Yellowfin tuna (Thunnus albacares) and skipjack tuna (Katsuwonus pelamis) are pelagic and highly migratory species, serving as primary targets in global pelagic fisheries. Their population distribution and abundance are susceptible to the impacts of climate-induced changes in the marine environment, exhibiting a response lag. In order to explore the influence of climate change on the juvenile populations of yellowfin tuna and skipjack tuna in the western and central Pacific Ocean (WCPO) and the associated lag effects, this study, based on Long Short-Term Memory (LSTM) neural networks, analyzed the impact of the Oceanic Niño index (ONI) on the Catch per Unit Effort (CPUE) of yellowfin tuna and skipjack tuna in the WCPO purse seine fishery from 1982 to 2021. Different time step lengths were employed to simulate the lag effects (0−12 months) of CPUE response to ONI. The results indicate LSTM is a suitable tool for analyzing the lag effects of relationship between the abundance of pelagic species, such as yellowfin tuna and skipjack tuna, and environmental factors like ONI. In the WCPO regions north and south of the equator, there exists a time lag in the response of juvenile yellowfin tuna and skipjack tuna CPUE to ONI, with the optimal lag period being 12 months for each region. The correspondence of the optimal lag period with the age of the harvested population (nearly 1 year) suggests that the reproductive capacity or survival rate of juvenile yellowfin tuna and skipjack tuna is influenced by climate change and the resulting changes in the marine environment. The research methodology and results provide new insights for subsequent studies in analyzing the stock dynamics and distribution of key species in the WCPO.

The crown-of-thorns starfish (CoTS) outbreak has caused severe damage to coral reefs in China and the Indo-Pacific region. Fish predators have been considered as an important factor in controlling the population outbreak, but there is a lack of research on the fish that can prey on crown-of-thorns starfish in China. By collecting coral reef fish from five outbreak areas of crown-of-thorns starfish in the Xisha Islands, CoTS DNA was detectedin the intestinal contents of the reef fish using PCR technology, and which compared with reported CoTS larval predators. The results showed that a total of 62 fish belonging to 23 families, 36 genera and 50 species were captured in this survey. CoTS DNA was detected in the gut contents of four fish species, namely Naso lituratus, Dascyllus reticulatus, Chelilinus trilobatus and Lethrinus erythropterus. Among them, N. lituratus, C. trilobatus and L. erythropterusare reported for the first time as potential predators of CoTS. This study has identified the potential predators of CoTS for the first time in China, providing important references for the development of early prevention and control techniques for crown-of-thorns starfish.

There are strong exchange of matter and energy and complex dynamic processes between mesoscale eddies and submesoscale fronts at their edges. At present, the investigation of mesoscale eddies has become more and more mature, but because of the small spatial scale and rapid time change of the submesoscale front, it is a great challenge to investigate its three-dimensional structure. In this paper, a new method is proposed to investigate the submesoscale front at the edge of the ocean mesoscale eddy. Firstly, the submesoscale front at the edge of the mesoscale eddies is identified using multi-source satellite remote sensing data, and then the multi-type shipborne survey equipment is used for multidisciplinary investigation. A submesoscale front at the edge of typical eddies in the Kuroshio Extension area from August 21 to August 25, 2023 is investigated by using this method. The survey method proposed in this paper can effectively identify, track and investigate the submesoscale front at the edge of mesoscale eddies.

This study is based on the meteorological, oceanic, terrain and other physical quantity data covered by the observation points in the southern Zhoushan Islands from January 1, 2019 to December 31, 2021, and uses long short-term memory neural network (LSTM) to build deep learning wave forecast model. We explore the impact of the input-output sequence ratio and the number of input elements on the prediction performance of the model, realize the short-term forecast of the three elements of waves in the Zhoushan sea area, that is the significant wave height, the significant wave period and the propagation direction, and use the data during the 2022 typhoons “Hinnamnor” and “Muifa” to test the model’s prediction ability for extreme sea conditions. The research results show that the multi-element deep learning wave forecast model trained based on measured data has good prediction accuracy and stability, and can realize the prediction of extreme sea conditions. When the input-output sequence ratio is 1∶1, the model accuracy is higher. In non-extreme sea conditions, the three-element model with a prediction time of 1 hour accurately predicts significant wave height, significant wave period and direction, with Root Mean Squared Errors (RMSE) of 0.116 m, 0.569 s, and 24.583° respectively. In extreme sea conditions, the prediction RMSE for the significant wave height is 0.191 m. The increase in the number of input elements can further improve the model accuracy but also increase the training cost when the prediction time is long.

