Acta Oceanologica Sinica最新文献

The wave-induced liquefaction of seabed is responsible for causing damage to marine structures. Particle composition and consolidation degree are the key factors affecting the pore water pressure response and liquefaction behavior of the seabed under wave action. The present study conducted wave flume experiments on silt and silty fine sand beds with varying particle compositions. Furthermore, a comprehensive analysis of the differences and underlying reasons for liquefaction behavior in two different types of soil was conducted from both macroscopic and microscopic perspectives. The experimental results indicate that the silt bed necessitates a lower wave load intensity to attain the liquefaction state in comparison to the silty fine sand bed. Additionally, the duration and development depth of liquefaction are greater in the silt bed. The dissimilarity in liquefaction behavior between the two types of soil can be attributed to the variation in their permeability and plastic deformation capacity. The permeability coefficient and compression modulus of silt are lower than those of silty fine sand. Consequently, silt is more prone to the accumulation of pore pressure and subsequent liquefaction under external loading. Prior research has demonstrated that silt beds with varying consolidation degrees exhibit distinct initial failure modes. Specifically, a dense bed undergoes shear failure, whereas a loose bed experiences initial liquefaction failure. This study utilized discrete element simulation to examine the microscopic mechanisms that underlie this phenomenon.

The Indonesian Throughflow (ITF), which connects the tropical Pacific and Indian oceans, plays important roles in the inter-ocean water exchange and regional or even global climate variability. The Makassar Strait is the main inflow passage of the ITF, carrying about 77% of the total ITF volume transport. In this study, we analyze the simulated ITF in the Makassar Strait in the Simple Ocean Data Assimilation version 3 (SODA3) datasets. A total of nine ensemble members of the SODA3 datasets, of which are driven by different surface forcings and bulk formulas, and with or without data assimilation, are used in this study. The annual mean water transports (i.e., volume, heat and freshwater) are related to the combination of surface forcing and bulk formula, as well as whether data assimilation is employed. The phases of the seasonal and interannual variability in water transports cross the Makassar Strait, are basically consistent with each other among the SODA3 ensemble members. The interannual variability in Makassar Strait volume and heat transports are significantly correlated with El Niño-Southern Oscillation (ENSO) at time lags of −6 to 7 months. There is no statistically significant correlation between the freshwater transport and the ENSO. The Makassar Strait water transports are not significantly correlated with the Indian Ocean Dipole (IOD), which may attribute to model deficiency in simulating the propagation of semiannual Kelvin waves from the Indian Ocean to the Makassar Strait.

In recent years, there has been a significant acceleration in the thinning, calving and retreat of the Pine Island Ice Shelf (PIIS). The basal channels, results of enhanced basal melting, have the potential to significantly impact the stability of the PIIS. In this study, we used a variety of remote sensing data, including Landsat, REMA DEM, ICESat-1 and ICESat-2 satellite altimetry observations, and IceBridge airborne measurements, to study the spatiotemporal changes in the basal channels from 2003 to 2020 and basal melt rate from 2010 to 2017 of the PIIS under the Eulerian framework. We found that the basal channels are highly developed in the PIIS, with a total length exceeding 450 km. Most of the basal channels are ocean-sourced or groundingline-sourced basal channels, caused by the rapid melting under the ice shelf or near the groundingline. A raised seabed prevented warm water intrusion into the eastern branch of the PIIS, resulting in a lower basal melt rate in that area. In contrast, a deep-sea trough facilitates warm seawater into the mainstream and the western branch of the PIIS, resulting in a higher basal melt rate in the main-stream, and the surface elevation changes above the basal channels of the mainstream and western branch are more significant. The El Niño event in 2015–2016 possibly slowed down the basal melting of the PIIS by modulating wind field, surface sea temperature and depth seawater temperature. Ocean and atmospheric changes were driven by El Niño, which can further explain and confirm the changes in the basal melting of the PIIS.

An obvious trend shift in the annual mean and winter mixed layer depth (MLD) in the Antarctic Circumpolar Current (ACC) region was detected during the 1960–2021 period. Shallowing trends stopped in mid-1980s, followed by a period of weak trends. The MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage, the areas north to Victoria Land and Wilkes Land, and the central parts of the South Indian sector. The newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian sector. The saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.

Upper ocean heat content (OHC) has been widely recognized as a crucial precursor to high-impact climate variability, especially for that being indispensable to the long-term memory of the ocean. Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts. Recently developed retrospective forecast experiments, based on a Community Earth System Model ensemble prediction system, offer a great opportunity to comprehensively explore OHC predictability. Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends. The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill, indicating that the persistence of OHC serves as the primary predictive signal for its predictability. The decline in actual prediction skill is more pronounced in the Indian and Atlantic oceans than in the Pacific Ocean, particularly within tropical regions. Additionally, notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability. The potential predictability of OHC generally surpasses the actual prediction skill at all lead times, highlighting significant room for improvement in current OHC predictions, especially for the North Indian Ocean and the Atlantic Ocean. Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations, develop effective data assimilation methods, and reduce model bias.

