Pub Date : 2026-01-19DOI: 10.1016/j.ocemod.2026.102684
Zhiyong Peng , Jiehua Wu , Peng Wang
Storm surge represents a significant coastal hazard in Fujian Province, where intense wave activity can exacerbate inundation by imparting additional momentum and mass flux to coastal waters. Accurate simulation of storm surge events requires explicit consideration of wave-current interactions, particularly wave-enhanced bottom stress. In this study, the effects of wave-enhanced bottom stress on storm surge induced by Typhoon Doksuri (2023) are investigated using numerical simulations. Results demonstrate that wave-enhanced bottom stress contributes to a 4-8 % increase in nearshore storm surge by modifying volume transport through the strait. Moreover, alternative parameterizations of bottom stress produce substantial differences in simulated surge magnitude. These findings highlight the critical importance of appropriately representing wave-enhanced bottom stress in numerical models to improve the reliability of storm surge and coastal inundation forecasts.
{"title":"How does wave-enhanced bottom stress affect typhoon-induced storm surge","authors":"Zhiyong Peng , Jiehua Wu , Peng Wang","doi":"10.1016/j.ocemod.2026.102684","DOIUrl":"10.1016/j.ocemod.2026.102684","url":null,"abstract":"<div><div>Storm surge represents a significant coastal hazard in Fujian Province, where intense wave activity can exacerbate inundation by imparting additional momentum and mass flux to coastal waters. Accurate simulation of storm surge events requires explicit consideration of wave-current interactions, particularly wave-enhanced bottom stress. In this study, the effects of wave-enhanced bottom stress on storm surge induced by Typhoon Doksuri (2023) are investigated using numerical simulations. Results demonstrate that wave-enhanced bottom stress contributes to a 4-8 % increase in nearshore storm surge by modifying volume transport through the strait. Moreover, alternative parameterizations of bottom stress produce substantial differences in simulated surge magnitude. These findings highlight the critical importance of appropriately representing wave-enhanced bottom stress in numerical models to improve the reliability of storm surge and coastal inundation forecasts.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102684"},"PeriodicalIF":2.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034765","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}
Pub Date : 2026-01-12DOI: 10.1016/j.ocemod.2026.102683
Hui Xu , Kai Yin , Sudong Xu , Pengju Han , Shangpeng Gong
Global warming has resulted in rising sea levels and an increased frequency of extreme weather events, intensifying the need for effective coastal defences against marine disasters. Nature-based solutions, which offer benefits such as wave dissipation, sediment deposition, ecological compatibility, and environmental adaptability, have emerged as a crucial strategy for coastal protection. Previous studies have predominantly modelled vegetation as simplified cylinders to examine its role in wave attenuation, often ignoring the potential influence of vegetation’s branching structure. Based on these, this research constructs reasonable flume experiments to analyse the discrepancy in wave-attenuation performance between typical branched flexible vegetation and non-branched flexible vegetation. The experiments involve varying water depths, wave heights, and wave periods to comprehensively evaluate the impact of vegetation structure on wave propagation. Comparative analysis results indicate that the branching structure significantly enhances the wave-absorbing capacity of flexible vegetation. Within the tested simulation parameters, the average wave attenuation effect in flexible branched vegetation zones (FBV) was approximately 47 % higher than in flexible unbranched vegetation zones (FUV), with this quantitative difference varying considerably under different hydrodynamic conditions. Furthermore, the findings suggest that the impact of vegetation on wave attenuation is more pronounced under conditions of greater wave heights, shallower water depths, and longer wave periods. On the other hand, the function of vegetation's branching structure in wave reduction becomes more significant as the water depth increases or the wave height decreases. Physical modelling experiments revealing the specific influence of branching structure on wave attenuation can provide a scientific foundation and practical guidance for designing more effective ecological coastal protection measures.
