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Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harnessed Convolutional Neural Networks (CNNs) to create a deep learning model, which has been applied to the remote sensing data procured from the Geostationary Ocean Color Imager (GOCI) spanning April 2011 to March 2021. Our research indicates that on a small time scale, wind and hydrodynamic forces both have a significant impact on the prediction results of CNNs model. Considering both wind and hydrodynamic forces can effectively improve the model's prediction efficiency for SSC. Moreover, we have employed CNNs to interpolate absent values within the remote sensing datasets, yielding enhancements superior to those attained via linear or multivariate regression techniques. Finally, the correlation coefficient between CNN-derived SSC estimates for Jiaozhou Bay (JZB) and its corresponding remote sensing data is 0.72. Correlation coefficient and root mean square error differ in different regions. In the shallow water of JZB, due to water level changes, there is limited data, and the correlation coefficient in this area is about 0.5-0.6. In the central region of JZB with sufficient data, the correlation coefficient is generally higher than 0.75. Therefore, we believe that this CNNs model can be used to predict the hourly variation of SSC. When juxtaposed with alternative methodologies, the CNN approach is found to economize computational resources and enhance processing efficiency.

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In the past decade (2010-2019), the annual maximum typhoon storm surge (AMTSS) accounted for 46.6% of the total direct economic loss caused by marine disasters in Chinese mainland, but its prediction in advance is challenging. By analyzing records of 23 tide-gauge stations, we found that the AMTSSs in Shanghai, Zhejiang and Fujian show significant positive correlations with the El Niño-Southern Oscillation (ENSO). For the 1987-2016 period, the maximum correlation is achieved at Pingtan station, where correlation coefficient between the AMTSS and Niño-3.4 is 0.55. The AMTSS occurring in El Niño years are stronger than those in non-El Niño years by 9-35 cm in these areas. Further analysis suggests that a developing El Niño can greatly modulate the behaviors of Northwest Pacific typhoons. Strong typhoons tend to make landfall in southeast China with stronger intensities and northward shifted landfall positions. This study indicates that the modulation effect by ENSO may provide potential predictability for the AMTSS, which is useful for the early alert and reduction of storm surge damages.

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Oceanic mesoscale eddies are common, especially in areas where zonal currents with meridional shear exists. The nonlinear effects complicate the analysis of mesoscale eddy dynamics. This study proposes a solitary (eddy) solution based on an asymptotic expansion of the nonlinear potential vorticity equation with a constant meridional shear of zonal current. This solution reveals several important consequences. For example, cyclonic (anticyclonic) eddies can be generated by the negative (positive) shear of the zonal current. Furthermore, the meridional structure of an eddy is asymmetrical, and the center of a cyclonic (anticyclonic) eddy tilts poleward (equatorward). Eddy width is inversely proportional to shear intensity. Eddy phase speed is proportional to shear intensity and the wave amplitude, and their spatial distribution show band-like pattern as they propagate westward. This nonlinear solitary solution is an extension of classical linear Rossby theory. Moreover, these findings could be applied to other areas with similar zonal current shear.

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River discharges are the important freshwater and nutrient sources for Bohai Sea (BS), and have a profound impact on the local marine environment. In this paper, the annual cycles of nutrient and phytoplankton dynamics in 1980s were reproduced using a coupled biogeochemical-physical model. Based on the validated simulations, the nutrient limitation characters were further investigated by running the model with the riverine nutrient altered, first enriching nitrogen and then phosphorus. It was found that although the riverine N:P ratios in Yellow and Haihe Rivers were much higher than the Redfield number, the nitrogen enrichment was still able to enhance the algae bloom in Laizhou and Bohai Bays. On the other hand, the response of algae growth to phosphorus enrichment was not thus obvious, which suggests that the local phytoplankton dynamics was characterized by the nitrogen limitation. Simulations also show that the nitrogen enrichment is generally accompanied by the phosphorus consumption, so a shift from nitrogen limitation to phosphorus limitation may occur if such a trend continues.

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