Pub Date : 2025-10-28DOI: 10.1016/j.ocemod.2025.102642
Hyungju Yoo , Haocheng Yu , Y. Joseph Zhang , Wenfan Wu , Fei Ye , Saeed Moghimi , Gregory Seroka , Zizang Yang , Edward Myers
Simulating Total Water Level (TWL) at continental scale is inherently challenging and it is often desirable to correct model bias a posteriori. Here we present a simple yet effective bias correction method for NOAA’s STOFS-3D (Three-Dimensional Surge and Tide Operational Forecast System) forecasting system. The method seeks to dynamically correct the model bias, calculated from the results from the previous 2 days, by compensating it with an adjusted non-tidal elevation boundary condition. The adjustment is spatially uniform but varies over each forecast cycle. We demonstrate that the existing 3D model bias is largely attributable to the model’s exclusion of the large-scale steric effect, and therefore the method can be effectively used to incorporate this effect into the 3D model. Assessment at over 140 NOAA stations in US east and Gulf coasts show significant reductions in biases and root-mean-square errors for the non-tidal elevation and TWL, while having a small impact on tides and surges during extreme conditions.
{"title":"A bias correction method for total water level prediction at continental scale","authors":"Hyungju Yoo , Haocheng Yu , Y. Joseph Zhang , Wenfan Wu , Fei Ye , Saeed Moghimi , Gregory Seroka , Zizang Yang , Edward Myers","doi":"10.1016/j.ocemod.2025.102642","DOIUrl":"10.1016/j.ocemod.2025.102642","url":null,"abstract":"<div><div>Simulating Total Water Level (TWL) at continental scale is inherently challenging and it is often desirable to correct model bias <em>a posteriori</em>. Here we present a simple yet effective bias correction method for NOAA’s STOFS-3D (Three-Dimensional Surge and Tide Operational Forecast System) forecasting system. The method seeks to dynamically correct the model bias, calculated from the results from the previous 2 days, by compensating it with an adjusted non-tidal elevation boundary condition. The adjustment is spatially uniform but varies over each forecast cycle. We demonstrate that the existing 3D model bias is largely attributable to the model’s exclusion of the large-scale steric effect, and therefore the method can be effectively used to incorporate this effect into the 3D model. Assessment at over 140 NOAA stations in US east and Gulf coasts show significant reductions in biases and root-mean-square errors for the non-tidal elevation and TWL, while having a small impact on tides and surges during extreme conditions.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102642"},"PeriodicalIF":2.9,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516668","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 : 2025-10-28DOI: 10.1016/j.ocemod.2025.102643
Fei Ye , Y. Joseph Zhang , Haocheng Yu , Felicio Cassalho , Julio Zyserman , Soroosh Mani , Saeed Moghimi , Hyungju Yoo , Greg Seroka , Zizang Yang , Edward Myers
Accurate simulation of compound flooding in the coastal transition zone requires a fully coupled hydrologic–hydrodynamic modeling system to capture the complex interactions between inland and oceanic floodwaters. Despite recent advances in fully coupled 3D modeling frameworks, significant challenges persist in resolving flow through intricate river networks, especially where small channels are poorly represented due to limitations in digital elevation models (DEMs). This study addresses these challenges by enhancing the model meshing process and evaluating coupling strategies in the lower Mississippi River region, a representative coastal transition zone with a dense and complex river network. We improve a previously developed semi-automatic meshing approach by incorporating the National Hydrography Dataset to ensure clean delineation and connectivity of small channels where DEM uncertainties often cause artificial blockages. We also assess two strategies for integrating hydrologic model outputs into the hydrodynamic domain: (1) a conventional “hand-off” method that imposes freshwater streamflows at the land boundary combined with spatially varying precipitation, and (2) an alternative scheme that distributes hydrologic outputs at every resolved channel within the hydrodynamic mesh. Results show that the enhanced mesh, combined with updated topographic data, substantially reduces domain-wide bias and improves water-level skill at inland USGS stations. The alternative coupling scheme produces results comparable to the base method, providing an extensible framework for potential future development. By improving inland channel resolution and establishing a pathway for deeper coupling with hydrologic models, this work strengthens the scientific foundation and contributes to the operational readiness of compound flood forecasting.