{"title":"Effects of monopodial branching flexible vegetation on the wave attenuation by vegetation","authors":"Hui Xu , Kai Yin , Sudong Xu , Pengju Han , Shangpeng Gong","doi":"10.1016/j.ocemod.2026.102683","DOIUrl":"10.1016/j.ocemod.2026.102683","url":null,"abstract":"<div><div>Global warming has resulted in rising sea levels and an increased frequency of extreme weather events, intensifying the need for effective coastal defences against marine disasters. Nature-based solutions, which offer benefits such as wave dissipation, sediment deposition, ecological compatibility, and environmental adaptability, have emerged as a crucial strategy for coastal protection. Previous studies have predominantly modelled vegetation as simplified cylinders to examine its role in wave attenuation, often ignoring the potential influence of vegetation’s branching structure. Based on these, this research constructs reasonable flume experiments to analyse the discrepancy in wave-attenuation performance between typical branched flexible vegetation and non-branched flexible vegetation. The experiments involve varying water depths, wave heights, and wave periods to comprehensively evaluate the impact of vegetation structure on wave propagation. Comparative analysis results indicate that the branching structure significantly enhances the wave-absorbing capacity of flexible vegetation. Within the tested simulation parameters, the average wave attenuation effect in flexible branched vegetation zones (FBV) was approximately 47 % higher than in flexible unbranched vegetation zones (FUV), with this quantitative difference varying considerably under different hydrodynamic conditions. Furthermore, the findings suggest that the impact of vegetation on wave attenuation is more pronounced under conditions of greater wave heights, shallower water depths, and longer wave periods. On the other hand, the function of vegetation's branching structure in wave reduction becomes more significant as the water depth increases or the wave height decreases. Physical modelling experiments revealing the specific influence of branching structure on wave attenuation can provide a scientific foundation and practical guidance for designing more effective ecological coastal protection measures.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102683"},"PeriodicalIF":2.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034766","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}
Pub Date : 2026-01-06DOI: 10.1016/j.ocemod.2026.102682
Zhor Benhafid , Elise Mayrand , Sid Ahmed Selouani
Ocean acidification poses a growing threat to marine ecosystems and aquaculture productivity, particularly in under-monitored coastal regions such as Eastern Canada. Existing pH prediction frameworks typically rely on multi-year records combining extensive carbonate chemistry, physical, and biological parameters. While these models can achieve high accuracy, their data requirements make them costly, complex, and challenging to implement for local, site-specific acidification forecasting in aquaculture contexts. To address this limitation, this study benchmarks several machine learning models for coastal pHSWS prediction using only three routinely measured environmental variables (temperature, salinity, sea level), from which we derived moving-average descriptors, local gradients, and two temporal indicators, resulting in a compact set of 11 input features. Six different models and a multivariate linear regression baseline were trained on one of the most complete and extended high-frequency datasets available (BSSS2018) and evaluated across four independent datasets: one from the same site but six months earlier (BSSS2017), and three from nearby bays in northeastern New Brunswick collected between 2017 and 2019. Among all tested models, XGBoost emerged as the most reliable and interpretable, achieving the best trade-off between sensitivity and precision at the operational acidification threshold (). Its performance remained acceptable within-site but declined across bays due to environmental and seasonal discrepancies, underscoring the importance of training data representativeness. SHAP-based explainability confirmed that Julian day was the dominant predictor, integrating the composite effects of seasonal environmental variability. Overall, this study demonstrates that using only low-cost, routinely measured features provides a promising foundation for short-term coastal pH forecasting, particularly for aquaculture monitoring needs. Despite limited inter-bay generalization, the proposed framework shows that interpretable machine learning models can deliver actionable early-warning insights under realistic data constraints. It constitutes one of the first data-driven benchmarks explicitly tested at aquaculture-relevant thresholds, highlighting a scalable and transparent approach toward operational acidification forecasting.
