The streamline construction and placement of the marine flow field is of great significance for recognizing and understanding the marine flow field. In the process of streamline drawing, the selection of integration step is very important, which can directly affect the effect of streamline placement. The fixed step size algorithm is often not used because it cannot adapt to the changing curvature. The previous adaptive step size streamline algorithm has the problems of low degree of freedom and poor multi-scale applicability. In view of the above problems, this paper introduces information entropy into the step size calculation for the first time, and proposes an adaptive step size algorithm of marine streamline controlled by information entropy. Firstly, the entropy field is obtained by calculating the information entropy of the flow field, and then the flow field is divided into high entropy region and low entropy region according to the entropy value, and each integration point is given a new step size, so that the flow field can adaptively adjust the step size according to the intensity of change, that is, the step size of the high entropy region (the region with sharp change) is smaller, and the step size of the low entropy region (the region with gentle change) is larger. The experimental results show that the proposed algorithm can significantly increase the number of integration points and streamlines in the rapidly changing region, better draw the details of the streamline at the feature, and reduce the number of integration points and streamlines in the unimportant region without affecting the placement effect to improve the computational efficiency. Compared with the previous adaptive step size algorithm, the proposed algorithm significantly improves the degree of freedom of step size adjustment and the scale applicability, and can be applied to different scales of marine flow field.

The prediction of El Niño-Southern Oscillation is one of the hot issues in climate change research. This paper combines swin-transformer model with spatio-temporal fusion attention mechanism, and uses CMIP6 multi-model simulation historical data from 1850 to 2014, SODA assimilated data from 1871 to 1979 and GODAS assimilated data from 1980 to 2023 to construct El Niño-Southern Oscillation prediction model—ENSO-STformer. The model was fully trained on CMIP6 and SODA datasets and evaluated on GODAS data. The results show that the average skill of this model in predicting the Niño3.4 index at 11-month lead times exceeds those of CanCM4, CCSM3, and GFDLaer04 by 5.1%, 21.6%, and 12.4% respectively. Meanwhile, the Niño3.4 index related skills of the proposed model are significantly better than other deep learning models in the medium and long term. Effective ENSO forecasts can be made for up to 24 months, and the 2015−2016 El Niño event simulation shows strong ability to cope with spring forecast obstacles.

Short-term precipitation nowcasting is a critical task in both meteorology and hydrology. However, current deep learning methods often yield ambiguous prediction results and exhibit significant cumulative errors. To address the limitations associated with these predictive methods, particularly the challenges of cumulative error and lack of clarity in prediction sequences, we propose a novel approach based on a Multi-scale Attention Encoding-Dynamic Decoding Network (MAEDDN) for short-term precipitation nowcasting. This method leverages the learning of spatiotemporal features from input data to accurately predict future precipitation scenarios. To obtain richer feature information from the input sequences, the encoding process employs convolutional blocks with spatial and channel attention for encoding. And a multi-scale fusion module is introduced to address the challenge of capturing both small-scale and large-scale information in precipitation distribution simultaneously. To enhance the clarity of the predicted sequences, the model needs to better understand the precipitation process. Therefore, in the decoding process, a dynamic decoding network is proposed in response to the generation and dissipation processes accompanying short-term precipitation. This network flexibly filters the decoding process by learning the intensity distribution and change trends of past input data. Experiments are conducted by using the precipitation data from the open-source SEVIR dataset, and comparisons are made with the best methods reported so far. The experimental results reveal that: (1) MAEDDN enhances the forecasting capability in areas with high-intensity precipitation, and (2) The clarity of the predicted image sequences by MAEDDN is significantly better than that of other models. The constructed multi-scale attention encoding captures the complex relationships in meteorological data more effectively, while the dynamic decoding adapts the decoding process based on different scenarios, resulting in more accurate prediction outcomes.

The stratospheric sulfate aerosol layer formed after large volcanic eruptions can inhibit the formation and development of tropical cyclones. But relevant studies mainly focus on the Atlantic Ocean, and few involve other sea areas. The Northwest Pacific Ocean is the area where most tropical cyclones are generated. Exploring the climate influencing factors is helpful for us to deeply understand the generation and development mechanism of tropical cyclones. Based on the data of tropical cyclones in the Northwest Pacific Ocean recorded in the International Optimum Orbit Database and the China Meteorological Administration database, the changes of sea surface temperature and the number of tropical cyclones in the Northwest Pacific Ocean before and after major volcanic eruptions during 1900−2023 are compared, and the effects of major volcanic eruptions on tropical cyclones in the Northwest Pacific Ocean are discussed. By comparing the number of tropical cyclones two years before the eruption and two years after the eruption, we found that the number of tropical cyclones in the Northwest Pacific decreased significantly after a major eruption. The sea surface temperature significantly responds to low-latitude volcanic eruptions, but does not significantly respond to high-latitude volcanic eruptions. Our study shows that the increase of aerosol forcing after large volcanic eruptions is closely related to the frequency of tropical cyclones, but the decrease of sea surface temperature caused by it may not be the direct cause of the decrease of tropical cyclones, and its mechanism may be related to the migration of the intertropical convergence zone caused by aerosol forcing, which still needs further investigation.