{"title":"Improving compound flood modeling skill in coastal transition zones","authors":"Fei Ye , Y. Joseph Zhang , Haocheng Yu , Felicio Cassalho , Julio Zyserman , Soroosh Mani , Saeed Moghimi , Hyungju Yoo , Greg Seroka , Zizang Yang , Edward Myers","doi":"10.1016/j.ocemod.2025.102643","DOIUrl":"10.1016/j.ocemod.2025.102643","url":null,"abstract":"<div><div>Accurate simulation of compound flooding in the coastal transition zone requires a fully coupled hydrologic–hydrodynamic modeling system to capture the complex interactions between inland and oceanic floodwaters. Despite recent advances in fully coupled 3D modeling frameworks, significant challenges persist in resolving flow through intricate river networks, especially where small channels are poorly represented due to limitations in digital elevation models (DEMs). This study addresses these challenges by enhancing the model meshing process and evaluating coupling strategies in the lower Mississippi River region, a representative coastal transition zone with a dense and complex river network. We improve a previously developed semi-automatic meshing approach by incorporating the National Hydrography Dataset to ensure clean delineation and connectivity of small channels where DEM uncertainties often cause artificial blockages. We also assess two strategies for integrating hydrologic model outputs into the hydrodynamic domain: (1) a conventional “hand-off” method that imposes freshwater streamflows at the land boundary combined with spatially varying precipitation, and (2) an alternative scheme that distributes hydrologic outputs at every resolved channel within the hydrodynamic mesh. Results show that the enhanced mesh, combined with updated topographic data, substantially reduces domain-wide bias and improves water-level skill at inland USGS stations. The alternative coupling scheme produces results comparable to the base method, providing an extensible framework for potential future development. By improving inland channel resolution and establishing a pathway for deeper coupling with hydrologic models, this work strengthens the scientific foundation and contributes to the operational readiness of compound flood forecasting.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102643"},"PeriodicalIF":2.9,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466587","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 : 2025-10-27DOI: 10.1016/j.ocemod.2025.102641
Zijian Cui , Tao Ding , Beifeng Zhou , Chujin Liang , Weifang Jin , Feilong Lin
Modern remote sensing techniques can now systematically extract coherent internal tidal signals (mode-1 and mode-2) from global sea surface height measurements. This capability arises from the accumulation of multi-source satellite altimetry data. However, the steady-state internal tides constructed by this method have limitations. They cannot fully characterize how dynamic oceanographic environmental variations influence internal tides. In realistic oceanic conditions, stratification and background currents significantly modulate the phase velocity and amplitude of internal tides. This modulation significantly enhances the energy proportion of incoherent internal tides. This study proposes applying the Gaussian beam superposition method to the Wavefront model to improve its capability in calculating internal tide energy evolution within complex oceanic environments, with validation provided by two sets of mooring observations from the northern South China Sea. The developed approach demonstrates potential for modeling time-varying patterns in global internal tide energy distribution under varying stratification and background current conditions.
