Ocean Modelling最新文献_第10页

Impacts of a layered snow density evolution scheme on Arctic snow and sea ice simulation in the CICE sea ice model 分层雪密度演变方案对CICE海冰模式下北极雪和海冰模拟的影响

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-10-15 DOI: 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.

雪密度在雪和海冰热力学中起着至关重要的作用。然而，目前的耦合全球气候模式通常依赖于海冰模式分量中雪特性的经验常数，这限制了我们对雪过程如何影响雪和海冰演变的理解。为了解决这个问题，我们在洛斯阿拉莫斯海冰模型（CICE）中实现了分层雪密度参数化，该模型明确考虑了应变压实、风力压实和新雪沉积。与对照运行相比，我们的实验表明，该方案减少了冬季雪深的正偏和北极雪温的冷偏。冬季电导率热损失的减少说明了温度偏差的改善，从而在冬季增加了净表面能增益。85%的额外能量增益完全归因于北极上空积雪热导率的密度依赖性变化。进一步的时空分析表明，积雪深度和积雪密度变化的驱动因素存在明显的季节差异。冬季以风压实和降雪为竞争过程，6、7月以消融为主。它们对泛北极多年平均雪密度变化的贡献分别为+0.161（风压实）、-0.198（降雪）、+0.016（应变压实）、+0.012（相变）和-0.003（雪冰）kg·m-3·hr-1。相应的雪深变化率分别为-0.095、+0.277、-0.020、-0.103和-0.009 cm·day-1。

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引用次数: 0

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 利用数值模式和基于机器学习的海洋垂直混合参数化模拟西北太平洋超级台风Bolaven（2023）的海洋响应

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-10-15 DOI: 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.

利用区域海洋模拟系统（ROMS）和基于机器学习（ML）的海洋垂直混合参数化（OVMP）方案对西北太平洋超级台风Bolaven（2023）的海洋响应进行了模拟和研究。传统的OVMP方案，如MY25和KPP，低估了台风条件下的海洋垂直混合过程。为了解决这一限制，利用高分辨率并行大涡模拟模型（PALM）生成了台风Bolaven下的垂直涡粘度（Km）数据，并用于训练基于xgboost的ML模型。该XGBoost模型用于形成基于ml的OVMP方案，并通过Forpy耦合器集成到ROMS模型中。结果表明，与传统的OVMP模式相比，ROMS-ML耦合模式能显著改善对海表温度（SST）冷却和地下热结构的模拟。基于ml的OVMP方案估计台风Bolaven下更强的海洋垂直混合，增强了上层海洋热再分布，与卫星和原位观测更接近。热力分析表明，上层海洋的温度冷却主要是由强烈的海洋垂直混合、潜热损失和垂直平流驱动的。值得注意的是，台风Bolaven改变了北太平洋副热带模态水（STMW）的结构，使其面积和厚度减小，表明热储减弱，可能对区域气候缓冲产生影响。动量能分析证实，垂直粘度是台风Bolaven期间海洋能量输入的主要来源，促进了局地涡旋的产生和相关的冷却。此外，台风海葵（2023）的附加诊断结果表明，虽然基于ml的OVMP方案比传统方案更准确地捕获局部冷却，但它往往高估了环流复杂和水深陡峭地区的垂直混合。总的来说，本研究强调了物理信息的ML方法在提高极端天气事件下海洋模拟精度方面的潜力，为在气候变化和超级台风更频繁的情况下改善大气-海洋耦合预测系统提供了一条有希望的途径。

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引用次数: 0

The role of longitudinal alignment between surface and bottom forcing on the full-column turbulence mixing in the coastal ocean 海面与海底纵向对强迫在沿海海洋全柱湍流混合中的作用

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-10-10 DOI: 10.1016/j.ocemod.2025.102637

Jiahao Huang , Marcelo Chamecki , Qing Li , Bicheng Chen

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.

浅水海岸环境中的Langmuir湍流可以到达海底，发展成Langmuir超级单体，增强了其大小和混合强度。沿海Langmuir湍流的两个基本问题仍然不清楚：(i)不同情况下湍流的能量循环，（ii）其对垂直混合的影响。我们研究这些问题使用大涡模拟，考虑对齐和相反的风浪和电流方向。结果表明，Langmuir超单体具有强烈的全柱窄带能量模式，在能谱上与Langmuir湍流不同。这种模式发生在风/波和洋流方向一致时，但当它们相反时就消失了。在后一种情况下，在地表和底部边界附近只存在Langmuir湍流和剪切湍流；此外，尽管在模拟中没有分层，但它们的强度被中间层屏障抑制，限制了表面-底部的相互作用。当朗缪尔超级单体存在时，上升流和下升流分支之间的表面-底部动量交换是高度不对称的。正如旋涡-管道连接事件所表明的那样，表面和底部湍流之间的紧密联系只能在上升流区域找到。因此，上升流运动对动量通量的贡献比下升流运动大得多。这些结果表明，尽管近地表风浪相互作用在海洋表面形成了窗型，但能否激活或抑制全柱超级胞体取决于近地表风浪强迫和近底切变强迫之间的不同相互作用。一旦Langmuir超级单体被激活，它们在能量和动量输运方面与Langmuir湍流有显著的不同；因此，它们不能被简单地视为朗缪尔湍流的“全柱”版本。