{"title":"Explainable machine learning models for coastal pH forecasting at aquaculture-relevant thresholds in Eastern Canada","authors":"Zhor Benhafid , Elise Mayrand , Sid Ahmed Selouani","doi":"10.1016/j.ocemod.2026.102682","DOIUrl":"10.1016/j.ocemod.2026.102682","url":null,"abstract":"<div><div>Ocean acidification poses a growing threat to marine ecosystems and aquaculture productivity, particularly in under-monitored coastal regions such as Eastern Canada. Existing pH prediction frameworks typically rely on multi-year records combining extensive carbonate chemistry, physical, and biological parameters. While these models can achieve high accuracy, their data requirements make them costly, complex, and challenging to implement for local, site-specific acidification forecasting in aquaculture contexts. To address this limitation, this study benchmarks several machine learning models for coastal pH<sub>SWS</sub> prediction using only three routinely measured environmental variables (temperature, salinity, sea level), from which we derived moving-average descriptors, local gradients, and two temporal indicators, resulting in a compact set of 11 input features. Six different models and a multivariate linear regression baseline were trained on one of the most complete and extended high-frequency datasets available (BSSS2018) and evaluated across four independent datasets: one from the same site but six months earlier (BSSS2017), and three from nearby bays in northeastern New Brunswick collected between 2017 and 2019. Among all tested models, XGBoost emerged as the most reliable and interpretable, achieving the best trade-off between sensitivity and precision at the operational acidification threshold (<span><math><mrow><mi>p</mi><msub><mrow><mi>H</mi></mrow><mrow><mi>S</mi><mi>W</mi><mi>S</mi></mrow></msub><mo><</mo><mn>7</mn><mo>.</mo><mn>75</mn></mrow></math></span>). Its performance remained acceptable within-site but declined across bays due to environmental and seasonal discrepancies, underscoring the importance of training data representativeness. SHAP-based explainability confirmed that Julian day was the dominant predictor, integrating the composite effects of seasonal environmental variability. Overall, this study demonstrates that using only low-cost, routinely measured features provides a promising foundation for short-term coastal pH forecasting, particularly for aquaculture monitoring needs. Despite limited inter-bay generalization, the proposed framework shows that interpretable machine learning models can deliver actionable early-warning insights under realistic data constraints. It constitutes one of the first data-driven benchmarks explicitly tested at aquaculture-relevant thresholds, highlighting a scalable and transparent approach toward operational acidification forecasting.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102682"},"PeriodicalIF":2.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938698","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}
Pub Date : 2026-01-06DOI: 10.1016/j.ocemod.2026.102681
Jie Yu , David D. Flagg , Tommy G. Jensen , Tim J. Campbell , Qing Wang , Denny P. Alappattu
We present a recent study to implement and test schemes for diagnostic calculations of skin sea surface temperature in the Navy’s Coastal Ocean Model (NCOM). This includes three schemes for estimating the cool anomaly in the viscous sublayer (i.e., the ocean skin), and a fourth scheme that adds an estimate of a warm anomaly in the solar radiation-driven, thermally stratified diurnal layer at near-surface depths. Applications of these schemes are made, and their performances are evaluated against field measurements from the Coupled Air-Sea Processes and Electromagnetic Ducting Research East campaign (CASPER-East), showing overall good agreements. The statistics of the model-observation comparisons are similar and do not indicate any systematic bias towards any scheme, but differences in the model performances are noticeable and vary depending on the surface wind and solar conditions. To understand the discrepancies among the schemes, inter-model comparisons are analyzed based on the conditions of surface wind stress and solar radiation flux. The issues associated with making the warm-layer correction are discussed, in particular, including the sensitivity of the diagnostic warm-layer anomaly to the layer thickness specified a priori, and the risk of double-counting the effect of solar radiation penetration when using the high-resolution NCOM temperature fields.
