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        <title>Frontiers in Marine Science | Physical Oceanography section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/marine-science/sections/physical-oceanography</link>
        <description>RSS Feed for Physical Oceanography section in the Frontiers in Marine Science journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-14T05:40:43.765+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1813774</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1813774</link>
        <title><![CDATA[Asymmetric decadal changes in the relationship between warm water volume and ENSO around 2000]]></title>
        <pubdate>2026-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhongtian Wu</author><author>Li Yan</author><author>Jianjun Xu</author><author>Shaojun Zheng</author>
        <description><![CDATA[The warm water volume (WWV), representing oceanic heat content, is the most widely used oceanic predictor of El Niño–Southern Oscillation (ENSO). However, the decadal variation in the relationship between WWV and ENSO remain unclear. Here we investigate the decadal changes in WWV’s predictive skill on ENSO from two aspects: the positive-negative ENSO event asymmetry and the Western-Eastern Pacific asymmetry. In this study, it’s found that an asymmetric decadal shift occurs in the predictive ability of WWV on El Niño and La Niña. Before 2000, WWV can predict ENSO beyond one-year lead, with the predictability reaching maximum at 8 months lead. This lead relation between WWV and ENSO decreased after 2000, mainly for the dramatically weakened La Niña predictability with the lead time of maximum predictability changing from 8 months to 3 months, while the El Niño predictability remained robust beyond 8 months lead. Such decreased predictive ability of WWV on La Niña is linked to the increased multi-year La Niña after 2000. We also identified the distinct roles and changes of WWV in the Western and Eastern Pacific Oceans. Revealed by information flow, after 2000 the stabilizing influences of Western Pacific WWV on ENSO increased and the key region expanded southward (from 4°S–3°N to 10°S–3°N), while the destabilizing influences of Eastern Pacific WWV on ENSO weakened and its scope narrowed (from 4°S–3°N to 2°S–2°N). Specifically in the Western Pacific after 2000, not only is the equatorial WWV important, but the off-equator (6°S–10°S) WWV in southern hemisphere is also significant. These results further extend our understanding on the prediction of ENSO by WWV.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1823735</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1823735</link>
        <title><![CDATA[Optimized design of storm surge barrier and seawall defenses for Macao under extreme typhoons and sea-level rise]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fuhai Dao</author><author>Xiaohui Wang</author><author>Shaobo Qiao</author><author>Shasha Lu</author><author>Xian Zhu</author><author>Wenjie Dong</author>
        <description><![CDATA[For low-lying coastal metropolises all over the world, coastal infrastructure is particularly vulnerable to typhoon-induced storm surges, which can be significantly intensified by accelerating sea-level rise. The efficacy of hard engineering defenses in such dynamically changing conditions needs thorough evaluation. This study employs a high-resolution coupled model to evaluate and optimize hard engineering defenses and propose an economic yet effective design criterion, taking the Macao Peninsula as a key example. The modeling framework rests on a novel blended Holland-ERA5 wind field and an unstructured computational mesh refined to 10 m resolution along the Macao coastline. Concretely, the analysis demonstrates that the government’s existing plan for an inner harbor seawall is insufficient, whereas a storm surge barrier at the harbor entrance is necessary when considering future sea-level rise. Meanwhile, the minimum crest height of the barrier can be lower than the height of a peak storm tide. For instance, a barrier crest height of just 2.40 m above mean sea level can effectively prevent inundation during Typhoon Hato, which had a peak storm tide of 3.78 m. The difference stems from the short duration of the surge peak and the limited overflow volume, revealing a design criterion that the barrier height can be decoupled from absolute peak water levels by exploiting the buffering capacity of the sheltered basin. This study also identifies key vulnerabilities along the urban shoreline where no overflow can be tolerated, and prescribes staged crest elevations for both the supplementary seawalls and the barrier itself under present-day and future sea-level conditions. Without these integrated defenses, the extent of Hato-intensity inundation would expand by 13% by mid-century and 84% by the end of the century under a high-emission trajectory. By establishing a transferable methodology that links dynamic surge processes to cost-efficient defense elevations, this work provides both an applicable design criterion and a high-resolution modeling framework that can be adapted to other tidally choked urban estuaries worldwide.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1733489</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1733489</link>
