Ocean Modelling最新文献

{"title":"Mechanism of suspended sediment variation during a landing typhoon in the Yangtze River Estuary","authors":"Jie Wang ,&nbsp;Cuiping Kuang ,&nbsp;Daidu Fan ,&nbsp;Jilong Chen ,&nbsp;Rufu Qin ,&nbsp;Dan Wang","doi":"10.1016/j.ocemod.2025.102554","DOIUrl":"10.1016/j.ocemod.2025.102554","url":null,"abstract":"<div><div>Hydrodynamic variations and suspended sediment concentration (SSC) dynamics under typhoon conditions are critical to understanding sediment transport processes in the Yangtze River Estuary (YRE). Strong winds, waves, and currents during typhoons alter water levels, flow velocities, and SSC distribution patterns, significantly affecting estuarine stratification. This study employs a validated three-dimensional coupled hydrodynamic-salinity-sediment transport model integrated with wave dynamics to investigate storm surge characteristics and SSC variations during Typhoon Muifa (September 2022). The mechanisms driving SSC changes were analyzed through wave effects and bottom shear stress (BSS). Key findings include: 1) Storm surge evolution during Typhoon Muifa transitioned from positive to negative storm surge due to the typhoon's southeast-to-northwest path, with a declining trend post-landfall. 2) Peak BSS values at NC2 (outer North Channel) reached 9.0 Pa, five times higher than normal conditions, while the maximum bottom-layer SSC at NB2 (outer North Branch) exceeded 15 kg/m³. 3) Nonlinear wave-current interactions modulated BSS magnitudes and SSC distribution. These interactions enhanced BSS in outer estuarine regions, suppressed tidal forces, and promoted sediment deposition, ultimately reducing SSC across all study sites. The results highlight distinct SSC responses to typhoon-induced dynamics, underscoring the need for further research on wave-sediment interaction mechanisms.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102554"},"PeriodicalIF":3.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface drifter trajectory prediction in the Gulf of Mexico using neural networks","authors":"Matthew D. Grossi ,&nbsp;Stefanie Jegelka ,&nbsp;Pierre F.J. Lermusiaux ,&nbsp;Tamay M. Özgökmen","doi":"10.1016/j.ocemod.2025.102543","DOIUrl":"10.1016/j.ocemod.2025.102543","url":null,"abstract":"<div><div>Machine learning techniques are applied to Lagrangian trajectory reconstructions, which are important in oceanography for providing guidance to search and rescue efforts, forecasting the spread of harmful algal blooms, and tracking pollutants and marine debris. This study evaluates the ability of two types of neural networks for learning ocean trajectories from nearly 250 surface drifters released during the Grand Lagrangian Deployment in the Gulf of Mexico from Jul-Oct 2012. First, simple fully connected neural networks were trained to predict an individual drifter’s trajectory over 24<!--> <!-->h and 5<!--> <!-->d time windows using only that drifter’s previous velocity time series. These networks, despite having successfully learned modeled trajectories in a previous study, failed to outperform common autoregressive models in any of the tests conducted. This was true even when drifters were pre-sorted into geospatial groups based on past trajectories and different networks were trained on each group to reduce the variability that each network had to learn. In contrast, a more sophisticated social spatio-temporal graph convolutional neural network (STN), originally developed for learning pedestrian trajectories, demonstrated greater potential due to two important features: learning spatial and temporal patterns simultaneously, and sharing information between similarly-behaving drifters to facilitate the prediction of any particular drifter. Position prediction errors averaged around 60<!--> <!-->km at day 5, roughly 20<!--> <!-->km lower than autoregression, and even better for certain subsets of drifters. The passage of Tropical Cyclone Isaac over the drifter array as a tropical storm and Category 1 hurricane provided a unique opportunity to also explore whether these models would benefit from adding wind as a predictor when making short 24<!--> <!-->h predictions. The STNs were found to not benefit from wind on average, though certain subsets of drifters exhibited slightly lower reconstruction errors at hour 24 with the addition of wind.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102543"},"PeriodicalIF":3.