The classification of the springtime water mass has an important influence on the hydrography, regional climate change and fishery in the Taiwan Strait. Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019, we analyze the spatial distributions of temperature (T) and salinity (S) in the investigation area. Then by using the fuzzy cluster method combined with the T-S similarity number, we classify the investigation area into 5 water masses: the Minzhe Coastal Water (MZCW), the Taiwan Strait Mixed Water (TSMW), the South China Sea Surface Water (SCSSW), the South China Sea Subsurface Water (SCSUW) and the Kuroshio Branch Water (KBW). The MZCW appears in the near surface layer along the western coast of Taiwan Strait, showing low-salinity (<32.0) tongues near the Minjiang River Estuary and the Xiamen Bay mouth. The TSMW covers most upper layer of the investigation area. The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait, beneath which is the SCSUW. The KBW is a high temperature (core value of 26.36°C) and high salinity (core value of 34.62) water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.

With the improvements in the density and quality of satellite altimetry data, a high-precision and high-resolution mean sea surface model containing abundant information regarding a marine gravity field can be calculated from long-time series multi-satellite altimeter data. Therefore, in this study, a method was proposed for determining marine gravity anomalies from a mean sea surface model. Taking the Gulf of Mexico (15°–32°N, 80°–100°W) as the study area and using a removal-recovery method, the residual gridded deflections of the vertical (DOVs) are calculated by combining the mean sea surface, mean dynamic topography, and XGM2019e_2159 geoid, and then using the inverse Vening-Meinesz method to determine the residual marine gravity anomalies from the residual gridded DOVs. Finally, residual gravity anomalies are added to the XGM2019e_2159 gravity anomalies to derive marine gravity anomaly models. In this study, the marine gravity anomalies were estimated with mean sea surface models CNES_CLS15MSS, DTU21MSS, and SDUST2020MSS and the mean dynamic topography models CNES_CLS18MDT and DTU22MDT. The accuracy of the marine gravity anomalies derived by the mean sea surface model was assessed based on ship-borne gravity data. The results show that the difference between the gravity anomalies derived by DTU21MSS and CNES_CLS18MDT and those of the ship-borne gravity data is optimal. With an increase in the distance from the coast, the difference between the gravity anomalies derived by mean sea surface models and ship-borne gravity data gradually decreases. The accuracy of the difference between the gravity anomalies derived by mean sea surface models and those from ship-borne gravity data are optimal at a depth of 3–4 km. The accuracy of the gravity anomalies derived by the mean sea surface model is high.

An analysis of a 68-year monthly hindcast output from an eddy-resolving ocean general circulation model reveals the relationship between the interannual variability of the Kerama Gap transport (KGT) and the Kuroshio/Ryukyu Current system. The study found a significant difference in the interannual variability of the upstream and downstream transports of the East China Sea- (ECS-) Kuroshio and the Ryukyu Current. The interannual variability of the KGT was found to be of paramount importance in causing the differences between the upstream and downstream ECS-Kuroshio. Additionally, it contributed approximately 37% to the variability of the Ryukyu Current. The interannual variability of the KGT was well described by a two-layer rotating hydraulic theory. It was dominated by its subsurface-intensified flow core, and the upper layer transport made a weaker negative contribution to the total KGT. The subsurface flow core was found to be mainly driven by the subsurface pressure head across the Kerama Gap, and the pressure head was further dominated by the subsurface density anomalies on the Pacific side. These density anomalies could be traced back to the eastern open ocean, and their propagation speed was estimated to be about 7.4 km/d, which is consistent with the speed of the local first-order baroclinic Rossby wave. When the negative (positive) density anomaly signal reached the southern region of the Kerama Gap, it triggered the increase (decrease) of the KGT towards the Pacific side and the formation of an anticyclonic (cyclonic) vortex by baroclinic adjustment. Meanwhile, there is an increase (decrease) in the upstream transport of the entire Kuroshio/Ryukyu Current system and an offshore flow that decreases (increases) the downstream Ryukyu Current.

This study aims to investigate characteristics of continental shelf wave (CSW) on the northwestern continental shelf of the South China Sea (SCS) induced by winter storms in 2021. Mooring and cruise observations, tidal gauge data at stations Hong Kong, Zhapo and Qinglan and sea surface wind data from January 1 to February 28, 2021 are used to examine the relationship between along-shelf wind and sea level fluctuation. Two events of CSWs driven by the along-shelf sea surface wind are detected from wavelet spectra of tidal gauge data. The signals are triply peaked at periods of 56 h, 94 h and 180 h, propagating along the coast with phase speed ranging from 6.9 m/s to 18.9 m/s. The dispersion relation shows their property of the Kelvin mode of CSW. We develop a simple method to estimate amplitude of sea surface fluctuation by along-shelf wind. The results are comparable with the observation data, suggesting it is effective. The mode 2 CSWs fits very well with the mooring current velocity data. The results from rare current help to understand wave-current interaction in the northwestern SCS.

Sea fog is a disastrous weather phenomenon, posing a risk to the safety of maritime transportation. Dense sea fogs reduce visibility at sea and have frequently caused ship collisions. This study used a geographically weighted regression (GWR) model to explore the spatial non-stationarity of near-miss collision risk, as detected by a vessel conflict ranking operator (VCRO) model from automatic identification system (AIS) data under the influence of sea fog in the Bohai Sea. Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data. The spatial distributions of near-miss collision risk, sea fog, and the parameters of GWR were mapped. The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea, in which near-miss collision risk in the fog season is significantly higher than that outside the fog season, especially in the northeast (the sea area near Yingkou Port and Bayuquan Port) and the southeast (the sea area near Yantai Port). GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season, with higher R-squared (0.890 in fog season, 2018), than outside the fog season (0.723 in non-fog season, 2018). GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally. Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.