Ocean Modelling最新文献

{"title":"Revisiting tidal rectification by bottom topography","authors":"Logueminda Sabaga ,&nbsp;Yves Morel ,&nbsp;Nadia Ayoub ,&nbsp;Patrick Marsaleix ,&nbsp;Hoavo Hova ,&nbsp;Alexis Chaigneau","doi":"10.1016/j.ocemod.2025.102587","DOIUrl":"10.1016/j.ocemod.2025.102587","url":null,"abstract":"<div><div>Tidal rectification plays a key role in controlling mean transport in coastal areas and coast-basin material exchange. To calculate mean flows, conventional approaches require high-resolution basin-scale numerical simulations which demands substantial computational resources. This study revisits tidal rectification governed by topographic variation and bottom friction, and proposes a new analytical solution.</div><div>The first step is to derive solutions in the simplest possible configuration. We thus revisit solutions in one-dimensional (1D) configurations, using a Lagrangian approach from which Eulerian results are derived. Exact solutions are provided for the frictionless case and new approximate solutions are developed for a more realistic quadratic bottom friction.</div><div>We then analyze the influence of viscosity on solutions from numerical models. We find that the latter has moderate influence when quadratic bottom friction is considered. However, when the steady rectified current extends over regions deeper than a critical depth, viscosity can lead to spurious effects and alter the accuracy of the numerical results. We show the critical depth can be expressed as a function of friction coefficient, tidal flux and topography variation length-scale.</div><div>We finally extend the analytical solutions derived for the 1D case to the two-dimensional (2D) case. The 2D solutions are compared to results from an ocean general circulation model solving the full barotropic equations in an academic configuration with a complex topography and a quadratic bottom friction. Comparison between analytical solutions and numerical simulations shows good agreement for both the magnitude and direction of the steady rectified tidal current. Sensitivity tests to bottom friction and tide amplitude show that the steady rectified current is parallel to the isobaths and independent of the magnitude of the bottom friction coefficient at first order.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"197 ","pages":"Article 102587"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672651","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 introduction of observation-based wave physics into the wind-wave model WAM: Wave assessments for a semi-enclosed basin","authors":"Marcel Ricker ,&nbsp;Joshua Kousal ,&nbsp;Heinz Günther ,&nbsp;Arno Behrens ,&nbsp;Joanna Staneva","doi":"10.1016/j.ocemod.2025.102564","DOIUrl":"10.1016/j.ocemod.2025.102564","url":null,"abstract":"<div><div>This study presents the successful integration of ST6 observation-based source term physics into the WAM Cycle 7 wind-wave model, marking the first comprehensive validation and comparison with the existing wave physics ST3 and ST4 of WAM, with a specific focus on the Black Sea. Overall, validations demonstrate consistent and satisfactory performance across all three source term physics, aligning with findings from previous studies using different wave models in various regions. Minor discrepancies exist. ST3 excels in representing significant wave height, crucial for wave power applications, while ST4 and ST6 best reproduce T_M02 mean wave period and mean wave direction, impacting Stokes drift direction and thereby drift simulations. Validation metrics unaffected by bias confirm the similarity of the three physics, ruling out calibration issues. Evaluations of ERA5 wind forcing reveal larger errors induced by wind compared to source terms, emphasising the significance of wind quality and model calibration over physics choice. During extreme events, the physics exhibit similar performance, although wind speeds remain relatively low compared to the global ocean. Studies indicate greater discrepancies among physics in larger-scale scenarios with increased fetch, prevailing swell, or hurricanes, where ST6 demonstrates superior performance, while ST4 fares better in low-wind conditions. These findings showcase the need for the examination of these scenarios in the new WAM Cycle 7 version.