        <title><![CDATA[Western boundary current - driven shelf sea deoxygenation on the Agulhas bank]]></title>
        <pubdate>2026-05-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Juliane U Wihsgott</author><author>Matthew R Palmer</author><author>Alex J Poulton</author><author>Margaux Noyon</author><author>Michael Roberts</author><author>Ekaterina Popova</author>
        <description><![CDATA[Dissolved oxygen is a fundamental component of healthy marine ecosystems and the livelihoods and economies they support. Despite its importance, dissolved oxygen is declining globally, and the processes driving deoxygenation in western boundary current systems remain poorly constrained. Here we present new autonomous ocean glider data from the eastern Agulhas Bank, a temperate shelf system off South Africa strongly influenced by the Agulhas Current. We identify a two-stage self-enhancing deoxygenation mechanism: shelf-edge exchange injects cold, nutrient-rich but oxygen-deficient South Indian Central Water onto the shelf, establishing and maintaining strong water column stratification; while wind-driven coastal upwelling fuels intense primary production and organic matter sinking that further enhances oxygen decline within the shelf sea interior. The first turbulence measurements in this region show that vertical mixing is too weak to sufficiently ventilate subsurface and near bed layers on the shelf, allowing low-oxygen conditions to persist. These results demonstrate that western boundary currents can precondition shelf seas for episodic oxygen depletion, with important implications for ecosystem resilience under projected climate-driven intensification of boundary current dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1866365</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1866365</link>
        <title><![CDATA[Correction: Eddy-induced chlorophyll variability in the Norwegian Sea revealed by Bio-Argo observations]]></title>
        <pubdate>2026-05-05T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Nikita Sandalyuk</author><author>Eduard Khachatrian</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1775616</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1775616</link>
        <title><![CDATA[Structure and dynamics of a mesoscale eddy in the Kara Sea marginal ice zone during summer 2024]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dmitrii S. Krasilnikov</author><author>Vladimir A. Dobrodii</author><author>Vladimir V. Zhmur</author><author>Igor E. Kozlov</author><author>Olga A. Sashova</author><author>Sergey A. Mosharov</author><author>Alexey V. Zimin</author><author>Yury V. Fomin</author><author>Evgeniy V. Plotnikov</author>
        <description><![CDATA[The Marginal Ice Zone (MIZ) is a dynamic region where the atmosphere, ocean, and sea ice actively interact, giving rise to frequent eddy formation. Clarifying the processes governing this zone is crucial for both accurate modeling of local circulation and improved prediction of Arctic climate change, particularly sea-ice retreat and ecosystem shifts. This study investigates a mesoscale eddy in the poorly studied northeastern Kara Sea MIZ using a joint analysis of in situ and satellite measurements from summer 2024. We also apply principles from ellipsoidal vortex theory. The eddy's complex evolution is described, and its key parameters are quantified. The eddy was found to contain and transport a substantial volume of freshened cold water, potentially modifying the structure of surrounding waters. Two potential mechanisms for the eddy's formation were proposed, each requiring further investigation through dedicated modeling and observational efforts. Significant differences in the phytoplankton biomass and production rates were identified across the eddy, whereas species composition showed no significant variation. These results highlight the role of mesoscale eddies in freshwater redistribution and biophysical coupling in the MIZ, while underscoring the need to advance theories of eddy dynamics and incorporate these processes into regional and climate models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1779587</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1779587</link>
        <title><![CDATA[Impact of typhoon translation speed on swell-dominated wave energy redistribution along Zhejiang Coast]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weimin Liao</author><author>Yuchuan Fang</author><author>Shiyao Ying</author><author>Shunxiang Zhang</author><author>Ruinan Guo</author><author>Wei Chen</author><author>Zengliang Miao</author>