{"title":"Skin sea surface temperature diagnostics in a regional ocean model","authors":"Jie Yu , David D. Flagg , Tommy G. Jensen , Tim J. Campbell , Qing Wang , Denny P. Alappattu","doi":"10.1016/j.ocemod.2026.102681","DOIUrl":"10.1016/j.ocemod.2026.102681","url":null,"abstract":"<div><div>We present a recent study to implement and test schemes for diagnostic calculations of skin sea surface temperature in the Navy’s Coastal Ocean Model (NCOM). This includes three schemes for estimating the cool anomaly in the viscous sublayer (i.e., the ocean skin), and a fourth scheme that adds an estimate of a warm anomaly in the solar radiation-driven, thermally stratified diurnal layer at near-surface depths. Applications of these schemes are made, and their performances are evaluated against field measurements from the Coupled Air-Sea Processes and Electromagnetic Ducting Research East campaign (CASPER-East), showing overall good agreements. The statistics of the model-observation comparisons are similar and do not indicate any systematic bias towards any scheme, but differences in the model performances are noticeable and vary depending on the surface wind and solar conditions. To understand the discrepancies among the schemes, inter-model comparisons are analyzed based on the conditions of surface wind stress and solar radiation flux. The issues associated with making the warm-layer correction are discussed, in particular, including the sensitivity of the diagnostic warm-layer anomaly to the layer thickness specified <em>a priori</em>, and the risk of double-counting the effect of solar radiation penetration when using the high-resolution NCOM temperature fields.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102681"},"PeriodicalIF":2.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938699","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}
Pub Date : 2026-01-03DOI: 10.1016/j.ocemod.2026.102678
Davide Grande , Roberto Buizza , Andrea Storto
This review examines recent advances in the application of machine learning to ocean data assimilation, covering contributions published between 2020 and 2025. We identify emerging trends, recurring limitations, and critical open questions, structuring the discussion around four scientific challenges: observation integration, boundary treatment, fine-scale process representation, and physical consistency. While convolutional neural networks remain widely used, particularly in bias correction and super-resolution tasks, recent studies increasingly employ multilayer perceptrons, long short-term memories, transformers and neural operators for error estimation, sequential bias correction, and latent-space assimilation. Despite this architectural diversity, most contributions remain confined to idealized configurations or offline modules, with limited evidence of generalization and integration into operational pipelines. We conclude that hybrid systems combining embedded physical knowledge with systematic validation across different oceanic regimes will be essential to unlock the full potential of machine learning-enhanced ocean data assimilation.
{"title":"Machine learning in ocean data assimilation: Advances, gaps and the road to operations","authors":"Davide Grande , Roberto Buizza , Andrea Storto","doi":"10.1016/j.ocemod.2026.102678","DOIUrl":"10.1016/j.ocemod.2026.102678","url":null,"abstract":"<div><div>This review examines recent advances in the application of machine learning to ocean data assimilation, covering contributions published between 2020 and 2025. We identify emerging trends, recurring limitations, and critical open questions, structuring the discussion around four scientific challenges: observation integration, boundary treatment, fine-scale process representation, and physical consistency. While convolutional neural networks remain widely used, particularly in bias correction and super-resolution tasks, recent studies increasingly employ multilayer perceptrons, long short-term memories, transformers and neural operators for error estimation, sequential bias correction, and latent-space assimilation. Despite this architectural diversity, most contributions remain confined to idealized configurations or offline modules, with limited evidence of generalization and integration into operational pipelines. We conclude that hybrid systems combining embedded physical knowledge with systematic validation across different oceanic regimes will be essential to unlock the full potential of machine learning-enhanced ocean data assimilation.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102678"},"PeriodicalIF":2.9,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938700","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}
Pub Date : 2026-01-03DOI: 10.1016/j.ocemod.2026.102680
Wenfan Wu , Zhengui Wang , Y Joseph Zhang , Jian Shen , Richard Tian , Lewis C Linker , Carl F Cerco
Estuaries, as transitional zones between land and ocean, exhibit highly nonlinear, cross-scale hydrodynamic processes that present substantial challenges for numerical modeling. Using Chesapeake Bay as an example, we demonstrate a physically based calibration procedure with observation-derived parametrizations, together with a high-resolution unstructured model without bathymetry smoothing. The results indicate that highly turbid water greatly affects the downward penetration of solar radiation, particularly in the upper Bay and tributaries. By incorporating the spatially varying Jerlov water types derived from satellite-based Kd490 data, we systematically improve water temperature simulations across the Bay, reducing the average RMSE to 0.484 °C (0.775 °C) for surface (bottom) temperature at 121 long-term monitoring stations maintained by EPA's Chesapeake Bay Program. Moreover, the presence of mud layers is found to facilitate tidal propagation in tributaries, thereby enhancing saltwater intrusion there. By applying spatially varying bottom drag coefficients calculated from the observed sediment types, we achieve significant improvements in salinity simulations, with an average RMSE of 0.809 PSU (1.331 PSU) for surface (bottom) salinity. In general, the present study reduces temperature and salinity errors by ∼60 % compared to previous modeling studies in the Bay. This study underscores the advantages of physically based calibration procedures that help make the model results more defensible.
