Search Results (81)

The barotropic sea level difference (SLD) across the Korea/Tsushima Strait (KTS) is considered an index of the volume transport into the East/Japan Sea. This study investigates the interannual variability of the barotropic SLD (the KTS inflow) from 1985 to 2017 and its relationship to upper-ocean (<300 m) current variability in the western North Pacific. An increase in the KTS inflow is associated with a weakening of the Kuroshio current through the Tokara Strait and upper-ocean cooling in the North Pacific Subtropical Gyre, characteristic of a La Niña-like state. Diagnostic analysis reveals that the KTS inflow variability is linked to at least two statistically distinct and concurrent modes of oceanic variability. The first mode is tied to the El Niño–Southern Oscillation through large-scale changes in the Kuroshio system. The second mode, which is linearly uncorrelated with the first, is associated with regional eddy kinetic energy variability in the western North Pacific. The identification of these parallel pathways suggests a complex regulatory system for the KTS inflow. This study provides a new framework for understanding the multi-faceted connection between the KTS and upstream oceanic processes, with implications for the predictability of the ocean environmental conditions in the East/Japan Sea. Full article

A comprehensive diagnosis of eddy–mean flow interaction in the Kuroshio Current (KC) region and the associated energy conversion pathway is conducted employing a state-of-the-art high-resolution global ocean–sea ice coupled model. The spatial distributions of the energy reservoirs and their conversions exhibit significant complexity. The cross-stream variation is found in the energy conversion pattern in the along-coast region, whereas a mixed positive–negative conversion pattern is observed in the off-coast region. Considering the area-integrated conversion rates between energy reservoirs, barotropic and baroclinic instabilities dominate the energy transferring from the mean flow to eddy field in the KC region. When the KC separates from the coast, it becomes highly unstable and the energy conversion rates intensify visibly; moreover, the local variations of the energy conversion are significantly influenced by the topography in the KC extension region. The mean available potential energy is the total energetic source to drive the barotropic and baroclinic energy pathway in the whole KC region, while the mean kinetic energy supplies the total energy in the extension region. For the whole KC region, the mean current transfers 84.9 GW of kinetic energy and 37.3 GW of available potential energy to the eddy field. The eddy kinetic energy is generated by mixed barotropic and baroclinic processes, amounting to 84.9 GW and 15.03 GW, respectively, indicating that topography dominates the generation of mesoscale eddy. Mean kinetic energy amounts to 11.08 GW of power from the mean available potential energy and subsequently supplies the barotropic pathway. Full article

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction. Full article

For successful economic management of cephalopod fisheries, short-lived squid with a 1-year life span require stock assessment over a short timescale, accompanied by both in-season and real-time stock management. However, insufficient information is available about the dynamic distribution patterns of the squid Uroteuthis duvauceli, Uroteuthis edulis, Loliolus sumatrensis, and Loliolus japonica in China. Such information is vital for establishing a sustainable fisheries management system. In this study, seasonal fishery-independent bottom-trawling surveys were performed from 2018 to 2019 in the southern Yellow and East China Seas to assess the seasonal changes in the distribution of these species. Most U. duvauceli occurred at sea bottom temperatures (SBTs) of 24.52–26.96 °C and sea bottom salinity (SBS) of 30.2–31.54‰ during the summer and at 20.02–22.75 °C and 33.46–34.22‰ during the autumn. Most U. edulis occurred at an SBT of 18.29–19.61 °C and SBS 34.78–35.08‰ during the spring, at 18.63–19.43 °C and 34.43–34.66‰ during the summer, at 8.78–21.81 °C and 34.39–34.77‰ during the autumn, and at 17.90–21.55 °C and 34.34–34.61‰ during the winter. Overall, most L. sumatrensis occurred at 21.00–23.15 °C and 34.11–34.50‰ in autumn. U. duvauceli concentrated in the fishing grounds of Zhoushan during the spring, Lvsi and Zhoushan-Changjiangkou during the summer, Zhoushan and Yushan-Mindong during the autumn, and Wentai-Yushan during the winter. L. sumatrensis mainly occurred in the fishing grounds of Dasha, Changjiangkou-Zhoushan, and Mindong during the summer, Yushan during the autumn, and nearshore areas during the winter. Most L. japonica occurred in the fishing grounds of Haizhou Bay and Zhoushan-Yushan during the spring, Haizhou Bay during the summer, Lvsi during the autumn, and Haizhou Bay during the winter. Our results will be useful for determining the total allowable catch from squid fisheries in these areas in China. Full article