        <description><![CDATA[This study investigates the impact of typhoon translation speed on wave spectral evolution and swell-dominated wave energy redistribution in the coastal waters of Zhejiang Province, China. Three historical typhoons with low, medium, and high translation speeds were selected as representative cases. The results show that translation speed affects the phase alignment between wave components; slow-translation typhoons advance swell spectral peaks towards wind/mixed waves. Reduced speed prolongs wave forcing but shrinks significant wave height (SWH) spatial coverage, while higher speeds amplify peak SWH and expand impact zones. During the active process, notable right-left asymmetry emerges, with SWH in the typhoon’s right-front quadrant reaching 2–3 times that in the left quadrant. This asymmetry drives southeastward-propagating swells, concentrating the major part of nearshore wave energy along Zhoushan’s southeastern coast. This study reveals the wave dynamic mechanisms and provides a theoretical reference for typhoon wave disaster forecasting in complex archipelago regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1677822</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1677822</link>
        <title><![CDATA[Eddy-induced chlorophyll variability in the Norwegian Sea revealed by Bio-Argo observations]]></title>
        <pubdate>2026-04-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nikita Sandalyuk</author><author>Eduard Khachatrian</author>
        <description><![CDATA[Mesoscale eddies play a vital role in shaping marine biogeochemical processes, particularly influencing chlorophyll (Chl) distribution in oceanic systems. Understanding how eddies affect Chl distribution is critical for assessing regional productivity. The presented study focuses on the Norwegian Sea region, which is characterized by the high intensity of mesoscale eddy activity and complex dynamical processes. To study the eddy impact on the Chl distribution in the region of interest, we employed a colocalization method that combines altimetry data with Bio-Argo profiles, allowing us to derive composite Chl structures for both cyclonic (CEs) and anticyclonic (AEs) eddies. Our study provides insights into the subsurface 2D and 3D patterns of eddy-induced Chl distribution, showing that both CEs and AEs can enhance Chl concentration in the Norwegian Sea, with AEs driving greater Chl elevation during the summer months. Our analysis reveals that both eddy types are associated with positive subsurface Chl anomalies, reaching up to 0.5–0.7 mg/m³, with the strongest signals confined to the upper 50 m. While CEs exhibit peak anomalies near 25 m depth, AEs show a more complex dipole-like structure with maxima located both in the core and at the periphery within a depth layer of ∼20–50 m. The obtained results also demonstrated remarkably high concentration of Chl within the Lofoten Vortex. Given the prevalence of mesoscale eddies in the region, these findings suggest their crucial role in the biogeochemical dynamics of the Norwegian Sea.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1705858</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1705858</link>
        <title><![CDATA[Evidence of increased hydrodynamic retention in the spawning grounds of large pelagic fishes in the western Mediterranean]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrea Casaucao</author><author>Baptiste Mourre</author><author>Maria Pilar Tugores</author><author>Rosa Balbín</author><author>Lara Díaz-Barroso</author><author>Ismael Hernández-Carrasco</author><author>Patricia Reglero</author><author>Diego Alvarez-Berastegui</author>
        <description><![CDATA[Hydrography shapes the reproductive and early life ecology of migratory large pelagics such as Atlantic bluefin tuna. While their spawning grounds have traditionally been linked to areas with suitable temperature, low productivity, and moderate surface mixing, other oceanographic processes are likely crucial to ensure that early life stages remain in favorable habitats. We hypothesize that retentive oceanographic patterns are a defining feature of these spawning areas, distinguishing them from surrounding regions. To test this hypothesis, we first evaluated the skill of a high-resolution, data-assimilative hydrodynamic model to represent the ocean surface circulation around the Balearic Islands, where the main spawning ground of the Western Mediterranean is located. We then used Lagrangian particle tracking to investigate retention and dispersion patterns at the regional scale during the reproductive season of Atlantic bluefin tuna, albacore tuna, and swordfish. Retention and dispersion analyses revealed that, during the reproductive season, surface circulation favors particle transport towards the spawning ground, where particles tend to remain. This shows that the Western Mediterranean spawning ground is governed by basin-scale hydrodynamic regimes that aggregate particles from neighboring regions, providing a mechanistic basis for their persistence over time despite occasional anomalous years.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1808982</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1808982</link>
        <title><![CDATA[Analysis of wave system generation zones in the southeast Pacific using spatial tracking methods]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fujie Zhang</author><author>Huawei Dong</author>