{"title":"Improving cross-scale hydrodynamic simulations in the Chesapeake Bay with physically based calibration","authors":"Wenfan Wu , Zhengui Wang , Y Joseph Zhang , Jian Shen , Richard Tian , Lewis C Linker , Carl F Cerco","doi":"10.1016/j.ocemod.2026.102680","DOIUrl":"10.1016/j.ocemod.2026.102680","url":null,"abstract":"<div><div>Estuaries, as transitional zones between land and ocean, exhibit highly nonlinear, cross-scale hydrodynamic processes that present substantial challenges for numerical modeling. Using Chesapeake Bay as an example, we demonstrate a physically based calibration procedure with observation-derived parametrizations, together with a high-resolution unstructured model without bathymetry smoothing. The results indicate that highly turbid water greatly affects the downward penetration of solar radiation, particularly in the upper Bay and tributaries. By incorporating the spatially varying Jerlov water types derived from satellite-based Kd490 data, we systematically improve water temperature simulations across the Bay, reducing the average RMSE to 0.484 °C (0.775 °C) for surface (bottom) temperature at 121 long-term monitoring stations maintained by EPA's Chesapeake Bay Program. Moreover, the presence of mud layers is found to facilitate tidal propagation in tributaries, thereby enhancing saltwater intrusion there. By applying spatially varying bottom drag coefficients calculated from the observed sediment types, we achieve significant improvements in salinity simulations, with an average RMSE of 0.809 PSU (1.331 PSU) for surface (bottom) salinity. In general, the present study reduces temperature and salinity errors by ∼60 % compared to previous modeling studies in the Bay. This study underscores the advantages of physically based calibration procedures that help make the model results more defensible.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102680"},"PeriodicalIF":2.9,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976936","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}
Pub Date : 2026-01-03DOI: 10.1016/j.ocemod.2026.102679
Peng Liang , Yonghao Liang , Qiang Wang , Lina Yang , Tianyu Zhang
Kuroshio intrusion (KI) is a critical linkage between the Pacific and the South China Sea (SCS), profoundly influencing the variability of marine dynamical and ecological processes of the SCS. Due to the complex mechanism and the lack of predictability study on KI, the accuracy of KI prediction remains limited. This study obtains the fastest growing initial errors (FGIEs) of KI using the Regional Ocean Modelling System (ROMS) and conditional nonlinear optimal perturbation (CNOP) method. Specifically, the CNOP, which is an effective method in calculating FGIEs in a nonlinear system, refers to the perturbation that can lead to the maximum of an objective function at a target time under certain constraints. The calculation results reveal two types of FGIEs with similar spatial patterns but opposite signs. When superimposed on the background field, both types of errors exhibit rapid growth and northwestward propagation. At prediction time, the CNOP+ (with positive sea surface height error) and CNOP- (with negative sea surface height error) errors respectively cause significant overestimation and underestimation of KI. Notably, CNOP- errors may even lead to complete failure in predicting the occurrence of KI. The rapid error growth primarily originates from barotropic instability induced by the zonal velocity shear of the reference state. Sensitive areas for targeted observations, identified through vertical integration of initial total energy error, extend northwestward from the southern Luzon Strait to the interior SCS, centered near 120.5°E, 20°N. Remarkably, removing initial errors within this sensitive area (covering merely 0.1 % of the total model domain) can improve KI prediction accuracy most effectively, by 25 %∼38 %. This research provides an effective guidance for the design of targeted observation strategies, having great significance in improving the prediction skill of KI.