Satellite altimetry measurements enable the resolution of ocean variability from basin-scale to mesoscale. However, the spatial resolution is still limited. The two-dimensional map from the merged data for all the available altimetry satellites can resolve mesoscale eddies down to 150 km in mid-latitudes, for example. We introduce a generative diffusion model to downscale a merged altimetry dataset, which is applied to the eddy-rich Kuroshio Extension region from 2000 to 2022. A reanalysis dataset with a high-resolution model at a horizontal scale of approximately 12 km is employed to train the diffusion model. Using the trained generative diffusion model, the merged dataset at a grid size of 1/4° is downscaled. It was demonstrated that this trained generative diffusion model outperforms the other two high-resolution reanalyses and neural-network-based datasets. The downscaled data reproduce the spatial patterns and power spectra of satellite along-track measurements. The analysis also indicates that eddy kinetic energy at horizontal scales less than 250 km has intensified by 10.14 cm²/s² (2.07%) per decade since 2004 in the Kuroshio Extension region. Our results underscore the potential of generative diffusion models in downscaling satellite altimetry datasets and improving our understanding of ocean dynamics at mesoscales. Full article

This study introduces two distinct post-processing strategies to address systematic biases in sea surface temperature (SST) numerical forecasts, thereby enhancing SST predictive accuracy. The first strategy implements a spatiotemporal four-dimensional multi-grid analysis (4D-MGA) scheme within a three-dimensional variational (3D-Var) data assimilation framework. The second strategy establishes a hybrid deep learning architecture integrating empirical orthogonal function (EOF) analysis, empirical mode decomposition (EMD), and a backpropagation (BP) neural network (designated as EE–BP). The 4D-MGA strategy dynamically corrects systematic biases through a temporally coherent extrapolation of analysis increments, leveraging its inherent capability to characterize intrinsic temporal correlations in model error evolution. In contrast, the EE–BP strategy develops a bias correction model by learning the systematic biases of the SST numerical forecasts. Utilizing a satellite fusion SST dataset, this study conducted bias correction experiments that specifically addressed the daily SST numerical forecasts with 7-day lead times in the Kuroshio region south of Japan during 2017, systematically quantifying the respective error reduction potentials of both strategies. Quantitative verification reveals that EE–BP delivers enhanced predictive skill across all forecast horizons, achieving 18.1–22.7% root–mean–square error reduction compared to 1.2–9.1% attained by 4D-MGA. This demonstrates deep learning’s unique advantage in capturing nonlinear bias evolution patterns. Full article

Marine heatwaves (MHWs) can significantly impact marine ecosystems and socio-economic systems, and their severity may increase with global warming. Nevertheless, research on the onset and decline rates of MHWs remains limited, and their historical and future variations are not yet fully understood. This study, therefore, analyzes the spatiotemporal characteristics of MHW onset and decline rates by using historical and future sea surface temperature data from OISSTv2.1 and CMIP6. The results indicate that during the historical period from 1982 to 2014, MHW onset and decline rates were higher in eddy-active mid-latitude current systems and the western tropical region but lower in subtropical gyres. A remarkably high correlation (0.94) exists between the onset and decline rates; regions with higher onset rates also tend to have higher decline rates. Approximately 49.69% of the global ocean exhibits an increasing trend in MHW onset rates, with significant increases observed in the Eastern Equatorial Pacific. Meanwhile, 92.87% of oceanic regions exhibit an increase in decline rates. Looking ahead to the future (2015~2100), both the SSP245 and SSP585 scenarios display consistent spatial patterns of MHW onset and decline rates. The Kuroshio-Oyashio Extension, Gulf Stream, Antarctic Circumpolar Current, and Brazil-Malvinas Confluence regions exhibit relatively higher onset and decline rates. Under the SSP585 scenario, both the onset and decline rates of MHWs are higher than those under the SSP245 scenario. This indicates that as global warming intensifies, more extreme MHWs are likely to occur. This finding indicates that it is necessary to pay attention to the rate of global warming when mitigating its potential impacts. Full article

Multiscale oceanic motions continuously transfer kinetic energy across various spatiotemporal scales through kinetic energy cascade. Satellite altimetry offers long-term daily ocean data at 0.25-degree resolution, enabling the analysis of energy cascades in both wavenumber and frequency domains. While energy cascade studies in the wavenumber domain are well-developed, frequency domain analyses remain limited. In this study, using 24 years of velocity data from satellite altimetry, we analyze the surface frequency-domain kinetic energy cascade primarily using the coarse-graining method. Compared to other approaches in literature, the coarse-graining approach shows superiority in diagnosing energy cascade in the frequency domain. Using this approach in the Kuroshio Extension region, we compare the spatiotemporal variability of energy cascades between the frequency and wavenumber domains. A pronounced low-frequency forward cascade, distinct from the wavenumber domain results, is identified. We propose a theory linking this low-frequency forward cascade with eddy generation through eddy–mean flow interactions. Significant variability is also observed in frequency domain energy cascades. Further analysis shows that wind forcing only plays a minor role in modulating the temporal variability of the energy cascade. Our findings are crucial for evaluating the model’s fidelity and advancing investigation of climate variability from the perspective of energy transfer. Full article