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new EnOI-based multiscale assimilation system for global ocean circulation model","authors":"Li Liu ,&nbsp;Xunqiang Yin ,&nbsp;Xueen Chen","doi":"10.1016/j.ocemod.2025.102551","DOIUrl":"10.1016/j.ocemod.2025.102551","url":null,"abstract":"<div><div>With advancements in observational techniques and computational technologies, multi-scale data assimilation (MS-DA) has become a significant focus in data assimilation research. Global ocean models with variable resolution grids offer advantages in specific regions, highlighting the need for MS-DA methods tailored for these models. This study develops an MS-DA system that extends the single-scale data assimilation (SS-DA) framework. It integrates a multi-grid interpolation module and uses the MPI-OM ocean model with the Ensemble Kalman Filter (EAKF) assimilation technique. The system aims to enhance computational efficiency and improve the assimilation of temperature and salinity data, particularly in the South China Sea (SCS). The MS-DA system uses a two-step assimilation process to convert high-resolution model data to lower-resolution grids and vice versa, with parallel computing to accelerate data processing. The study compares the computational performance and assimilation outcomes of MS-DA and SS-DA. Results show that the MS-DA system reduces computational time by 20 % compared to SS-DA, with minimal differences in global sea surface temperature (SST) and salinity (SSS) assimilation. However, the MS-DA system performs better in the SCS, reducing the mean absolute error (MAE) for SST and SSS by 0.01 °C and 0.01 PSU, respectively. It also improves temperature and salinity assimilation in the upper 500 m of the SCS by 1.7 % and 6.2 %, respectively. Overall, the MS-DA system offers better assimilation performance with lower computational costs, especially in high-resolution regions like the SCS.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102551"},"PeriodicalIF":3.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the directional distributions of the observation-based wind input and dissipation terms in a third-generation wave model","authors":"Franklin F. Ayala ,&nbsp;Rubén D. Montoya ,&nbsp;Andrés F. Osorio ,&nbsp;Alexander Babanin","doi":"10.1016/j.ocemod.2025.102542","DOIUrl":"10.1016/j.ocemod.2025.102542","url":null,"abstract":"<div><div>New observation-based directional spreading functions for the wind input term <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>i</mi><mi>n</mi></mrow></msub></math></span> and the dissipation term <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>d</mi><mi>s</mi></mrow></msub></math></span> have been investigated in third-generation wave models (WAVEWATCH III, ST6 version). In this study we evaluate bimodal directional distribution for <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>d</mi><mi>s</mi></mrow></msub></math></span> and narrower (<span><math><mrow><msup><mrow><mo>cos</mo></mrow><mrow><mn>2</mn></mrow></msup><mi>θ</mi></mrow></math></span> and <span><math><mrow><msup><mrow><mo>cos</mo></mrow><mrow><mn>4</mn></mrow></msup><mi>θ</mi></mrow></math></span>) distributions for <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>i</mi><mi>n</mi></mrow></msub></math></span>. The impact of these new directional functions on the integral wave parameters, as well as the nondirectional and directional spectra, is analyzed through a set of numerical experiments and their results are compared against buoy observations in the Caribbean Sea. Improvements in the estimation of <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> under wind-sea dominant conditions are presented. For the shape of frequency spectra, the bimodal distribution of <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>d</mi><mi>s</mi></mrow></msub></math></span> causes a weaker energy enhancement by comparison with the narrower distribution of <span><math><msub><mrow><mi>S</mi></mrow><mrow><mi>i</mi><mi>n</mi></mrow></msub></math></span>, but it generally shows a better fit than the default functions in the model. The combined effect of both modifications brings larger energy allocation around the main wave propagation direction, generating both an energy increase and a downshift at the spectral peak. These findings suggest the relevance of including realistic directional distributions and reveal the need for further studies of directional distributions of the source terms.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102542"},"PeriodicalIF":3.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The simulation of the South China Sea by the variable resolution version of the global ocean general circulation model LICOM3.0","authors":"Jiangfeng Yu ,&nbsp;Jingwei Xie ,&nbsp;Hailong Liu ,&nbsp;Pengfei Lin ,&nbsp;Zipeng Yu ,&nbsp;Jiahui Bai","doi":"10.1016/j.ocemod.2025.102549","DOIUrl":"10.1016/j.ocemod.2025.102549","url":null,"abstract":"<div><div>We develop a variable-resolution method based on the tripolar grid to achieve fine-resolution regional simulations with limited computational resources. Based on the global ocean general circulation model LICOM3.0, we select the South China Sea (SCS) as the refined area and design five experiments to assess the impact of the variable-resolution grid on oceanic simulation. The