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"197 ","pages":"Article 102564"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178029","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":"Developing Intelligent Earth System Models : An AI scheme of K-profile parameterization and stable coupling into CESM with FTA","authors":"Bin Mu ,&nbsp;Kang Yang ,&nbsp;Bo Qin ,&nbsp;Hao Li ,&nbsp;Shijin Yuan","doi":"10.1016/j.ocemod.2025.102567","DOIUrl":"10.1016/j.ocemod.2025.102567","url":null,"abstract":"<div><div>Parameterization schemes in numerical models are employed to represent the effects of subgrid-scale physical processes but are often limited by incomplete understanding of physical processes and computational constraints, leading to inaccuracies and inefficiencies. Artificial intelligence (AI) models have been introduced to enhance simulation accuracy or computational efficiency. However, hybrid Earth System Models (ESMs), which integrate AI into traditional frameworks, must also consider stability in coupled simulations. In this study, we replace the default K-profile parameterization (KPP) in Community Earth System Model (CESM) with a transformer-based AI model (KPP-DL). We first perform offline evaluations, demonstrating the AI model’s ability to closely replicate KPP’s key outputs. Subsequently, we couple KPP-DL into CESM via Fortran-Torch adaptor (FTA) and evaluate the hybrid CESM’s performance in terms of accuracy, stability, and computational efficiency. Hybrid CESM maintains stable operation for at least 3 years, with approximately a 3-5 times improvement in the computational efficiency of vertical mixing. During online coupled simulations, KPP-DL exhibits strong agreement with KPP in simulating key vertical mixing coefficients while hybrid CESM produces consistent results for variables such as temperature and salinity. Our results highlight the potential of AI-driven approaches to achieve accuracy approaching that of KPP and stability coupled in ESMs while improving the efficiency, suggesting that intelligent ESMs represent a promising future direction for numerical modeling.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"197 ","pages":"Article 102567"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298194","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":"Waves in a temperate, microtidal and restricted Mediterranean coastal lagoon","authors":"Bartolomé Morote-Sánchez ,&nbsp;Francisco López-Castejón ,&nbsp;Javier Gilabert","doi":"10.1016/j.ocemod.2025.102578","DOIUrl":"10.1016/j.ocemod.2025.102578","url":null,"abstract":"<div><div>The study focused on wave dynamics in the Mar Menor, a hypersaline coastal lagoon in the Southwestern Mediterranean Sea, prompted by ecological crises and the need to understand the physical drivers of its ecology, especially sediment transport and nutrient resuspension. The research employs the SWAN model for wave simulations forced with recorded winds from a meteorological station in the lagoon, covering data from August to October 2019. Model results were validated against data from an ADCP deployed near the met station. The results indicated that Mar Menor experiences low-intensity winds, with occasional strong Northeasterly winds causing the highest waves of up to 1.25 meters high. Self-Organizing Maps (SOMs) analysis provides a classification of wave height, period, wavelength, and bottom orbital velocity, resulting in six wave map categories. The analysis revealed that the largest waves are linked to Southerly winds, and sediment resuspension is most significant during storms, not affecting the deepest 6-meter depth central part of the lagoon. The study concludes that wave-induced orbital velocities can mobilize sediment resuspension during extreme events, potentially disturbing the anoxic bottom. The lagoon was strongly stratified after a flash flood occurred between 12–15 September 2019 with an anoxic bottom layer. Waves driven by winds blowing for long time, although not strongly, contributed to break the stratification producing the fish mass mortality observed in the northern part of the lagoon one month later. Identifying specific wind patterns associated with each SOM map category shows the predictive potential of SOMs for forecasting wave patterns from wind data. Due to their low computational requirements and reliance solely on wind forecasts, SOMs analysis offer a practical tool for early warning systems and for managing ecological risks in environmentally sensitive areas such as coastal lagoons.