{"title":"Application of Gaussian beam superposition method in the Wavefront model for internal tides","authors":"Zijian Cui , Tao Ding , Beifeng Zhou , Chujin Liang , Weifang Jin , Feilong Lin","doi":"10.1016/j.ocemod.2025.102641","DOIUrl":"10.1016/j.ocemod.2025.102641","url":null,"abstract":"<div><div>Modern remote sensing techniques can now systematically extract coherent internal tidal signals (mode-1 and mode-2) from global sea surface height measurements. This capability arises from the accumulation of multi-source satellite altimetry data. However, the steady-state internal tides constructed by this method have limitations. They cannot fully characterize how dynamic oceanographic environmental variations influence internal tides. In realistic oceanic conditions, stratification and background currents significantly modulate the phase velocity and amplitude of internal tides. This modulation significantly enhances the energy proportion of incoherent internal tides. This study proposes applying the Gaussian beam superposition method to the Wavefront model to improve its capability in calculating internal tide energy evolution within complex oceanic environments, with validation provided by two sets of mooring observations from the northern South China Sea. The developed approach demonstrates potential for modeling time-varying patterns in global internal tide energy distribution under varying stratification and background current conditions.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102641"},"PeriodicalIF":2.9,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416901","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 : 2025-10-15DOI: 10.1016/j.ocemod.2025.102640
Hao Yin , Jie Su , Jiping Liu , Mingfeng Wang
Snow density plays crucial roles in snow and sea ice thermodynamics. However, current coupled global climate models typically rely on empirical constants for snow properties in sea ice model components, limiting our understanding of how snow processes influence snow and sea ice evolution. To address this, we implemented a layered snow density parameterization in the Los Alamos Sea Ice Model (CICE), which explicitly considers strain compaction, wind-driven compaction, and fresh snow deposition. Compared to the control run, our experiments show that this scheme reduces wintertime positive bias in snow depth and cold bias in snow temperature in the Arctic. The reduction in winter conductivity heat loss accounts for the improvement in temperature biases, resulting in an enhanced net surface energy gain in the winter. Eighty-five percent of this additional energy gain is attributed solely to the density-dependent variation of the snow thermal conductivity over the Arctic. Further spatiotemporal analysis reveals distinct seasonal difference in the drivers of snow depth and density changes. Wind compaction and snowfall emerge as competing processes in winter, while ablation dominates during June and July. Their contributions to pan-Arctic multi-year mean snow density change are +0.161 (wind compaction), -0.198 (snowfall), +0.016 (strain compaction), +0.012 (phase changes), and -0.003 (snow-ice) kg·m-3·hr-1. The corresponding rates of snow depth changes are -0.095, +0.277, -0.020, -0.103, and -0.009 cm·day-1.
{"title":"Impacts of a layered snow density evolution scheme on Arctic snow and sea ice simulation in the CICE sea ice model","authors":"Hao Yin , Jie Su , Jiping Liu , Mingfeng Wang","doi":"10.1016/j.ocemod.2025.102640","DOIUrl":"10.1016/j.ocemod.2025.102640","url":null,"abstract":"<div><div>Snow density plays crucial roles in snow and sea ice thermodynamics. However, current coupled global climate models typically rely on empirical constants for snow properties in sea ice model components, limiting our understanding of how snow processes influence snow and sea ice evolution. To address this, we implemented a layered snow density parameterization in the Los Alamos Sea Ice Model (CICE), which explicitly considers strain compaction, wind-driven compaction, and fresh snow deposition. Compared to the control run, our experiments show that this scheme reduces wintertime positive bias in snow depth and cold bias in snow temperature in the Arctic. The reduction in winter conductivity heat loss accounts for the improvement in temperature biases, resulting in an enhanced net surface energy gain in the winter. Eighty-five percent of this additional energy gain is attributed solely to the density-dependent variation of the snow thermal conductivity over the Arctic. Further spatiotemporal analysis reveals distinct seasonal difference in the drivers of snow depth and density changes. Wind compaction and snowfall emerge as competing processes in winter, while ablation dominates during June and July. Their contributions to pan-Arctic multi-year mean snow density change are +0.161 (wind compaction), -0.198 (snowfall), +0.016 (strain compaction), +0.012 (phase changes), and -0.003 (snow-ice) kg·m<sup>-3</sup>·hr<sup>-1</sup>. The corresponding rates of snow depth changes are -0.095, +0.277, -0.020, -0.103, and -0.009 cm·day<sup>-1</sup>.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102640"},"PeriodicalIF":2.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416782","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 : 2025-10-15DOI: 10.1016/j.ocemod.2025.102639
Dongliang Shen, Xiaofeng Li