        <description><![CDATA[Ocean waves are generally a superposition of wave systems from different meteorological events (e.g., extratropical cyclones). While spectral partitioning can identify individual wave systems at specific grid points, it fails to preserve their spatial coherence, resulting in disorganized partitioned wave fields that hinder physical interpretation. This study evaluates two spatial tracking algorithms, namely WAVEWATCH III (WW3) method and JIANG method, to reconstruct spatially coherent wave fields and investigate wave generation zones in the southeast Pacific. The tracking results are validated against simulations driven by artificially segmented wind fields. Results indicate that wave systems affecting the southeast Pacific primarily originate from meteorological events within the southeast trade winds, the southern storm belt, and the northern storm belt. The WW3 method is prone to tracking discontinuities, whereas the JIANG method improves continuity by merging events with matching boundaries but often over-merges events that share similar generation patterns. Both methods exhibit limitations when dealing with wave systems that have similar parameters but distinct origins, as their tracking logic primarily relies on the spatial gradient of wave parameters. Despite these limitations, spatial tracking methods effectively reconstruct physically interpretable wave fields and provide practical support for wave source identification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1698557</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1698557</link>
        <title><![CDATA[Upper ocean mixing, surface heat fluxes, and heat content variability in the upper 150 m during Hurricane Laura (2020)]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Senam K. Tsei</author><author>Stephan D. Howden</author><author>Arne R. Diercks</author><author>Karthik Balaguru</author><author>Matthieu Le Hénaff</author><author>Jun A. Zhang</author><author>Iam-Fei Pun</author><author>Kevin M. Martin</author>
        <description><![CDATA[This study integrates thermistor observations, hurricane glider measurements, and model-derived temperatures to examine the oceanic processes influencing Hurricane Laura’s rapid intensification, the role of the preexisting warm mixed layer at Stone Mooring (StM) in modulating cooling of the mixed layer, and the processes governing mixed layer heat evolution during and after storm. Prior to the storm’s passage, StM featured a 31°C warm mixed layer and elevated heat content (60–80 kJ/cm2), which strongly preconditioned the upper ocean. Thermistor data showed that Hurricane Laura produced a mixed layer cooling of 1.2°C on 26 August, compared with a model-estimated cooling of −1.04°C. A 1D shear-driven mixed layer model experiment indicates that, without the warm preexisting mixed layer, temperatures could have cooled down to 28.53°C, supporting the hypothesis that thermal structures associated with Loop Current warm core eddies rarely experience substantial cooling during hurricane passage. We also show that relatively small surface heat fluxes (5.04 kJ/cm2) sustained Hurricane Laura during intensification at StM. Mixed layer heat budget analysis shows that entrainment and surface heat fluxes were the primary drivers of the observed temperature tendency. These results improve understanding of upper ocean processes and demonstrate that observations from StM provide valuable constraints for operational hurricane models in the Gulf of Mexico.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1798048</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1798048</link>
        <title><![CDATA[A deep learning approach for near-coastal sea surface temperature prediction]]></title>
        <pubdate>2026-03-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xianbiao Kang</author><author>Lianzhi Wang</author><author>Haijun Song</author><author>Guansuo Wang</author>
        <description><![CDATA[Accurate near-coastal sea surface temperature (SST) prediction remains challenging due to the limitations of numerical ocean models in resolving fine-scale coastal dynamics. This study proposes a novel deep learning framework specifically designed for station-level SST forecasting in nearshore regions. The framework employs a seasonal stratified sampling strategy to capture thermodynamic patterns across the annual cycle while preventing temporal distribution shift. Building upon the Segment Recurrent Neural Network (SegRNN) architecture, we identify a fundamental information compression bottleneck that causes forecast smoothing. To address this limitation, an Attention-Enhanced Parallel Multi-step Forecast (Attn-PMF) strategy is developed, enabling the model to directly retrieve high-variance features from historical sequences through global attention mechanisms. Validated using four years (2021–2024) of hourly observations from 31 coastal stations in the East China Sea, the proposed framework demonstrates superior performance compared to the operational FIO-COM numerical model, particularly for lead times beyond 48 hours. Results show that the Attn-PMF strategy effectively preserves high-frequency variability and mitigates forecast degradation, providing reliable predictions for coastal management and marine safety applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1775896</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1775896</link>