{"title":"The fastest growing initial error and identification of sensitive area for targeted observation in predicting the Kuroshio intrusion into the South China Sea with a high-resolution regional ocean model","authors":"Peng Liang , Yonghao Liang , Qiang Wang , Lina Yang , Tianyu Zhang","doi":"10.1016/j.ocemod.2026.102679","DOIUrl":"10.1016/j.ocemod.2026.102679","url":null,"abstract":"<div><div>Kuroshio intrusion (KI) is a critical linkage between the Pacific and the South China Sea (SCS), profoundly influencing the variability of marine dynamical and ecological processes of the SCS. Due to the complex mechanism and the lack of predictability study on KI, the accuracy of KI prediction remains limited. This study obtains the fastest growing initial errors (FGIEs) of KI using the Regional Ocean Modelling System (ROMS) and conditional nonlinear optimal perturbation (CNOP) method. Specifically, the CNOP, which is an effective method in calculating FGIEs in a nonlinear system, refers to the perturbation that can lead to the maximum of an objective function at a target time under certain constraints. The calculation results reveal two types of FGIEs with similar spatial patterns but opposite signs. When superimposed on the background field, both types of errors exhibit rapid growth and northwestward propagation. At prediction time, the CNOP+ (with positive sea surface height error) and CNOP- (with negative sea surface height error) errors respectively cause significant overestimation and underestimation of KI. Notably, CNOP- errors may even lead to complete failure in predicting the occurrence of KI. The rapid error growth primarily originates from barotropic instability induced by the zonal velocity shear of the reference state. Sensitive areas for targeted observations, identified through vertical integration of initial total energy error, extend northwestward from the southern Luzon Strait to the interior SCS, centered near 120.5°E, 20°N. Remarkably, removing initial errors within this sensitive area (covering merely 0.1 % of the total model domain) can improve KI prediction accuracy most effectively, by 25 %∼38 %. This research provides an effective guidance for the design of targeted observation strategies, having great significance in improving the prediction skill of KI.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"201 ","pages":"Article 102679"},"PeriodicalIF":2.9,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171904","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}
Pub Date : 2026-01-02DOI: 10.1016/j.ocemod.2026.102677
Qingyu Zheng , Qi Shao , Guijun Han , Wei Li , Hong Li , Xuan Wang
Advances in ocean observation technology have significantly enhanced the accuracy of Earth system forecasting. Reconstructing missing information of nonlinear evolution processes from observational data is essential for investigating rapid changes in the marine environment and climate. However, traditional methods often struggle to extract unseen nonlinear processes from data. In fact, a large amount of dynamic evolution information hidden in historical data has not been effectively mined. To address this issue, we propose DeepDA, a latent space data assimilation approach based on deep learning. DeepDA employs a generative deep learning model to capture complex spatiotemporal multiscale features and nonlinear evolution processes in observations. By incorporating an attention mechanism, DeepDA effectively assimilates rich historical information of sea surface temperature. The results show that DeepDA remains highly stable in generating nonlinear evolution even with extensive data gaps and high noise levels. Notably, when only 10% (sparse sampling) of observation is available, the increase in error for DeepDA is limited to 40% compared to the case with complete data. Furthermore, DeepDA demonstrates effectiveness in multiscale reconstruction and analysis of climate variability, generating nonlinear patterns that are more physically consistent than linear methods. The nonlinear features extracted from the latent space exhibit multiscale structures, which may provide new insights into enhancing ocean data assimilation.