The Oceanic responses to Super Typhoon Bolaven (2023) in the Northwest Pacific Ocean are simulated and investigated by the Regional Ocean Modeling System (ROMS) integrated with a Machine Learning (ML) based ocean vertical mixing parameterization (OVMP) scheme. Traditional OVMP schemes, such as MY25 and KPP, underestimate the ocean vertical mixing processes under typhoon condition. To address this limitation, vertical eddy viscosity (Km) data were generated under Typhoon Bolaven using the high-resolution Parallelized Large Eddy Simulation Model (PALM) and used to train a XGBoost-based ML model. This XGBoost model is used to form a ML-based OVMP scheme and integrated into ROMS model via Forpy coupler. The results indicate that ROMS-ML coupled model can significantly improve the simulations of sea surface temperature (SST) cooling and subsurface thermal structure compared to traditional OVMP schemes. The ML-based OVMP scheme estimates stronger ocean vertical mixing under Typhoon Bolaven, enhancing the upper-oean heat redistribution and aligning more closely with the satellite and in-situ observations. Thermodynamic analyses reveal that the temperature cooling in the upper ocean is primarily driven by strong ocean vertical mixing, latent heat loss, and vertical advection. Notably, the structure of the North Pacific Subtropical Mode Water (STMW) was altered by Typhoon Bolaven, with reductions in its area and thickness, suggesting a weakened heat reservoir and potential impact on regional climate buffering. Momentum energy analyses confirm that vertical viscosity is the dominant contributor to oceanic energy input during Typhoon Bolaven, promoting local eddy generation and associated cooling. Moreover, additional diagnostics under Typhoon Haikui (2023) indicate that while the ML-based OVMP scheme captures localized cooling more accurately than traditional schemes, it tends to overestimate vertical mixing in regions with complex circulation and steep bathymetry. Overall, this study highlights the potential of physics-informed ML approaches in improving the accuracy of ocean simulations under extreme weather events, offering a promising pathway for improving coupled atmosphere–ocean prediction systems under climate change with more frequent super typhoons.
{"title":"Simulating oceanic responses to Super Typhoon Bolaven (2023) in the Northwest Pacific Ocean using a numerical model coupled with machine learning-based ocean vertical mixing parameterization","authors":"Dongliang Shen, Xiaofeng Li","doi":"10.1016/j.ocemod.2025.102639","DOIUrl":"10.1016/j.ocemod.2025.102639","url":null,"abstract":"<div><div>The Oceanic responses to Super Typhoon Bolaven (2023) in the Northwest Pacific Ocean are simulated and investigated by the Regional Ocean Modeling System (ROMS) integrated with a Machine Learning (ML) based ocean vertical mixing parameterization (OVMP) scheme. Traditional OVMP schemes, such as MY25 and KPP, underestimate the ocean vertical mixing processes under typhoon condition. To address this limitation, vertical eddy viscosity (Km) data were generated under Typhoon Bolaven using the high-resolution Parallelized Large Eddy Simulation Model (PALM) and used to train a XGBoost-based ML model. This XGBoost model is used to form a ML-based OVMP scheme and integrated into ROMS model via Forpy coupler. The results indicate that ROMS-ML coupled model can significantly improve the simulations of sea surface temperature (SST) cooling and subsurface thermal structure compared to traditional OVMP schemes. The ML-based OVMP scheme estimates stronger ocean vertical mixing under Typhoon Bolaven, enhancing the upper-oean heat redistribution and aligning more closely with the satellite and in-situ observations. Thermodynamic analyses reveal that the temperature cooling in the upper ocean is primarily driven by strong ocean vertical mixing, latent heat loss, and vertical advection. Notably, the structure of the North Pacific Subtropical Mode Water (STMW) was altered by Typhoon Bolaven, with reductions in its area and thickness, suggesting a weakened heat reservoir and potential impact on regional climate buffering. Momentum energy analyses confirm that vertical viscosity is the dominant contributor to oceanic energy input during Typhoon Bolaven, promoting local eddy generation and associated cooling. Moreover, additional diagnostics under Typhoon Haikui (2023) indicate that while the ML-based OVMP scheme captures localized cooling more accurately than traditional schemes, it tends to overestimate vertical mixing in regions with complex circulation and steep bathymetry. Overall, this study highlights the potential of physics-informed ML approaches in improving the accuracy of ocean simulations under extreme weather events, offering a promising pathway for improving coupled atmosphere–ocean prediction systems under climate change with more frequent super typhoons.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102639"},"PeriodicalIF":2.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324806","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}
