ecological diversity. Research-oriented monitoring of these changes requires a sustainable, multi-disciplinary approach. For this purpose, the Korea Institute of Ocean Science and Technology (KIOST) constructed the Socheongcho Ocean Research Station (S-ORS), a steel-framed tower-type platform, in the central Yellow Sea about 50 km off the western coast of the Korean Peninsula. This station is equipped with about forty sensors for interdisciplinary oceanographic observations. Since its construction in 2014, this station has continuously conducted scientific observations and provided qualified time series: physical oceanographic variables such as temperature, salinity, sea level pressure, wave, and current; biogeochemical variables such as chlorophyll-a, photosynthetically active radiation, and total suspended particles; atmospheric variables including air temperature, wind, greenhouse gasses, and air particles including black carbon. A prime advantage is that this platform has provided stable facilities including a wet lab where scientists can stay and experiment on in situ water samples. Several studies are in process to understand and characterize the evolution of environmental signals, including air-sea interaction, marine ecosystems, wave detection, and total suspended particles in the central Yellow Sea. This paper provides an overview of the research facilities, maintenance, observations, scientific achievements, and next steps of the S-ORS with highlighting this station as an open lab for interdisciplinary collaboration on multiscale process studies.

Marine heatwaves (MHWs) are among the most severe climatic events under global warming conditions; they damage marine ecosystems and result in socioeconomic losses. In East Asia, the sea surface temperature (SST) has increased significantly and is projected to increase further. Considering such increases in SST over East Asia, studies on MHWs are needed to minimize the damage caused. In this study, we classified the spatiotemporal characteristics of East Asian MHWs during boreal summer with a self-organizing map for 39 years (1982–2020) and determined their unexplored possible mechanisms. Four dominant modes of MHWs across East Asia were identified: the 1) Subtropical Gyre, 2) East China Sea, 3) East Sea/Japan Sea, and 4) Yellow Sea modes. The total annual number of MHW days has increased by about 1.23 days yr⁻¹. The significant trend is mainly determined by the Subtropical Gyre mode, whereas other modes do not exhibit significant trends. We found that the East China Sea mode could be attributed to a strong East Asian summer monsoon via ocean stratification by the low-salinity water from the Yangtze (Changjiang) River. The East Sea/Japan Sea and Yellow Sea modes could be linked to remote forcings over the subtropics, leading to persistent anticyclonic circulation over East Asia. The anomalous anticyclone contributed to the MHW occurrences by enhancing downward shortwave radiation and reducing entrainment cooling caused by Ekman downwelling. These findings have important implications for distinguishing the characteristics of East Asian MHWs and reveal the possibility of predicting them by identifying their precursors. Significance Statement: This study aimed to better understand East Asian marine heatwaves (MHWs) and their causes. We utilized a self-organizing map to classify four patterns of MHWs during boreal summer across East Asia—the Subtropical Gyre, East China Sea, East Sea/Japan Sea, and Yellow Sea modes. We found that enhanced shortwave radiation and latent heat flux play an important role in modulating MHWs in East Asia. A reduced vertical-mixing-amplified ocean warming, and the ocean dynamics drove different temporal evolutions of the MHWs among the four modes. We also identified the potential precursors, which would help in improving the future prediction of MHWs in East Asia.

Although 2020 was the fourth warmest year on record in northern Asia, the cold condition in April 2020 caused severe damage to the agricultural and marine ecosystems in northeastern Asia. Previous studies have indicated that the dipole atmospheric circulation over Siberia and the East Sea (Japan Sea) produced this cold environment with strong northwesterly wind. However, the potential causes of the dipole circulation over northeastern Asia remain unclear. In this study, we found that the East Atlantic/Western Russia (EAWR) pattern and blocking combined to produce the atmospheric structure. The wave train originated from the vorticity forcing of northwestern/central Russia and propagated from Western Europe to the East Sea via the background westerly and northerly winds. In addition, the Siberian blocking days increased eleven times in April 2020 relative to the climatological average, and an easterly (westerly) anomaly was observed over Mongolia–northeastern China (northern Russia). The strong blocking and EAWR pattern led to the robust atmospheric dipole structure with a prevailing northerly flow in April 2020, thereby causing the extreme cold condition over northeastern Asia. Our results provide novel insights into the cause of extreme cold condition in April over northeastern Asia.

In 2023, the global temperature rose significantly, triggering a marine heatwave (MHW) in the East China Sea (ECS) from August 12 to October 13. Unlike the typical July onset, this event was delayed. Here we investigate the mechanisms behind the late onset and prolonged duration of the 2023 MHW in the ECS and its potential linkage to global warming. The early phase was driven by shortwave radiation and ocean dynamics, while the later phase saw reduced northerly winds, allowing warmer, more humid southerlies to dominate. This shift decreased the air-sea humidity difference, enhancing downward latent heat fluxes. Future CMIP6 projections also suggest that delayed MHWs are linked to weak heat exchanges, limiting the oceanic heat transport to the atmosphere. Our findings highlight the increasing risk of prolonged MHWs in the ECS and the urgent need for improved climate preparedness.