        <title><![CDATA[Physics-enhanced deep learning for sea surface temperature forecasting via multi-scale feature integration]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hu Li</author><author>Qingao Liu</author><author>Tianping Wang</author><author>Haoyu Wang</author><author>Shuo Yang</author>
        <description><![CDATA[Accurate prediction of sea surface temperature (SST) is essential for marine environmental monitoring and climate forecasting. However, most existing deep-learning-based approaches rely heavily on data-driven methodologies and lack sufficient integration of physical mechanisms, thereby limiting their physical consistency and interpretability. To overcome this limitation, this study introduces a multi-source coupled prediction neural network (MSCPNN), which incorporates temperature, salinity, and current dynamics into a multi-scale feature learning framework. Built upon the multi-feature physical neural network (MFPNN), the proposed model integrates a convolutional block attention module (CBAM), where channel attention adaptively models the multi-factor coupling among temperature, salinity, and currents, and spatial attention captures multi-scale spatial patterns in SST. Using high-resolution reanalysis data from the South China Sea spanning 2011 to 2020, comprehensive experiments were conducted comparing MSCPNN with MFPNN and PCL-MFPNN. The results demonstrate that MSCPNN significantly outperforms the baseline models across multiple evaluation metrics—including RMSE, correlation coefficient, PSNR, and SSIM—achieving an average reduction in RMSE of 17% and an increase in correlation coefficient of 0.035, which reflects higher predictive accuracy and improved physical consistency. Ablation studies further validate the superiority of multi-factor coupling over single-factor alternatives and clarify the distinct contributions of salinity and currents to SST prediction. Overall, MSCPNN advances the accuracy and stability of long-term SST forecasting while providing a more interpretable framework for intelligent ocean prediction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1758561</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1758561</link>
        <title><![CDATA[Defining an appropriate range of scales for application of the gradient Richardson number, with implications for observations of stratified shear turbulence at laboratory and ocean scales]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Daniel G. MacDonald</author><author>Louis Goodman</author>
        <description><![CDATA[The Richardson number (Ri) represents the square of the ratio of buoyancy frequency to vertical shear, and a value less than ¼ has long been recognized as a necessary condition for the generation of turbulence, particularly at small scales. At larger scales, it is common to evaluate a bulk Richardson number, (RiB), with arbitrary values of criticality, generally greater than ¼. Despite the ubiquity of this concept in modern oceanography, the range of scales over which the critical value of ¼ is valid has not been well documented. Here, spectral and energetics arguments are used to identify the primitive shear length scale, lS=(νS¯)12, where ν is kinematic viscosity and S¯ is velocity shear, as the fundamental scale for appropriate scales for a critical value of Ri = ¼. These findings are evaluated against a variety of data, suggesting that the range 10 ls – 100 ls is an approximate range of validity for the critical value of ¼. This range is equivalent to 100<Re < 10,000, where Re is a Reynolds number based on thickness and velocity across the layer. Further data analysis suggests that turbulence persists at values of RiB far above ¼ in very thick layers (Re > 105), and that turbulence intensity is enhanced for thin layers (Re< 105). We hypothesize the former is due to the existence of smaller scale regions within the layer where Ri locally falls below ¼, while the latter is the result of stratification increasing the ratio of turbulent kinetic energy to fluid mass. Historically, the value of Re has been considered of minimal relevance to stratified turbulence in the ocean. However, here we demonstrate the significance of Re, and suggest a broader view of turbulence within Re-Ri parameter space. The proposed parameter space may potentially yield new insights into turbulence closures, particularly at lower Re values, provide a more rigorous approach to defining critical values of RiB, greatly facilitating interpretation of observational data, and provide a more rigorous framework for the use of direct numerical simulation (DNS) results and laboratory experiments of turbulence to inform geophysical scale dynamics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1757436</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1757436</link>
        <title><![CDATA[Oceanward surface transport from the NW African upwelling zone by coastal jet detachment and filaments]]></title>