Langmuir turbulence in shallow-water coastal environments can reach the seafloor, developing into Langmuir supercells, which enhance size and mixing intensity. Two fundamental issues in coastal Langmuir turbulence remain unclear: (i) the energy cycle of the turbulence under different circumstances, and (ii) its effect on vertical mixing. We investigate these issues using large eddy simulations, considering aligned and opposing wind-wave and current directions. Results show that Langmuir supercells possess an intense full-column, narrow-band energetic mode, distinct from Langmuir turbulence in the energy spectrum. This mode occurs with aligned wind/wave and current directions but disappears when they oppose. In the latter case, only Langmuir and shear turbulence exist near surface and bottom boundaries; moreover, despite no stratification in simulations, their intensities are suppressed by a mid-layer barrier that limits surface-bottom interaction. When Langmuir supercells are present, the surface-bottom exchange of momentum is highly asymmetric between upwelling and downwelling limbs. Strong connections between surface and bottom turbulence, as indicated by the vortex-tube-connection events, can only be found in upwelling regions. As a result, the upwelling motions contribute considerably more to the momentum flux than the downwelling motions. All these results indicate that, despite the windrow pattern on the ocean surface from near-surface wind-wave interaction, whether full-column supercells can be activated or suppressed depends on different interactions between near-surface wind-wave forcing and near-bottom shear forcing. Once Langmuir supercells are activated, they differ significantly from Langmuir turbulence from the perspectives of energy and momentum transport; therefore, they cannot be simply treated as a “full column” version of Langmuir turbulence.
{"title":"The role of longitudinal alignment between surface and bottom forcing on the full-column turbulence mixing in the coastal ocean","authors":"Jiahao Huang , Marcelo Chamecki , Qing Li , Bicheng Chen","doi":"10.1016/j.ocemod.2025.102637","DOIUrl":"10.1016/j.ocemod.2025.102637","url":null,"abstract":"<div><div>Langmuir turbulence in shallow-water coastal environments can reach the seafloor, developing into Langmuir supercells, which enhance size and mixing intensity. Two fundamental issues in coastal Langmuir turbulence remain unclear: (i) the energy cycle of the turbulence under different circumstances, and (ii) its effect on vertical mixing. We investigate these issues using large eddy simulations, considering aligned and opposing wind-wave and current directions. Results show that Langmuir supercells possess an intense full-column, narrow-band energetic mode, distinct from Langmuir turbulence in the energy spectrum. This mode occurs with aligned wind/wave and current directions but disappears when they oppose. In the latter case, only Langmuir and shear turbulence exist near surface and bottom boundaries; moreover, despite no stratification in simulations, their intensities are suppressed by a mid-layer barrier that limits surface-bottom interaction. When Langmuir supercells are present, the surface-bottom exchange of momentum is highly asymmetric between upwelling and downwelling limbs. Strong connections between surface and bottom turbulence, as indicated by the vortex-tube-connection events, can only be found in upwelling regions. As a result, the upwelling motions contribute considerably more to the momentum flux than the downwelling motions. All these results indicate that, despite the windrow pattern on the ocean surface from near-surface wind-wave interaction, whether full-column supercells can be activated or suppressed depends on different interactions between near-surface wind-wave forcing and near-bottom shear forcing. Once Langmuir supercells are activated, they differ significantly from Langmuir turbulence from the perspectives of energy and momentum transport; therefore, they cannot be simply treated as a “full column” version of Langmuir turbulence.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102637"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267922","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 : 2025-10-09DOI: 10.1016/j.ocemod.2025.102636
Julie Cheynel , Lucia Pineau-Guillou , Pascal Lazure , Marta Marcos , Florent Lyard , Nicolas Raillard
Changes in extreme sea levels, combined with the growth of coastal population, are critical factors in evaluating the risks related to coastal flooding. Thus, studying the variability and trends of storm surges, a major contributor to extreme sea levels, becomes essential for coastal protection policies. We developed in the North Atlantic the first hourly surge hindcast covering the full 20th century (1900–2015) on a 0.1°grid, and called ClimEx hindcast. We validated the hindcast against 34 long-term tide gauges. The model shows overall very good performance for surges (Root Mean Square Error of 9.3 cm on average), and good performance for extreme surges, despite an overall underestimation. To investigate the variability and trends in storm surges, we performed a non-stationary extreme value analysis on modeled and observed storm surges. The seasonality of storm surges is highly dependent on the area. The seasonal amplitude varies from typically 10 cm, to more than 40 cm in the North Sea. The storm surge season occurs around December–January in the north of the domain (above 40°N), due to winter extra-tropical cyclones, and around September–October in the south-west, due to tropical cyclones. The dependence of storm surges with the North Atlantic Oscillation extends from the coasts to the deep ocean, and is positive above 50°N and negative below. Observed storm surges show mostly non significant or small trends ( 1 mm/yr), while the model displays positive trends almost everywhere, possibly due to inhomogeneities in the atmospheric forcing dataset prior to 1950.