Iron oxides play a significant role in the global soil (sediment) organic carbon (OC) storage. Mangrove wetlands, receiving both terrestrial and marine inputs, provide a unique habitat for the preservation of organic carbon by reactive iron oxides (Fe_R). However, the impact of Fe_R in the surface sediments of mangroves on the preservation process of OC, as well as the selectivity of Fe_R towards different OC components, is still unclear. The surface sediments in the present work was collected in the natural mangrove areas around the estuaries of the Dafeng River and Maowei Sea in Guangxi, which are highly influenced by tides and rivers. The research was focused on the changes in the content and composition of iron-bound organic carbon (Fe-OC) in sediments. The results indicated that the average content of Fe-OC in surface sediments in the estuaries of Dafeng River and Maowei Sea was 0.16% ± 0.07% and 0.17% ± 0.07%, respectively. These values represented 16.2 ± 5.04% and 10.9 ± 5.63% of the total organic carbon (TOC) content in the respective sediments, which were primarily preserved through adsorption. Furthermore, the preservation of OC by Fe_R was influenced by the sediment particle size, the content and form of Fe_R, and the source and composition of TOC. TOC, Fe_R and Fe-OC were mainly distributed in the smaller grain size sediment fractions. The predominant form of iron in surface sediments was Fe³⁺, accounting for 87.42% of the total iron, and was relatively higher in high salinity sediments. Fe_R selectively preserved OC with higher δ¹³C (stable carbon isotope natural abundance) and aromatic OC. Compared with the Maowei Sea, the proportion of protein-like fluorescent components in Fe-OC of surface sediments from the Dafeng River estuary was higher and the proportion of protein-like fluorescent components increased as the molar ratio of Fe-OC∶Fe increased. This study helps to clarify the selective preservation mechanism of OC by Fe_R in mangrove surface sediments in Guangxi, and deepen our understanding of the preservation process of OC in land-sea interface sediments.

To analysis the suitability of using wind field data for forecasting Pacific saury habitat in the northwest Pacific, this paper use the generalized additive model to fit the habitat suitability index (HSI) for Pacific saury in summer and autumn, based on the Chinese fishery data, environmental data and four types of wind field data included the China-France oceanography satellite (CFOSAT) during June to November in 2019−2020. The result indicates that: (1) Weighted analysis shows distinct seasonal variation of environmental variables on catch per unit effort, with chlorophyll concentration and sea surface temperature having the highest weights in summer and autumn respectively, while wind speed had the lowest weight and direct proportionality to the weight. (2) The average accuracy of the four data in summer and autumn is 68.37% and 76.65% respectively, and CFOSAT reaching the highest accuracy of 80.94% in autumn. (3) The high-HSI areas are consistent with the fishing grounds of Pacific saury, while the HSI high-value regions of scatter meter in autumn seems more robust. There are advantages of using wind speed on the forecast model in autumn, as this model can reflect the influence of transient variation factors on the migration and aggregation of Pacific saury.

The phenomenon of marine phytoplankton bloom in ocean refers to the annual cycle increase in biomass caused by rapid reproduction, which plays an important role in the biochemical cycles of marine organisms. However, the spatiotemporal variation characteristics of global phytoplankton blooms and their response mechanisms to the environment still require further exploration. Based on the chlorophyll a products of the MODIS-Aqua (Moderate Resolution Imaging Spectroradiometer) from 2003 to 2022, we extracted the bloom indexes of global ocean phytoplankton (the ratio of bloom duration and bloom intensity). Then, we analyzed their spatiotemporal characteristics, trends, and correlations with environmental factors. The results indicated that there are significant seasonal and latitudinal difference in the distribution of the bloom indexes. Blooms in high latitudes of the Northern Hemisphere mainly occur from April to October, while in mid-low latitudes, blooms mainly occur from November to March of the following year. In the Southern Hemisphere, blooms develop in the month of November and persist until March of the following year in high latitudes, while those in low and middle latitudes occur from May to September. The ratio of bloom duration and bloom intensity show decreasing trends mainly in the mid-low latitude regions of the North Pacific, while increasing trends are observed in mid-high latitude regions of the Southern Hemisphere. The distribution and trends of bloom indices are both regulated by environmental factors. Sea surface temperature and photosynthetically active radiation promote blooms intensity in high latitude waters, but inhibit them in low latitude waters. Meanwhile, the wind speed plays a restraining role in the high latitude sea area and a promoting role in the low latitude sea area.