High-resolution (2 km), high-frequency (hourly) SST data of the Advanced Himawari Imager (AHI) flown onboard the Japanese Himawari-8 geostationary satellite were used to derive the monthly climatology of temperature fronts in the South China Sea. The SST data from 2015 to 2022 were processed with the Belkin–O’Reilly algorithm to generate maps of SST gradient magnitude GM. The GM maps were log-transformed to enhance contrasts in digital maps and reveal additional features (fronts). The combination of high-resolution, cloud-free, four-day-composite SST imagery from AHI, the advanced front-preserving gradient algorithm BOA, and digital contrast enhancement with the log-transformation of SST gradients allowed us to identify numerous mesoscale/submesoscale fronts (including a few fronts that have never been reported) and document their month-to-month variability and spatial patterns. The spatiotemporal variability of SST fronts was analyzed in detail in five regions: (1) In the Taiwan Strait, six fronts were identified: the China Coastal Front, Taiwan Bank Front, Changyun Ridge Front, East Penghu Channel Front, and Eastern/Western Penghu Islands fronts; (2) the Guangdong Shelf is dominated by the China Coastal Front in winter, with the eastern and western Guangdong fronts separated by the Pearl River outflow in summer; (3) Hainan Island is surrounded by upwelling fronts of various nature (wind-driven coastal and topographic) and tidal mixing fronts; in the western Beibu Gulf, the Red River Outflow Front extends southward as the Vietnam Coastal Front, while the northern Beibu Gulf features a tidal mixing front off the Guangxi coast; (4) Off SE Vietnam, the 11°N coastal upwelling gives rise to a summertime front, while the Mekong Outflow and associated front extend seasonally toward Cape Camau, close to the Gulf of Thailand Entrance Front; (5) In the Luzon Strait, the Kuroshio Front manifests as a chain of three fronts across the Babuyan Islands, while west of Luzon Island a broad offshore frontal zone persists in winter. The summertime eastward jet (SEJ) off SE Vietnam is documented from five-day mean SST data. The SEJ emerges in June–September off the 11°N coastal upwelling center and extends up to 114°E. The zonally oriented SEJ is observed to be located between two large gyres, each about 300 km in diameter. Full article

This research evaluates the potential spaces of deep offshore waters for cultivating the Larimichthys crocea, analyzing ocean profile temperature data from 2000 to 2022 according to the species’ environmental temperature suitability. There are significant seasonal variations and differences in habitat distributions of different temperature ranges in China’s surrounding waters. The range of maximum living space obtained according to the tolerance temperature shows a trend of being larger in summer and smaller in winter; and the range of viable habitat space obtained based on the suitable and optimal temperature shows a trend of being smaller in summer and larger in winter. Broad areas meeting tolerance temperatures offer broad, yet impractical, site selection options. In contrast, areas with optimal temperatures are limited, which means the availability of ideal site locations is very restricted. Regions consistently within the 20–28 °C range are best for practical site selection. Year-round suitable areas are primarily found at depths of 30 to 90 m in the southern East China Sea and the South China Sea, particularly within the 40 to 50 m depth range. Water mass like the South China Sea Surface Water and the Kuroshio Surface Water consistently maintain suitable temperatures, making them ideal for aquaculture. Full article

The subsurface chlorophyll maximum depth (SCMD) is an indicator of the spatial activity of marine organisms and changes in the ecological environment. Ubiquitous mesoscale eddies are among the important factors regulating the Kuroshio–Oyashio confluence region. In this study, we use satellite altimeter observations and high-resolution reanalysis data to explore seasonal variations in the SCMD and its responses to different types of eddies based on methods of composite averaging and normalization. The results show that variations in the SCMD induced by the evolution of the eddies were prominent in the summer and autumn. The monopoles of the SCMD exhibited internally shallow and externally deep features in the cyclonic eddies (CEs), while the contrary trend was observed in the anticyclonic eddies (ACEs). The SCMD was positively correlated with the intensity of the eddies and sea surface temperature, and was negatively correlated with the depth of the mixed layer. These correlations were more pronounced in the CEs (summer) and ACEs (autumn). Both the CEs and ACEs prompted the westward transport of chlorophyll-a (Chl-A), where ACEs transported it over a longer distance than the CEs. Full article