{"title":"A secular sea level hindcast (1900–2015) to investigate extreme surges variability and trends in the North Atlantic","authors":"Julie Cheynel , Lucia Pineau-Guillou , Pascal Lazure , Marta Marcos , Florent Lyard , Nicolas Raillard","doi":"10.1016/j.ocemod.2025.102636","DOIUrl":"10.1016/j.ocemod.2025.102636","url":null,"abstract":"<div><div>Changes in extreme sea levels, combined with the growth of coastal population, are critical factors in evaluating the risks related to coastal flooding. Thus, studying the variability and trends of storm surges, a major contributor to extreme sea levels, becomes essential for coastal protection policies. We developed in the North Atlantic the first hourly surge hindcast covering the full 20th century (1900–2015) on a 0.1°grid, and called ClimEx hindcast. We validated the hindcast against 34 long-term tide gauges. The model shows overall very good performance for surges (Root Mean Square Error of 9.3 cm on average), and good performance for extreme surges, despite an overall underestimation. To investigate the variability and trends in storm surges, we performed a non-stationary extreme value analysis on modeled and observed storm surges. The seasonality of storm surges is highly dependent on the area. The seasonal amplitude varies from typically 10 cm, to more than 40 cm in the North Sea. The storm surge season occurs around December–January in the north of the domain (above 40°N), due to winter extra-tropical cyclones, and around September–October in the south-west, due to tropical cyclones. The dependence of storm surges with the North Atlantic Oscillation extends from the coasts to the deep ocean, and is positive above 50°N and negative below. Observed storm surges show mostly non significant or small trends (<span><math><mrow><mo><</mo><mo>±</mo></mrow></math></span> 1 mm/yr), while the model displays positive trends almost everywhere, possibly due to inhomogeneities in the atmospheric forcing dataset prior to 1950.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102636"},"PeriodicalIF":2.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267924","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}
Accurate wave prediction is essential for coastal and ocean engineering, as sea state conditions directly impact the design and operation of marine infrastructure, renewable energy systems, and maritime safety. While most research focuses on forecasting significant wave height (Hs) using increasingly complex models, other essential variables such as wave period (Tp) and direction (Dir) are often overlooked despite their importance in fully characterizing sea states.
This study addresses this gap by applying Artificial Intelligence (AI) models – Long Short-Term Memory (LSTM) networks and Random Forests (RF) – to predict Hs, Tp, and Dir. A novel window and flatten technique was introduced to restructure temporal data into a format suitable for machine learning, enhancing model performance for Dir and Tp predictions. Both models were tested under various wave conditions in the Mediterranean Sea
Results show that LSTM generally outperforms RF, particularly for Dir. However, RF models, which are not inherently designed for time series tasks, performed surprisingly well for Hs prediction and for short term Tp predictions. This opens promising avenues for developing hybrid models that combine sequential and non-sequential methods, potentially surpassing traditional sequence-to-sequence approaches in accuracy and robustness.
The study also highlights the challenge of accurately modelling Tp and the importance of evaluating model performance under varying energy conditions. Significant sensitivity to testing scenarios was observed, underlining the need for careful dataset selection and model validation. These findings provide a foundation for extending wave forecasting tools to more energetic environments such as the Atlantic Ocean and for advancing hybrid AI-based prediction frameworks.