During June 9th to 15th in 2017, the dynamic of Gymnodinium catenatum bloom along the coast of Hui’an County Fujian Province, were investigated. Six monitoring stations were established for marine environmental factors assessment over seven sampling trips. The water quality data from June 6th, along with the phytoplankton data collected in March and November 2017, was selected for comparative analysis. Furthermore, the survey results of dinoflagellate cysts and paralytic shellfish poison (PSP) in cultured shellfish were integrated to explore the relationship between dormant cysts and the bloom dynamics, as well as the patterns of PSP accumulation and elimination in cultured shellfish. The results revealed that during the bloom, conditions were characterized by sunny weather and favorable sea states, with water temperature ranging from 24.2℃ to 26.8℃ and the salinity between 30.4 and 33.2. The concentrations of nutrients were low, indicating oligotrophic conditions, while the density of G. catenatum peaked at 1.79×10⁶ cells/L during the bloom period. The interplay of favorable hydro-meteorological conditions, N/P ratio and the trophic pattern of G. catenatum provided a basis for the evolution of the bloom, whereas adverse weather conditions primarily contributed to its decline. The average density of G. catenatum cysts in the surveyed sea area was 33.2 cysts/g, suggesting potential for these cysts to germinate into trophic cells, which could lead to the reemergence of the bloom and therefore it necessitates further attention. During the bloom period, both oysters and mussels rapidly accumulated high concentrations of PSP toxins, with levels directly proportional to G. catenatum density. Additionally, even low densities of G. catenatum posed a risk for PSP exceedances. Mussels exhibited a rapid accumulation and slow elimination of PSP, with a significantly greater capacity for toxin accumulation compared to oysters, while their elimination rate was significantly slower.

Sargassum horneri has good economic traits and value, and its culture is gradually being emphasized. However, the suitable water depth for culture is still unclear, and the physiological characteristics and ecological functions of S. horneri in different water depths are also unclear. In this paper, we compared the primary productivity, nutrient uptake capacity, and algal trophic composition of S. horneri at different water depths (0.5 m and 2 m) based on light intensity in different water depths. The results showed that light intensity varied significantly (P < 0.05) with water depths and that the difference in light intensity due to water depth was significantly correlated with the primary productivity of S. horneri. The total productivity of S. horneri grown at 0.5 m water depth (upper layer) was significantly higher than that of S. horneri grown at 2 m water depth (lower layer) (P < 0.05), and S. horneri in the upper layer showed higher primary productivity and carbon sequestration capacity. The uptake of ${{\rm {NH}}_4^+} $ and ${{\rm {PO}}_4^{3-}} $ by the upper layer of S. horneri was significantly better than that of the lower layer of S. horneri under the light conditions (P<0.05), which has good potential in purifying the eutrophic water and regulating the ecological balance of the waters. However, the uptake of ${{\rm {NO}}_3^-} $ and ${{\rm {NO}}_2^-} $ by the upper layer of S. horneri is significantly lower than the lower layer of S. horneri (P>0.05), and it indicates that some strong light inhibition phenomenon occurs in S. horneri which affects the uptake of ${{\rm {NO}}_3^-} $ and ${{\rm {NO}}_2^-} $ by S. horneri. The moisture content of S. horneri at different water depths ranged from 71% to 75%, the ash content from 20% to 23%, the total lipids content from 6% to 8%, and the crude protein content from 8% to 10%. Nutrient composition did not differ significantly (P > 0.05) between the two water depth treatment groups. Studies have shown that by appropriately raising the culture layer, S. horneri can achieve higher productivity and nutrient uptake capacity, while the nutrient composition of S. horneri in the different water layers would remain relatively stable. This study has great significance for the technological development and upgrading of the artificial culture of S. horneri in natural sea areas as well as for the ecological environmental protection of sea areas.

The tidal creek system is an active geomorphic unit in coastal wetlands, and the water environment of different level tidal creeks changes significantly, leading to spatial distribution differences of biological communities. This study selected a typical tidal creek unit in the Huanghe River Delta and used environmental DNA metabarcoding (eDNA) technique to detect the diversity of invertebrates. The biological co-occurrence network analysis and redundancy analysis (RDA) were respectively used to reveal the keystone species and driving factors of the invertebrate community in the typical tidal creek. The results showed that a total of 127 operational taxonomic units (OTUs) of invertebrates were detected in the tidal creek unit, belonging to 9 phyla, 24 classes, 53 orders, 103 families, 87 genera, and 90 species; among them, the class level was dominated by the Arthropoda (43.9%), and the genus level was dominated by the Perinereis (25.2%). The comprehensive diversity index (CD) analysis showed that the comprehensive diversity of invertebrates in the third-level tidal creek was the highest, and the comprehensive diversity of invertebrates in the first-level tidal creek was the lowest. The biological co-occurrence network analysis showed that the Perinereis linea and the Obelia dichotoma were the keystone species, which played a key role in maintaining the stability of the invertebrate community structure in the tidal creek. The RDA showed that the silicate content of the water body, temperature, and the proportion of fine sand and clay in the sediment were the main environmental factors affecting the invertebrate community characteristics in the tidal creek. Correlation network analysis showed that the keystone species were significantly affected by silicate content, clay, and nitrogen content in water (P < 0.05). The research results are helpful for understanding the community structure of typical tidal creek invertebrates, revealing the keystone species of typical tidal ditch invertebrates, and providing data support and theoretical reference for the monitoring and protection of invertebrate diversity.