{"title":"Generating unseen nonlinear evolution in the ocean using deep learning-based latent space data assimilation model","authors":"Qingyu Zheng , Qi Shao , Guijun Han , Wei Li , Hong Li , Xuan Wang","doi":"10.1016/j.ocemod.2026.102677","DOIUrl":"10.1016/j.ocemod.2026.102677","url":null,"abstract":"<div><div>Advances in ocean observation technology have significantly enhanced the accuracy of Earth system forecasting. Reconstructing missing information of nonlinear evolution processes from observational data is essential for investigating rapid changes in the marine environment and climate. However, traditional methods often struggle to extract unseen nonlinear processes from data. In fact, a large amount of dynamic evolution information hidden in historical data has not been effectively mined. To address this issue, we propose DeepDA, a latent space data assimilation approach based on deep learning. DeepDA employs a generative deep learning model to capture complex spatiotemporal multiscale features and nonlinear evolution processes in observations. By incorporating an attention mechanism, DeepDA effectively assimilates rich historical information of sea surface temperature. The results show that DeepDA remains highly stable in generating nonlinear evolution even with extensive data gaps and high noise levels. Notably, when only 10% (sparse sampling) of observation is available, the increase in error for DeepDA is limited to 40% compared to the case with complete data. Furthermore, DeepDA demonstrates effectiveness in multiscale reconstruction and analysis of climate variability, generating nonlinear patterns that are more physically consistent than linear methods. The nonlinear features extracted from the latent space exhibit multiscale structures, which may provide new insights into enhancing ocean data assimilation.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102677"},"PeriodicalIF":2.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976937","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}
Pub Date : 2025-12-29DOI: 10.1016/j.ocemod.2025.102676
Silje Christine Iversen , Ann Kristin Sperrevik , Kai Håkon Christensen
Infrared satellite sea surface temperature (SST) observations capture the strong warming signal at the surface of the ocean during diurnal warming events. Assimilating these observations into ocean forecast models might introduce forecast errors, as the models do not fully resolve the strong diurnal cycle captured by SST observations. A standard approach is to remove affected SST observations from the assimilated dataset using a simplistic filter. However, this approach also removes observations not affected by diurnal warming. For high northern latitudes, where diurnal warming events are less frequent than at lower latitudes, it is unclear whether observations affected by strong warming negatively impact the forecasts, which would justify applying the filter. Here, we explore the consequences of both assimilating diurnal warming-affected SSTs and applying the filter using an ocean forecast model covering the seas off Norway. Through data assimilation experiments assimilating synthetic SSTs, we find that the strong warming signal in observations impacts forecasts in undesirable ways. The surface warming spreads below the mixed layer, decreasing its depth, and changes made to the model below the mixed layer persist into subsequent forecasts. Applying the filter reduces assimilated observations by 46 % without degrading forecasts, suggesting a redundancy in satellite SSTs.
{"title":"How does assimilating satellite SSTs affected by strong diurnal warming impact higher latitude ocean forecasts?","authors":"Silje Christine Iversen , Ann Kristin Sperrevik , Kai Håkon Christensen","doi":"10.1016/j.ocemod.2025.102676","DOIUrl":"10.1016/j.ocemod.2025.102676","url":null,"abstract":"<div><div>Infrared satellite sea surface temperature (SST) observations capture the strong warming signal at the surface of the ocean during diurnal warming events. Assimilating these observations into ocean forecast models might introduce forecast errors, as the models do not fully resolve the strong diurnal cycle captured by SST observations. A standard approach is to remove affected SST observations from the assimilated dataset using a simplistic filter. However, this approach also removes observations not affected by diurnal warming. For high northern latitudes, where diurnal warming events are less frequent than at lower latitudes, it is unclear whether observations affected by strong warming negatively impact the forecasts, which would justify applying the filter. Here, we explore the consequences of both assimilating diurnal warming-affected SSTs and applying the filter using an ocean forecast model covering the seas off Norway. Through data assimilation experiments assimilating synthetic SSTs, we find that the strong warming signal in observations impacts forecasts in undesirable ways. The surface warming spreads below the mixed layer, decreasing its depth, and changes made to the model below the mixed layer persist into subsequent forecasts. Applying the filter reduces assimilated observations by 46 % without degrading forecasts, suggesting a redundancy in satellite SSTs.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102676"},"PeriodicalIF":2.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938697","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}
Pub Date : 2025-12-22DOI: 10.1016/j.ocemod.2025.102675
Óscar A. Caballero-Martínez , Carmen Zarzuelo , Gabriel Navarro , I. Emma Huertas , Antonio Tovar-Sánchez , Eugenio Fraile-Nuez , Marcos Larrad-Revuelto , Manuel Díez-Minguito
Port Foster (Deception Island, Antarctica) is a semi-enclosed flooded caldera, connected to the Southern Ocean through its narrow inlet (Neptune’s Bellows), whereby the water exchange with the Bransfield Strait takes place. This study addresses tidally-induced sea level variations and horizontal currents at intratidal and subtidal time scales in Port Foster, focusing on the inlet. The approach relies on a comprehensive field campaign and simulations performed with a complex computational hydrodynamical model. Tides are synchronous, mesotidal, and mixed, mainly semidiurnal. Phase lags between water levels and horizontal currents are near . Therefore, Port Foster is dynamically short regarding tidal propagation. The constituent dominates water levels and currents, with a weak ebb dominance observed. At a tidal scale, peak currents occur in Neptune’s Bellows (with modelled data close to ) with an east–west direction in tidal ellipses, while inside Port Foster, currents are much weaker (). The numerical model reveals complex semidiurnal circulation in the inlet, including a counter-clockwise eddy forming during flood periods. This pattern produces different rotation directions of the semidiurnal and diurnal tidal ellipses. At a subtidal scale, residual currents attain values up to in the inlet. They are negligible elsewhere. The potential residual bedload transport exhibits a pattern similar to that of the residual current. Residual eddies on either side of Neptune’s Bellows, with opposing rotations, indicate limited water exchange between Port Foster and the Bransfield Strait, resulting in a flushing time of approximately 75 days.