This study focuses on typical regions of strong ageostrophic processes in the Kuroshio using high-resolution remote sensing satellite reanalysis data and Argo float data. By analyzing the relationship between the Rossby number and chlorophyll concentration from June to August in the summer of 2020, the spatial characteristics of ageostrophic processes and their impact on the phytoplankton community distribution are explored. The results indicate that ageostrophic processes, driven by coastal topography, are stably generated in the regions of the Bashi Channel, northeastern Taiwan waters, southwestern Kyushu Island, and southern Shikoku Island. Furthermore, the intensity of these ageostrophic processes shows an overall positive correlation with chlorophyll concentration. The local mixing and subfront circulations induced by ageostrophic processes pump deep nutrients into the euphotic zone, supporting the growth and reproduction of phytoplankton, which leads to the formation of significant chlorophyll hotspots in regions controlled by ageostrophic processes. Full article

Oceanic mesoscale eddies are a kind of typical geostrophic dynamic process which can cause vertical movement in water bodies, thereby changing the temperature, salinity, density, and chlorophyll concentration of the surface water in the eddy. Based on multisource remote sensing data and Argo profiles, this study analyzes and compares the mesoscale eddy properties in four major western boundary current regions (WBCs), i.e., the Kuroshio Extension (KE), the Gulf Stream (GS), the Agulhas Current (AC), and the Brazil Current (BC). The 30-year sea surface height anomaly (SSHA) data are used to identify mesoscale eddies in the four WBCs. Among the four WBCs, the GS eddies have the largest amplitude and the BC eddies have the smallest amplitude. Combining the altimeter-detected eddy results with the simultaneous observations of sea surface temperature, sea surface salinity, sea surface density, and chlorophyll concentration, the local impacts of eddy activities in each WBCs are analyzed. The eddy surface temperature and salinity signals are positively correlated with the eddy SSHA signals, while the eddy surface density and chlorophyll concentrations are negatively correlated with eddy SSHA signals. The correlation analysis of eddy surface signals in the WBCs reveals that eddies have regional differences in the surface signal changes of eddy activities. Based on the subsurface temperature and salinity information provided by Argo profiles, the analysis of the vertical thermohaline characteristics of mesoscale eddies in the four WBCs is carried out. Eddies in the four WBCs have deep influence on the vertical thermohaline characteristics of water masses, which is not only related to the strong eddy activities but also to the thick thermocline and halocline of water masses in the WBCs. Full article

Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions. Full article

A comprehensive analysis was carried out to investigate the driving factors and influencing mechanisms of spatiotemporal variation of sea level at multiple scales in the East China Sea (ECS) via satellite altimetry datasets from 1993 to 2020. Based on the altimetry grid data processed by the local mean decomposition method, the spatiotemporal changes of ECS sea level are analyzed from the multi-scale perspective in terms of multi-year, seasonal, interannual, and multi-modal scales. The results revealed that the ECS regional mean sea level change rate is 3.41 ± 0.58 mm/year over the 28-year period. On the seasonal scale, the regional mean sea level change rates are 3.45 ± 0.66 mm/year, 3.35 ± 0.60 mm/year, 3.39 ± 0.71 mm/year, and 3.57 ± 0.75 mm/year, for the four seasons (i.e., spring, summer, autumn, and winter) respectively. The spatial distribution analysis showed that ECS sea level changes are most pronounced in coastal areas. The northeast sea area of Taiwan and the edge of the East China Sea shelf are important areas of mesoscale eddy activity, which have an important impact on regional sea level change. The ECS seasonal sea level change is mainly affected by monsoons, precipitation, and temperature changes. The spatial distribution analysis indicated that the impact factors, including seawater thermal expansion, monsoons, ENSO, and the Kuroshio Current, dominated the ECS seasonal sea level change. Additionally, the ENSO and Kuroshio Current collectively affect the spatial distribution characteristics. Additionally, the empirical orthogonal function was employed to analyze the three modes of ECS regional sea level change, with the first three modes contributing 26.37%, 12.32%, and 10.47%, respectively. Spatially, the first mode mainly corresponds to ENSO index, whereas the second and third modes are linked to seasonal factors, and exhibit antiphase effects. The analyzed correlations between the ECS sea level change and southern oscillation index (SOI), revealed the consistent spatial characteristics between the regions affected by ENSO and those by the Kuroshio Current. Full article