results show that the method can retain the model capacity for global ocean simulation and obtain results in the refined region comparable to the reference global high-resolution model. Improving the resolution in the SCS from 0.1° to 0.02° significantly enhances the model performance in simulating submesoscale phenomena. The model can effectively reproduce submesoscale processes generated by frontogenesis, topographic wakes, and their seasonal variation. We uncover the effect of the submesoscale vortex train near the Luzon Strait. In summer, the vortex train tends to carry positive vorticity westward into the SCS and constrain the negative vorticity along the Kuroshio Current. In winter, the vortex train is more intrusive into the SCS with enhanced filament activities.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102549"},"PeriodicalIF":3.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement in wintertime mixed layer depth simulation by CMIP6 compared to CMIP5 climate models","authors":"Tengfei Xu ,&nbsp;Shanshan Jin ,&nbsp;Xunwei Nie ,&nbsp;Zishan Qiu ,&nbsp;Hao Liu ,&nbsp;Ying Li ,&nbsp;Zexun Wei","doi":"10.1016/j.ocemod.2025.102545","DOIUrl":"10.1016/j.ocemod.2025.102545","url":null,"abstract":"<div><div>In this study, we assessed the simulated winter mixed layer depth (MLD) in the CMIP5 and CMIP6 models by comparing to Argo observations. Both the CMIP5 and CMIP6 models have well simulated the pattern of the winter MLD; however, there are large biases in the subtropical and subpolar regions, where the Subtropical Under Waters, Subtropical and Subantarctic Mode Waters, and North Atlantic Deep Water are formed. The CMIP6 models have not eliminated the simulation biases of winter MLD in subtropical and subpolar regions. However, Taylor Diagram (TD) analysis indicates that the CMIP6 models improved winter MLD simulation, showing less scatter and better TD scores than the CMIP5 simulations. Biases in the CMIP6 models were reduced by 11.60–27.55% in the subtropical to middle latitudes, and by 12.64–67.64% in the south/north subpolar Atlantic Ocean. With both the CMIP5 and CMIP6 models, the biases in winter MLD simulation are mainly attributed to inaccurate convection and stratification associated with heat/freshwater fluxes and thermohaline structures. Improving mixing parameterization is one of the most important updates in the CMIP6 models; however, it remains uncertain whether these improvements caused the winter MLD biases to decrease.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102545"},"PeriodicalIF":3.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wave condition prediction and uncertainty quantification based on SG-MCMC and deep learning model","authors":"Miao Yu ,&nbsp;Zhifeng Wang ,&nbsp;Wenfang Lu ,&nbsp;Dalei Song","doi":"10.1016/j.ocemod.2025.102547","DOIUrl":"10.1016/j.ocemod.2025.102547","url":null,"abstract":"<div><div>Deep learning methods have increasingly gained popularity in wave prediction; however, previous works predominantly focused on point estimates without quantifying the uncertainty of predictions. In certain scenarios, generating probabilistic forecasts with credible intervals is crucial for vessel navigation planning and marine infrastructure construction, etc. Therefore, this paper attempts to conduct a benchmark study on wave prediction uncertainty from both frequentist and Bayesian perspectives. A wave prediction model based on the ConvLSTM combined with various uncertainty quantification methods has been established, and a unified evaluation framework has been constructed using statistical decision theory. We utilized the fifth-generation atmospheric reanalysis dataset (ERA5) from the European Centre for Medium-Range Weather Forecasts as experimental data, employing key wave parameters (significant wave height, mean wave period, and mean wave direction) for model training and testing. Through extensive repeated experiments, we found that Bayesian methods generally provide more accurate average predictions, with the SG-MCMC method performing the best, achieving RMSE values of 0.37 m and 0.49 s for HS and TM at the 24-hour prediction, respectively, and CC values of 93.65 % and 90.65 %. On the other hand, confidence intervals obtained from frequentist methods offer broader coverage for data variations, with the MIS regression method performing the best, yielding the lowest MIS scores of only 2.92 m and 1.93 s for HS and TM at the 24-hour prediction, respectively. Indeed, this result underscores a limitation of current methods: the confidence intervals do not adequately reflect the accuracy of the predictions. In contrast, the SG-MCMC method demonstrates superior performance by excelling in both mean prediction and confidence limits.