{"title":"Improving multi-variable wave forecasting with AI: Integrating LSTM and random forest, using a window and flatten technique","authors":"Nerea Portillo Juan, Mónica Ferrer Gómez-Cano, Sara Yagüe Rubio, Vicente Negro Valdecantos","doi":"10.1016/j.ocemod.2025.102638","DOIUrl":"10.1016/j.ocemod.2025.102638","url":null,"abstract":"<div><div>Accurate wave prediction is essential for coastal and ocean engineering, as sea state conditions directly impact the design and operation of marine infrastructure, renewable energy systems, and maritime safety. While most research focuses on forecasting significant wave height (Hs) using increasingly complex models, other essential variables such as wave period (Tp) and direction (Dir) are often overlooked despite their importance in fully characterizing sea states.</div><div>This study addresses this gap by applying Artificial Intelligence (AI) models – Long Short-Term Memory (LSTM) networks and Random Forests (RF) – to predict Hs, Tp, and Dir. A novel window and flatten technique was introduced to restructure temporal data into a format suitable for machine learning, enhancing model performance for Dir and Tp predictions. Both models were tested under various wave conditions in the Mediterranean Sea</div><div>Results show that LSTM generally outperforms RF, particularly for Dir. However, RF models, which are not inherently designed for time series tasks, performed surprisingly well for Hs prediction and for short term Tp predictions. This opens promising avenues for developing hybrid models that combine sequential and non-sequential methods, potentially surpassing traditional sequence-to-sequence approaches in accuracy and robustness.</div><div>The study also highlights the challenge of accurately modelling Tp and the importance of evaluating model performance under varying energy conditions. Significant sensitivity to testing scenarios was observed, underlining the need for careful dataset selection and model validation. These findings provide a foundation for extending wave forecasting tools to more energetic environments such as the Atlantic Ocean and for advancing hybrid AI-based prediction frameworks.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102638"},"PeriodicalIF":2.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267923","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 : 2025-09-26DOI: 10.1016/j.ocemod.2025.102623
Ali Abdolali , Tyler J. Hesser , Aron Roland , Martha Schönau , David A. Honegger , Jane McKee Smith , Héloïse Michaud , Luca Centurioni
Using the six-month hurricane season of 2022 as a case study and the spectral wave model WAVEWATCH III, this effort shows that wave parameters produced via a variable-resolution global mesh (5–30 km) agree with a diverse array of validating observational datasets at a level comparable to that of a constant-resolution mesh (3 km) that is six times more costly to run. The optimized variable-resolution, unstructured triangular mesh is faithful to land geometry and wave transformation gradients while relaxing focus in deeper regions where gradients are typically less pronounced. Wave parameters measured via satellite altimetry, stationary buoy networks, and drifting buoys are employed to demonstrate not only a substantial increase in performance over a coarse, constant-resolution grid (40 km), with RMSE reduced from 0.28 m to 0.14 m and Correlation Coefficient (CC) improved from 0.92 to 0.98 overall, but also a comparable level of performance to that of a mesh that has undergone a full convergence analysis. Performance comparisons isolated to shallow regions and near cyclonic storms highlight the importance of resolving relevant geometries. For nearshore data, RMSE improves from 0.29 m to 0.13 m and CC from 0.89 to 0.98; in shallow regions, RMSE from 0.29 m to 0.15 m and CC from 0.88 to 0.97; and under cyclonic conditions, RMSE from 0.62 m to 0.35 m and CC from 0.93 to 0.98. Wave model results using the variable-resolution mesh were further analyzed to provide a detailed summary of the wave climate, including wind-wave and swell partitions, over the six-month study period in the study area.