        <pubdate>2026-03-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Luuk Rader</author><author>Borja Aguiar-González</author><author>Timothy David Price</author><author>Daura Vega-Moreno</author><author>Eugenio Fraile-Nuez</author><author>Francisco Machín</author>
        <description><![CDATA[The oceanward surface transport of particles, including marine litter, from the northwestern African upwelling zone is influenced by multiple interacting physical processes. This study applies the OceanParcels Lagrangian framework to investigate the mechanisms that may contribute to oceanward surface transport in this region, motivated by the hypothesis that the northwestern African upwelling system could represent a potential source of marine litter in the vicinity of the Canary Islands. The simulations suggest that the coastal jet stream and its detachment, upwelling filaments, and Stokes drift play key roles in shaping particle trajectories. In particular, coastal jet detachment appears to organize surface transport into narrow, oceanward-oriented particle corridors, while upwelling filaments may provide additional offshore export pathways. Stokes drift introduces a predominantly southward deflection that can reduce or modulate oceanward advection and enhance alongshore transport. These results provide a process-based, model-derived first assessment of previously understudied oceanward transport corridors in the NW African upwelling system. They are consistent with the hypothesis that this region may contribute to surface tracer transport toward the Canary Islands. However, caution is required when extrapolating these findings to marine debris, as windage is not included and may significantly alter transport pathways. Continued investigation, including observational validation and improved surface forcing representations, will help further constrain the mechanisms shaping particle transport in the NW African upwelling system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1732870</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1732870</link>
        <title><![CDATA[WaveUformer: a bias correction model for GWSM4C Wave Forecasting]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Can Fang</author><author>Longyu Jiang</author><author>Zeyu Wang</author><author>Quan Jin</author><author>Xingjie Jiang</author><author>Feng Hua</author>
        <description><![CDATA[Artificial intelligence (AI) models are being progressively applied to the field of wave forecasting. However, in operational forecast scenarios, these data-driven models exhibit error characteristics different from those of numerical models due to factors such as uncertainties in the driving wind fields. Traditional correction methods have limited capability to correct these data-driven biases, particularly for medium- to long-range forecasts and extreme sea states. To address this issue, this study proposes a deep learning-based post-processing correction model, WaveUformer, specifically designed to correct the forecast results of the AI wave model Global Wave Surrogate Model for Climate simulation (GWSM4C). The model synergistically processes driving wind field data and forecast wave field data, and integrates an adaptive correction mechanism based on forecast lead time with an efficient spatiotemporal attention network to effectively capture the dynamic evolution patterns of errors. Evaluation based on the full-year test data of 2023 shows that WaveUformer reduces the annual mean root mean square error of 24-240-hour significant wave height forecasts from 0.57 m to 0.39 m, achieving an overall relative improvement of 31%. In the case analysis of Typhoon, the model successfully corrected the underestimation bias of extreme conditions and accurately reproduced the spatial structure of high-wave areas. The results demonstrate that WaveUformer can reduce the forecast errors of AI models, improving their forecast accuracy and reliability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1783157</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1783157</link>
        <title><![CDATA[A significant wave height prediction method combining VMD decomposition and the GVSAO-CNN-BiGRU-SA model]]></title>
        <pubdate>2026-03-16T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Zongquan Ying</author><author>Wengeng Shen</author><author>Xuegang Wang</author><author>Yiming Zhao</author><author>Meihong Lin</author>