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102547"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a flexible data assimilation system for a 3D unstructured-grid ocean model under Earth System Modeling Framework","authors":"Hao-Cheng Yu ,&nbsp;Y. Joseph Zhang ,&nbsp;Lars Nerger ,&nbsp;Carsten Lemmen ,&nbsp;Jason C.S. Yu ,&nbsp;Tzu-Yin Chou ,&nbsp;Chi-Hao Chu ,&nbsp;Chuen-Teyr Terng","doi":"10.1016/j.ocemod.2025.102546","DOIUrl":"10.1016/j.ocemod.2025.102546","url":null,"abstract":"<div><div>We develop a new data assimilative (DA) system by combining two parallel frameworks: the Parallel DA Framework (PDAF) and the Earth System Modeling Framework (ESMF). The new DA system is built on ESMF at the top level driving PDAF and any combination of Earth system model (ESM) components, thereby allowing maximum flexibility and ease of implementation of DA in fully coupled ESM applications, with minimal intrusive code changes to the original model components. We demonstrate the new DA system using the 3D unstructured-grid ocean model SCHISM as the ESM component. The new system is validated using both simple benchmarks and a realistic simulation of the Kuroshio current system around Taiwan. The new system improves the model skill for temperature and surface elevation before, during and after typhoon events. The flexibility and ease of implementation make the new system widely applicable for other coupled ESMs, thus paving the way for a new generation of data assimilative cross-scale ESMs.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102546"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of ocean surface waves on sea ice using coupled wave–ice–ocean modelling","authors":"Shuo Li ,&nbsp;Alexander Babanin ,&nbsp;Qingxiang Liu ,&nbsp;Jin Liu ,&nbsp;Rui Li ,&nbsp;Joey Voermans","doi":"10.1016/j.ocemod.2025.102540","DOIUrl":"10.1016/j.ocemod.2025.102540","url":null,"abstract":"<div><div>The reduction of ice content in the context of climate change in polar oceans has sparked strong scientific interest in wave–ice interactions. To study the wave effect on sea ice in the marginal ice zone (MIZ), wave model WW3 and ocean–ice model MOM6-SIS2 are fully coupled by implementing schemes to account for non-breaking wave-induced turbulence and wave stress on ice. Improved estimations of ocean mixed layer depth and sea ice extent are achieved by incorporating the parameterizations of wave effects. The results also show that wave-induced turbulence enhances upper ocean mixing and significantly impedes ice growth with warmed sea surface in winter. Conversely, the sea surface is cooled in summer and the sea ice melt is slowed down. The wave stress on ice is predominantly directed towards the ice field and is comparable in magnitude to the wind stress. The sea ice extent is increased due to the wave stress.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102540"},"PeriodicalIF":3.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity analysis of wind drift parameter for lagrangian particle model of dummy drift trajectory in the Northern South China Sea","authors":"Yuxi Wu ,&nbsp;Enjin Zhao","doi":"10.1016/j.ocemod.2025.102539","DOIUrl":"10.1016/j.ocemod.2025.102539","url":null,"abstract":"<div><div>In this study, we applied the Lagrangian particle model to simulate the drift trajectories of horizontal dummies in maritime environments, with a specific focus on the northern South China Sea. Wind exerts a force on floating objects, causing them to drift in a direction influenced by the wind direction and strength. The wind drift factor quantifies this effect, indicating how much the wind's force contributes to the overall drift velocity. Sensitivity analysis, employing both the one-at-a-time and Sobol methods, was conducted to examine the model's responses to variations in wind drift parameters. The findings underscore a significant impact of the wind drift factor on the drift trajectory, where an increase in the wind drift coefficient resulted in a gradual deviation of the drift point towards the left, and the overall drift trajectory deviated towards the west. Specifically, when the wind drift coefficient was varied, the model exhibited significant changes in the average drift trajectory and increased instability, as indicated by larger standard deviations. Variations in the depth of wind influence, which pertains to the vertical extent of wind effect on sea surface dynamics, had a relatively smaller impact on the model's output compared to changes in the wind drift factor. These results were further validated by comparing the LPM-simulated trajectories with those predicted by the National Maritime Search and Rescue System. The optimal sea surface wind drift factor, determined through sensitivity experiments, was found to be 0.016, which yielded the most precise simulation of the dummy's drift trajectory.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102539"},"PeriodicalIF":3.1,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}