{"title":"Advancing multi-scale wave modeling: Global and coastal applications during the 2022 Atlantic hurricane season","authors":"Ali Abdolali , Tyler J. Hesser , Aron Roland , Martha Schönau , David A. Honegger , Jane McKee Smith , Héloïse Michaud , Luca Centurioni","doi":"10.1016/j.ocemod.2025.102623","DOIUrl":"10.1016/j.ocemod.2025.102623","url":null,"abstract":"<div><div>Using the six-month hurricane season of 2022 as a case study and the spectral wave model WAVEWATCH III, this effort shows that wave parameters produced via a variable-resolution global mesh (5–30 km) agree with a diverse array of validating observational datasets at a level comparable to that of a constant-resolution mesh (3 km) that is six times more costly to run. The optimized variable-resolution, unstructured triangular mesh is faithful to land geometry and wave transformation gradients while relaxing focus in deeper regions where gradients are typically less pronounced. Wave parameters measured via satellite altimetry, stationary buoy networks, and drifting buoys are employed to demonstrate not only a substantial increase in performance over a coarse, constant-resolution grid (40 km), with RMSE reduced from 0.28 m to 0.14 m and Correlation Coefficient (CC) improved from 0.92 to 0.98 overall, but also a comparable level of performance to that of a mesh that has undergone a full convergence analysis. Performance comparisons isolated to shallow regions and near cyclonic storms highlight the importance of resolving relevant geometries. For nearshore data, RMSE improves from 0.29 m to 0.13 m and CC from 0.89 to 0.98; in shallow regions, RMSE from 0.29 m to 0.15 m and CC from 0.88 to 0.97; and under cyclonic conditions, RMSE from 0.62 m to 0.35 m and CC from 0.93 to 0.98. Wave model results using the variable-resolution mesh were further analyzed to provide a detailed summary of the wave climate, including wind-wave and swell partitions, over the six-month study period in the study area.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102623"},"PeriodicalIF":2.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221531","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 : 2025-09-23DOI: 10.1016/j.ocemod.2025.102635
Martin Henke , Zhaoqing Yang
Cook Inlet, Alaska is a unique tidal estuary with extreme tidal regimes and the presence of seasonal ice coverage. In this study, the wave dynamics of Cook Inlet are explored through analysis of in-situ wave observations and spectral wave model simulations. The analysis first assesses the wave climate from an existing dataset — showing low-energy wave conditions as a mean state for the upper and lower inlets. Following, wave observations within the inlet are analyzed to reveal modulation by tidal constituents. Finally, a region-specific, ocean circulation coupled, spectral wave model is run over a storm event with current and ice forcings present. This simulation reveals that under extreme wind conditions, large waves can exceed 2 m and 6 m in the upper and lower inlet sections. Simulations results demonstrate that increases in significant wave height up to 1 m are observed due to the effects of wave–current interaction on opposing current gradients. This analysis provides insight into how the tidal phase can amplify or diminish wave energy over large extents of the inlet and the role sea ice plays in limiting regional wave energy. These outcomes demonstrate the combined influence of environmental variables current, water levels, and ice influencing wave dynamics and stress the importance of their implementation in wave modeling frameworks where applicable.
{"title":"The influence of tidal currents and sea ice on wave dynamics in Cook Inlet, Alaska","authors":"Martin Henke , Zhaoqing Yang","doi":"10.1016/j.ocemod.2025.102635","DOIUrl":"10.1016/j.ocemod.2025.102635","url":null,"abstract":"<div><div>Cook Inlet, Alaska is a unique tidal estuary with extreme tidal regimes and the presence of seasonal ice coverage. In this study, the wave dynamics of Cook Inlet are explored through analysis of in-situ wave observations and spectral wave model simulations. The analysis first assesses the wave climate from an existing dataset — showing low-energy wave conditions as a mean state for the upper and lower inlets. Following, wave observations within the inlet are analyzed to reveal modulation by tidal constituents. Finally, a region-specific, ocean circulation coupled, spectral wave model is run over a storm event with current and ice forcings present. This simulation reveals that under extreme wind conditions, large waves can exceed 2 m and 6 m in the upper and lower inlet sections. Simulations results demonstrate that increases in significant wave height up to 1 m are observed due to the effects of wave–current interaction on opposing current gradients. This analysis provides insight into how the tidal phase can amplify or diminish wave energy over large extents of the inlet and the role sea ice plays in limiting regional wave energy. These outcomes demonstrate the combined influence of environmental variables current, water levels, and ice influencing wave dynamics and stress the importance of their implementation in wave modeling frameworks where applicable.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"199 ","pages":"Article 102635"},"PeriodicalIF":2.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221518","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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