{"title":"Barotropic tides and residual transport in Port Foster (Deception Island, Antarctica)","authors":"Óscar A. Caballero-Martínez , Carmen Zarzuelo , Gabriel Navarro , I. Emma Huertas , Antonio Tovar-Sánchez , Eugenio Fraile-Nuez , Marcos Larrad-Revuelto , Manuel Díez-Minguito","doi":"10.1016/j.ocemod.2025.102675","DOIUrl":"10.1016/j.ocemod.2025.102675","url":null,"abstract":"<div><div>Port Foster (Deception Island, Antarctica) is a semi-enclosed flooded caldera, connected to the Southern Ocean through its narrow inlet (Neptune’s Bellows), whereby the water exchange with the Bransfield Strait takes place. This study addresses tidally-induced sea level variations and horizontal currents at intratidal and subtidal time scales in Port Foster, focusing on the inlet. The approach relies on a comprehensive field campaign and simulations performed with a complex computational hydrodynamical model. Tides are synchronous, mesotidal, and mixed, mainly semidiurnal. Phase lags between water levels and horizontal currents are near <span><math><mrow><mi>π</mi><mo>/</mo><mn>2</mn></mrow></math></span>. Therefore, Port Foster is dynamically short regarding tidal propagation. The <span><math><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> constituent dominates water levels and currents, with a weak ebb dominance observed. At a tidal scale, peak currents occur in Neptune’s Bellows (with modelled data close to <span><math><mrow><mn>0</mn><mo>.</mo><mn>90</mn><mspace></mspace><mi>m</mi><mspace></mspace><msup><mrow><mi>s</mi></mrow><mrow><mtext>-</mtext><mn>1</mn></mrow></msup></mrow></math></span>) with an east–west direction in tidal ellipses, while inside Port Foster, currents are much weaker (<span><math><mrow><mo>∼</mo><mn>0</mn><mo>.</mo><mn>05</mn><mspace></mspace><mi>m</mi><mspace></mspace><msup><mrow><mi>s</mi></mrow><mrow><mtext>-</mtext><mn>1</mn></mrow></msup></mrow></math></span>). The numerical model reveals complex semidiurnal circulation in the inlet, including a counter-clockwise eddy forming during flood periods. This pattern produces different rotation directions of the semidiurnal and diurnal tidal ellipses. At a subtidal scale, residual currents attain values up to <span><math><mrow><mn>0</mn><mo>.</mo><mn>10</mn><mspace></mspace><mi>m</mi><mspace></mspace><msup><mrow><mi>s</mi></mrow><mrow><mtext>-</mtext><mn>1</mn></mrow></msup></mrow></math></span> in the inlet. They are negligible elsewhere. The potential residual bedload transport exhibits a pattern similar to that of the residual current. Residual eddies on either side of Neptune’s Bellows, with opposing rotations, indicate limited water exchange between Port Foster and the Bransfield Strait, resulting in a flushing time of approximately 75 days.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"200 ","pages":"Article 102675"},"PeriodicalIF":2.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840659","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}
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