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"197 ","pages":"Article 102578"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298196","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":"Wavelet ocean data assimilation","authors":"Bradley Sciacca ,&nbsp;Hans Ngodock ,&nbsp;Joseph M. D’Addezio ,&nbsp;Matthew J. Carrier ,&nbsp;Innocent Souopgui","doi":"10.1016/j.ocemod.2025.102589","DOIUrl":"10.1016/j.ocemod.2025.102589","url":null,"abstract":"<div><div>Due to necessary assumptions of observational errors with an exigency for appropriate and timely inversion in the assimilation, dense observations are thinned and/or altered before being assimilated into ocean models. Historically, this process did not significantly restrict model skill because most of the observation types had a quite coarse horizontal distribution. However, recent advances in observation resolution demand new assimilation approaches, whereby small-scale features are actively corrected in the model background. A novel method is introduced that applies multiscale data assimilation utilizing the wavelet transform. Unlike other currently employed ocean multiscale techniques, this method is performed in a single analysis step. Utilizing the wavelet transform allows for observational information to be retained at all its original grid points, compared to the averaging and removal in traditional techniques, such as super observations. This comes from the unique space and frequency relation available to the wavelet transform, which instead filters the potentially correlated small-scale observation errors at each model grid point. Several six-month identical twin data assimilation experiments were used to validate the method. Results indicate comparable to substantial improvements over super observations. On average, the sea surface temperature RMSE was 39 % lower for the wavelet method over the six-months compared to super observations. The wavelet method was also able to constrain horizontal scales in assimilation 29 km and above compared to 60 km and above for the super observations.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"197 ","pages":"Article 102589"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572677","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 global high-resolution CMIP6 ensemble of wave climate simulations and projections using a coastal multigrid: Configuration and performance evaluation","authors":"Rajesh Kumar ,&nbsp;Gil Lemos ,&nbsp;Alvaro Semedo ,&nbsp;Jian-Guo Li","doi":"10.1016/j.ocemod.2025.102566","DOIUrl":"10.1016/j.ocemod.2025.102566","url":null,"abstract":"<div><div>This study presents a comprehensive evaluation of a high-resolution wave climate ensemble, driven by eight CMIP6 General Circulation Models (GCMs), using a coastal multigrid approach based on Spherical Multiple-Cell (SMC) grid. The use of the SMC grid allows for global wave climate simulations refined to high resolutions up to 6 km in coastal regions, where complex interactions between wind, waves, and bathymetry demand more precise modelling. The ensemble’s performance is assessed against wave reanalysis datasets and near-coastal <em>in-situ</em> wave observations, with a focus on key wave climate parameters: significant wave height, mean wave period, peak wave period, and mean wave direction. Results show that the ensemble is able to accurately represent the historical wave climate across diverse regions, excelling in coastal areas, when compared with previous similar datasets, namely across Europe, North America and the Maritime Continent. The multi-resolution SMC grid captures complicated coastal wave patterns and improves the representation of coastal wave dynamics. The ensemble’s ability to simulate both seasonal variability and extreme wave events highlights its potential for high-resolution global wave climate projections, with multiple applications for coastal management and adaptation strategies, marking an advancement in wave climate modelling through its integration in high-resolution, multigrid frameworks.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"197 ","pages":"Article 102566"},"PeriodicalIF":3.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231206","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":"Disentangling wavy and vortical motions in concurrent snapshots of the sea surface height and velocity","authors":"Chuanyin Wang ,&nbsp;Zhiyu Liu ,&nbsp;Hongyang Lin ,&nbsp;Cesar Rocha ,&nbsp;Qinghua Yang ,&nbsp;Dake Chen ,&nbsp;Junbin Gong","doi":"10.1016/j.ocemod.2025.102556","DOIUrl":"10.1016/j.ocemod.2025.102556","url":null,"abstract":"<div><div>Wide-swath satellite missions, such as Surface Water and Ocean Topography (SWOT) and Ocean Dynamics and Sea Exchanges with the Atmosphere (ODYSEA), will provide quasi-concurrent observations of the two-dimensional sea surface height and velocity. Thanks to their high spatial resolution, the spatial features of both vortical and wavy oceanic motions are expected to be captured by these observations. A natural question is whether one can disentangle vortical and wavy motions in these snapshot observations. This issue has attracted some efforts, but crucial progress remains to be made. Here, assuming that only a single concurrent snapshot of the sea surface height and velocity is available, we pursue a dynamical approach for disentangling vortical and wavy motions. This is realized by noting that wavy motions do not induce potential vorticity anomalies. A proof-of-concept application using an output of a realistic high-resolution numerical simulation suggests that the proposed approach is simple and efficient, and is particularly useful for separating wavy and vortical motions in observations by wide-swath satellite missions.