        <description><![CDATA[To improve the accuracy and robustness of significant wave height prediction under complex marine conditions, a multi-strategy Snow Ablation Optimization (GVSAO) model based on the Good Point Set Initialization Strategy (G), Cyclic Oscillation Mutation Strategy (V), and Snow Ablation Optimizer (SAO) is proposed to enhance parameter optimization. The GVSAO model combines Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and a Self-Attention Mechanism (SA) to construct the GVSAO-CNN-BiGRU-SA framework, which fully exploits the nonlinear characteristics of wave height time series. The study utilizes observed data from two observation points along the U.S. East Coast to the Gulf of Mexico (Stations 41013 and 42002) as well as from the Arabian Sea (Station 23020) and the Pacific Ocean (Station 46044). Comparative experiments on input feature combinations reveal that Intrinsic Mode Function (IMF) components derived from Variational Mode Decomposition (VMD) contribute more significantly to prediction accuracy than single physical features by effectively capturing dynamic time-frequency characteristics. The results demonstrate that the GVSAO model outperforms SAO, GSAO, and VSAO in terms of global exploration and stability, as validated by performance comparisons on the CEC2005 benchmark functions. Compared with the BiGRU model, the GVSAO-CNN-BiGRU-SA model exhibited superior performance, with RMSE reduced by 44.01% at Station 41013 and 15.12% at Station 42002. Similarly, it outperformed the CNN-BiGRU and CNN-BiGRU-SA models across all key metrics. The model achieved high-accuracy predictions in diverse marine environments, with relative mean errors within 0.5472%, RMSE within 0.1064 m, and correlation coefficients (R2) exceeding 0.99. Furthermore, in multi-step forecasting (3 to 48 hours), the model maintained high reliability with R2 values remaining above 0.84 across diverse geographic environments. The GVSAO-CNN-BiGRU-SA model provides a reliable solution for wave height prediction, contributing to marine engineering early warnings and energy utilization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1739607</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1739607</link>
        <title><![CDATA[Thoughts on prognostically modeling an eddying double-gyre ensemble mean]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrew C. Poje</author><author>Takaya Uchida</author><author>Quentin Jamet</author><author>Luolin Sun</author><author>Thierry Penduff</author><author>Bruno Deremble</author><author>Joseph Schoonover</author><author>Megan Trapanese</author><author>Nicolas Wienders</author><author>William K. Dewar</author>
        <description><![CDATA[We address the question of separating the ocean’s deterministic response to time-dependent forcing from its intrinsic chaotic variability. Ideally, one could compute the ensemble mean directly without performing numerous realizations, but this requires knowledge or closure of the second-order statistics — the classical turbulent-closure problem, here recast for a non-equilibrium, geophysical setting. Building on the ideas of nonlinear midlatitude ocean adjustment, we examine this problem using idealized quasi-geostrophic (QG) double-gyre ensembles subjected to episodic temporal variations in wind forcing. Our objective here is not to develop a subgrid parameterization of unresolved eddies, but rather to construct and test prognostic equations for the ensemble mean itself, using the simplest possible closure assumptions. We find that the performance of ensemble mean closures is highly dependent on the spatiotemporal structure of the forcing. Under slowly varying forcing, approximate closures reproduce the mean evolution reasonably well; under rapidly varying, near-zero-mean forcing, the simplest ensemble-mean closures fail, even at the level of basin-averaged total energy and enstrophy. In both regimes, the ensemble-mean response is not simply the accumulated imprint of the applied forcing, but instead appears as a continuing, non-equilibrated dialogue between the mean and eddy fields.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1727718</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1727718</link>
        <title><![CDATA[Characteristics and mechanisms of intraseasonal oscillation of mixed layer salinity in the western formation region of North Pacific Tropical Water]]></title>
        <pubdate>2026-02-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kai Li</author><author>Yun Qiu</author><author>Chunsheng Jing</author><author>Shangzhan Cai</author><author>Jindian Xu</author><author>Hangyu Chen</author>
        <description><![CDATA[Based on a time series of seawater salinity profile data collected by moored buoys, in conjunction with Soil Moisture Active Passive (SMAP) sea surface salinity (SSS) data from June 2015 to May 2022, as well as additional observational data, this study investigates the characteristics and mechanisms of intraseasonal oscillation of mixed layer salinity (MLS) in the western formation region of North Pacific Tropical Water (NPTW). The analysis reveals significant intraseasonal oscillations of MLS with two dominant periods of approximately 32 days and 16 days. Furthermore, the intraseasonal variability in MLS exhibits noticeable seasonality, being stronger during winter and spring and weaker during summer and autumn. Our analysis further reveals that these pronounced intraseasonal MLS oscillations are primarily attributed to evaporation minus precipitation (E-P), which is closely associated with Madden-Julian Oscillation (MJO) events. The MJO exerts dominant influence on MLS variations in the western formation region of NPTW. During the MJO active phase, the study area is influenced by cyclonic wind anomalies. The enhanced convection and increased precipitation, lead to a significant decrease in MLS. Similarly, during the MJO suppressed phase, the region is controlled by the anomalous anticyclonic winds, resulting in weakened convection and reduced precipitation, which causes an increase in salinity in the western formation region of NPTW. In addition to the dominant influence of MJO-induced changes in freshwater flux, the oceanic advection processes play a secondary role in the intraseasonal variability of MLS.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1777065</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1777065</link>