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was conducted from October 2019 to September 2020, yielding complete observations of atmosphere, ocean, sea ice thickness (SIT), and snow thickness. These observations provide new opportunities for the development of sea ice models. In this study, the seasonal evolution of SIT during MOSAiC was simulated using the ICEPACK sea ice model and atmospheric and oceanic forcing observations from two periods without missing data (from November 1, 2019 to May 7, 2020; from June 26 to July 27, 2020). The simulation was compared with SIT observation and the reasons for SIT simulation errors were diagnosed. The results show that, in the winter and spring seasons, the model can reproduce the increase in SIT, but overestimates the transition from submerged snow to sea ice and its contribution to sea ice mass balance. This causes the overestimation of SIT in spring. During the summer season, the combination of two thermodynamic schemes and three melt pond schemes indicates that the model overestimates the sea ice surface melting, resulting in thinner SIT at the end of simulation period. Our research demonstrates that the MOSAiC atmospheric and oceanic observation with all variables needed to force ICEPACK can be used to diagnose current sea ice models and very useful for their future improvements.

Strength is one of the key properties effect the interaction between sea ice and structures. Brine pockets and air bubbles in sea ice have important effects on the strength of sea ice. In order to explore the effects of ice pore structure such as porosity, shape and size distribution on the mechanical properties of sea ice from a microscopic perspective, a numerical sea ice model including pores was established based on the discrete element method to simulate the uniaxial compression process under brittle failure of granular ice in the directions of horizontal and vertical to ice surface. In the numerical simulation, the pore size was set to conform to uniform distribution, standard normal distribution, and Gamma distribution. Results show that porosity is the main factor affecting the strength of sea ice, and sea ice uniaxial compressive strength and elastic modulus decrease with the increase of porosity. When the compressive stress reaches extreme value, the cracks in sea ice develop rapidly. The cracks around circular pores develop mainly along the loading direction, and thus, the final failure of horizontally loaded ice samples exhibits large cracks. While the cracks around elliptic pores are easy to develop into crack band.When sea ice porosity is the same, the types of pore size distributions and locations of pores in ice have little effect on the uniaxial compressive strength and elastic modulus, but the development modes of cracks in sea ice are affected.

In this paper, the correction model of CMA-GFS numerical model wind field is constructed based on the deep learning U-Net network, and the construction of the quasi-real-time sea surface wind field is rapidly accomplished by interpolation method using the corrected wind field with the correction model as the background field (CMA-GFS_Unet), and using the scatterometer sea surface wind data from the four satellites, namely, HY-2B/2C/2D and MetOp-B as the observation data. This intelligent algorithm can realize the generation of global sea surface fusion wind field (Fusion_QRT) with a spatial resolution of 0.25° and a temporal resolution of 6 hours in quasi-real time with a lag of 3 hours. The CMA-GFS, CMA-GFS_Unet and Fusion_QRT wind fields are evaluated using the CCMP fusion wind field data and the 10 m wind vector data from the Chinese offshore buoys, respectively.The results show that the quality of the CMA-GFS_Unet wind field has been significantly improved, and the quality of the wind speed of the Fusion_QRT wind field has been further improved but the quality of the wind direction has been slightly reduced. The mean absolute errors (MAEs) of wind speed are 1.13 m/s, 0.89 m/s and 0.84 m/s for the three wind fields by using CCMP data as reference, and the CMA-GFS_Unet and Fusion_QRT wind fields have improved by 21.3% and 25.7% compared to the CMA-GFS, respectively; while the MAEs of wind direction are 17.5°, 15.5° and 16°, and have improved by11.3% and 8.6%, respectively.The MAEs of wind speed are 1.50 m/s, 1.36 m/s and 1.28 m/s for the three wind fields by using buoy data as reference, and have improved by 9.5% and 14.7% , respectively; while the MAEs of wind direction are 23.3°, 22.7° and 24.0°, and have improved by 3.0% and −3.9% , respectively.