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102556"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855274","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":"APPLE-MASNUM: Accelerating parallel processing for lightweight expansion of MASNUM on a single multi-GPU node","authors":"Qi Lou ,&nbsp;Changmao Wu ,&nbsp;Changming Dong ,&nbsp;Xingru Feng ,&nbsp;Yuanyuan Xia ,&nbsp;Li Liu ,&nbsp;Zhengwei Xu ,&nbsp;Xu Gao ,&nbsp;Meng Sun ,&nbsp;Xunqiang Yin","doi":"10.1016/j.ocemod.2025.102557","DOIUrl":"10.1016/j.ocemod.2025.102557","url":null,"abstract":"<div><div>The Marine Science and Numerical Modeling (MASNUM) system, developed for oceanic wave forecasting, play an important role in marine disaster prevention and maritime activities. However, its application is hampered by the requirement of large computing resources. To overcome these barriers, we have implemented an accelerating parallel processing for lightweight expansion of MASNUM (APPLE-MASNUM) on a single compute node with multiple GPUs. In initiating our approach, the mathematical-physics equations of the MASNUM system are thoroughly analyzed to pinpoint the primary computational bottlenecks. This study then transforms MASNUM from a multi-process MPI program into a preliminary GPU-compatible algorithms. Subsequently, the paper proposes an optimization strategy for two-dimensional four-point stencil computations. Following this, an optimization method for overlapping computation with communication is introduced. Finally, a refined data layout scheme tailored for GPUs is designed and implemented. Three numerical experiments with five-day wave forecasts demonstrated that compared to single-core MASNUM, the acceleration ratios of the framework presented in this study are 49.29-fold, 62.58-fold, and 65.74-fold, respectively. This considerable performance boost highlights the efficiency of the lightweight APPLE-MASNUM framework introduced in this research. This signifies the first implementation and optimization of the MASNUM model on a GPU-based heterogeneous platform.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102557"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911537","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":"Attention-enhanced deep learning model for reconstruction and downscaling of thermocline depth in the tropical Indian Ocean","authors":"Zhongkun Feng ,&nbsp;Jifeng Qi ,&nbsp;Delei Li ,&nbsp;Bowen Xie ,&nbsp;Guimin Sun ,&nbsp;Baoshu Yin ,&nbsp;Shuguo Yang","doi":"10.1016/j.ocemod.2025.102537","DOIUrl":"10.1016/j.ocemod.2025.102537","url":null,"abstract":"<div><div>Accurate estimation of high-resolution thermocline depth is important for investigating ocean processes and climate variability on multiple scales. Due to the sparse coverage and high costs associated with in situ observations, reconstructing ocean interior structure from sea surface data serves as a valuable alternative. In this study, a new deep learning model named Enhanced Block Attention Module-Convolutional Neural Network (EBAM-CNN) was proposed to reconstruct thermocline depth in the tropical Indian Ocean (TIO) from 1993 to 2022. Absolute dynamic topography (ADT), sea surface temperature (SST), and sea surface wind (SSW), along with geographic information (latitude and longitude) and temporal data, were employed as input variables. In comparison with the traditional convolutional neural network (CNN) model, the proposed model demonstrates better performance, with an overall Root Mean Square Error (RMSE) of 5.29 m and a Pearson Correlation Coefficient (R) of 0.87. In addition, this study employs a downscaling approach to reconstruct higher-resolution thermocline depth data. An analysis of the downscaling results confirmed that the proposed framework effectively reconstructed mesoscale sea subsurface features from high-resolution surface observations, significantly enhancing thermocline depth estimates and providing robust data support for oceanic and climatic research.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"196 ","pages":"Article 102537"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684506","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":"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":"2025-07-01","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}