        <title><![CDATA[Wave characteristics and wave energy resource assessment of the waters bordering China]]></title>
        <pubdate>2026-02-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yanan Wu</author><author>Qingwei Zhou</author><author>Wanjun Jiang</author><author>Yang Bai</author><author>He Wu</author>
        <description><![CDATA[Wave energy is a clean and renewable marine resource with great development potential. The China Sea, including the Bohai Sea, Yellow Sea, East China Sea and South China Sea, has complex marine conditions. This study aims to clarify the wave characteristics and spatiotemporal distribution of wave energy resources in the China Sea, providing a scientific basis for its rational development and utilization. Using 30-year ERA5 wind field data (1995–2024) as input, the SWAN wave model was adopted to numerically simulate the wave field in the China Sea. The multi-year, seasonal, and monthly average wave and wave ennergy characteristics were analyzed, and  wave energy resources was evaluated with wave energy rose diagrams at characteristic points. The Bohai Sea and Yellow Sea had low annual mean wave heights mostly below 1.2 m, the East China Sea 1.2–1.7 m, and high-latitude South China Sea up to 2 m. Most regions except the Bohai Sea and northern Yellow Sea had annual average energy flux densities exceeding 4 kW/m² , especially near Taiwan Island and South China Sea. Wave energy was richest in winter, followed by autumn, and poorest in spring; The characteristic points the dominant direction was east-northeast, with density mainly distributed in 1–10 s wave periods and 0–5 m significant wave heights. The China Sea has substantial wave energy potential. The spatiotemporal and directional characteristics identified provide important references for the design, planning and utilization of wave energy conversion projects in the region.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmars.2026.1657592</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmars.2026.1657592</link>
        <title><![CDATA[Daily-scale spatiotemporal prediction of thin sea ice thickness during the early freezing season based on EOF-Trans]]></title>
        <pubdate>2026-02-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jikun Liu</author><author>Guoping Zhang</author><author>Shuai Xing</author><author>Ming Gao</author><author>Pengcheng Li</author><author>Dandi Wang</author><author>Bo Zheng</author>
        <description><![CDATA[The accelerated decline of Arctic sea ice is profoundly reshaping regional climate regimes. Sea ice thickness (SIT), particularly under thin-ice conditions, is an important indicator for assessing early-season Arctic sea ice variability, and accurate prediction of its spatiotemporal evolution during the early freezing period is essential for characterizing short-term sea ice changes. In recent years, deep learning has emerged as a complementary approach to traditional sea ice prediction methods. However, existing deep learning-based studies have not fully exploited the large-scale spatial patterns and temporal contextual dependencies inherent in satellite-derived sea ice thickness. To address this limitation, this study proposes a spatiotemporal prediction framework named EOF-Trans for predicting daily-scale variability of thin sea ice thickness during the early freezing season. The method employs Empirical Orthogonal Functions (EOF) to decompose the sea ice thickness field into temporal mode series, utilizes a Transformer architecture to learn the temporal evolution characteristics, and subsequently reconstructs the predicted outputs back into the spatial thickness field by EOF, thereby enabling spatiotemporal sea ice prediction up to a leadtime of 21 days. Experimental results in the Beaufort Sea indicate that the proposed EOF-Trans framework significantly outperforms numerical models and classical deep learning architectures such as U-Net and ConvLSTM. On the 2022–2023 test set, it achieves a correlation coefficient of 88.04%, representing a 2% improvement over U-Net. Even at a leadtime of 21 days, the correlation remains approximately 84%, with the maximum spatial bias not exceeding 0.5 m. These results indicate that EOF-Trans effectively captures spatiotemporal regularities present in thin sea ice thickness, providing a complementary data-driven perspective for short-term sea ice prediction during the early freezing season.]]></description>
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