In this paper, the mesoscale model WRF-ARW (Weather Research and Forecasting Model-Advanced Research WRF)(Version 4.0) is used to simulate a typical sea breeze front case in Hainan Island under different weather conditions with high numerical resolution. By designing sensitivity tests for local urbanization, the influence of coastal urbanization on sea breeze fronts in Hainan Island and its possible influencing mechanism are analyzed. The results show that the sea breeze front structure difference caused by urbanization is the result of thermal and dynamic effects. The friction effect of the underlying surface and the enhancement of urban heat island hinder the sea breeze from advancing inland, weaken the cooling and humidification effect of the sea breeze front, and result in a relative lagging of the sea breeze front. The high sea-land thermal difference caused by urbanization enhances the sea breeze wind speed and amplitude, and the vertical updraft and sea breeze circulation thickness in front of the sea breeze front are significantly enhanced. The influence of urbanization on the advance of sea breeze fronts varies during different stages of development. In the early stages of the development of sea breeze fronts, the driving effect of the thermal difference between land and sea is offset by the hindering effect of friction, resulting in no significant impact on the advance of sea breeze fronts. In the strong stage of development of sea breeze fronts, the thermal difference between inland cities and non-urban areas under urbanization conditions has increased, hindering the advance of sea breeze fronts towards inland areas, resulting in a decrease in the penetration distance of sea breeze fronts inland. The influence of urbanization on the advance of sea breeze fronts varies under different weather conditions. Compared to clear weather, the thermal difference between urban and non-urban areas under cloudy weather is slightly stronger, strengthening the hindering effect of urban heat island effect on the advance of sea breeze fronts towards inland areas, resulting in a slightly longer lag distance of sea breeze fronts. Furthermore, when land use transitions to towns occur, net radiation energy exchange with atmospheric air decreases leading to notable declines in latent heat flux alongside increases in sensible heat flux levels. This increased the underlying surface temperature, enhanced the vertical upward movement of sea breeze, and thus caused the increase in boundary layer height.

Flocculation of fine sediments is a key process affecting sediment transport and dispersion in estuaries, which is controlled by complex dynamic structure of estuaries, and spatial distribution of flocs in a stratification condition needs to be explored. To solve this problem, in this study, based on the hydrology and sediment cruising observation in the Modaomen Estuary of the Pearl River during the dry season in 2020, spatial and temporal distribution of flocs in the Modaomen Estuary was analyzed, impacts of dynamics factors were investigated, and distribution pattern of flocs in a stratification condition was uncovered. The results show that median floc size in the Modaomen Estuary during the observation period ranged from 1.87 μm to 395.53 μm, and volumetric concentration of flocs ranged from 20.29 μL/L to 1495.67 μL/L. Vertically, median floc size in the middle and surface layers was generally larger than that in the bottom layer. The plane distribution characteristic of the median floc size is that the maximum values generally occurred at the central bar and the west side. Decomposition of multimodal floc size distributions indicates that the flocs in the Modaomen Estuary were composed of primary particles (Pp) and Flocculi (collectively known as Pico-flocs), microflocs (Micro), macroflocs (Macro), among which Macro was dominant. In view of vertical distribution, the volumetric concentration of Pico-flocs and Micro in the bottom layer tended to be larger than that in the surface and middle layers, while volumetric concentration of Macro in the surface and middle layers was generally larger than that in the bottom layer, which is closely related to dynamic structure in the salt water wedge estuary. Strong salinity stratification inhibited the exchange of flocs between different water layers, resulting in a relatively higher percentage of Macro in the surface layer than that in the middle and bottom layers. In the bottom layer flocs were affected by intensity of turbulent shear and deflocculation process was dominant. As a result, the percentage of Pico-flocs and Micro was higher than the surface and middle layers. This study is not only helpful to elucidate the flocculation mechanism of fine sediment under complex dynamics, but also provides technical support for regulation of mouth bar, goverment of water and sediment, and channel dredging in the Modaomen Estuary.

Under the influence of human activities and rapid climate change, the fluvial sediments flux entering into the South China Sea (SCS) has changed greatly. Based on the hydrological data of rivers around the SCS and sea surface Suspended Sediment Concentration data from 2001 to 2020, this study investigated spatial and temporal variation of sediment flux entering into the SCS. The results show that the sediment flux entering into the SCS exceeds 345 Mt/a during 2001−2020. Human activities result in a reduction of 300 Mt/a in sediment flux from the Zhujiang River, Red River and Mekong River. The sediment flux is also affected by typhoons and climate change: typhoons are the most important factor affecting the sediment flux of small rivers, and the sediment flux of the Gaoping River during the typhoon can reach 89% of total. Under the influence of the East Asian monsoon system, the sediment flux entering into the SCS characterized by significant seasonal variations, the sediment flux is high in wet season and low in dry season. During the wet season, the sediment flux entering into the SCS accounted for more than 80% of total, accordingly, the river plume has the typical characteristics of high concentration and large diffusion range in the wet season. Under the influence of El Niño-Southern Oscillation, the discharge and sediment flux into the sea around the South China Sea also have different periodic changes. The river discharge and sediment flux of large rivers around the South China Sea show a 2.5−3.0 a period, and are correlated with the NIÑO3.4 index, while the sediment flux Taiwan rivers has no obvious period on the interannual scale. Based on data of the 20 a, this study systematically demonstrates the influences of extreme weather, climate change and dam construction on the sediment flux entering into SCS since the 21st century, which is of great significance in the study of source-to-sink processes and watershed management.