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引用次数: 0

A secular sea level hindcast (1900–2015) to investigate extreme surges variability and trends in the North Atlantic 研究北大西洋极端浪涌变化和趋势的长期海平面后验（1900-2015）

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-10-09 DOI: 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 (

< \pm

1 mm/yr), while the model displays positive trends almost everywhere, possibly due to inhomogeneities in the atmospheric forcing dataset prior to 1950.

极端海平面的变化，加上沿海人口的增长，是评估沿海洪水风险的关键因素。因此，研究造成极端海平面的主要因素风暴潮的变化和趋势，对海岸保护政策至关重要。我们在北大西洋开发了第一个覆盖整个20世纪（1900-2015）的0.1°网格小时浪涌后cast，并称为ClimEx后cast。我们用34个长期潮汐计验证了预测结果。该模型显示浪涌的总体性能非常好（均方根误差平均为9.3厘米），对于极端浪涌的性能也很好，尽管总体上被低估了。为了研究风暴潮的变异性和趋势，我们对模拟和观测的风暴潮进行了非平稳极值分析。风暴潮的季节性在很大程度上取决于该地区。季节性振幅从典型的10厘米到北海的40厘米以上不等。风暴潮季节发生在12月至1月左右的北纬40°以上地区，主要受冬季热带外气旋的影响，而西南地区则在9月至10月左右，主要受热带气旋的影响。风暴潮与北大西洋涛动的相关性从海岸向深海延伸，在50°N以上为正，在50°N以下为负。观测到的风暴潮大多显示不显著或很小的趋势（±1毫米/年），而模式几乎在所有地方显示正趋势，可能是由于1950年以前大气强迫数据集的不均匀性。

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引用次数: 0

Improving multi-variable wave forecasting with AI: Integrating LSTM and random forest, using a window and flatten technique 用人工智能改进多变量波浪预报：结合LSTM和随机森林，利用窗口和平坦化技术

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-10-06 DOI: 10.1016/j.ocemod.2025.102638

Nerea Portillo Juan, Mónica Ferrer Gómez-Cano, Sara Yagüe Rubio, Vicente Negro Valdecantos

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.

准确的海浪预测对于沿海和海洋工程至关重要，因为海况条件直接影响海洋基础设施、可再生能源系统和海上安全的设计和运行。虽然大多数研究都集中在使用日益复杂的模型预测有效波高（Hs），但其他基本变量，如波浪周期（Tp）和方向（Dir），尽管它们在充分表征海况方面很重要，但往往被忽视。本研究通过应用人工智能（AI）模型-长短期记忆（LSTM）网络和随机森林(RF) -来预测Hs， Tp和Dir，从而解决了这一差距。引入了一种新的窗口和平坦技术，将时间数据重构为适合机器学习的格式，提高了Dir和Tp预测的模型性能。两种模型都在地中海的各种波浪条件下进行了测试。结果表明，LSTM总体上优于RF，特别是对于Dir。然而，RF模型本身并不是为时间序列任务而设计的，它在Hs预测和短期Tp预测中表现得出奇地好。这为开发结合序列和非序列方法的混合模型开辟了有希望的途径，有可能在准确性和鲁棒性方面超越传统的序列对序列方法。该研究还强调了准确建模Tp的挑战以及在不同能量条件下评估模型性能的重要性。观察到对测试场景的显着敏感性，强调需要仔细选择数据集和模型验证。这些发现为将海浪预报工具扩展到大西洋等能量更大的环境以及推进基于人工智能的混合预测框架提供了基础。

{"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}

引用次数: 0

Advancing multi-scale wave modeling: Global and coastal applications during the 2022 Atlantic hurricane season 推进多尺度波浪模拟：2022年大西洋飓风季节的全球和沿海应用

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-09-26 DOI: 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.

以2022年6个月的飓风季节为例研究和波谱波模型WAVEWATCH III，这项工作表明，通过变分辨率全球网格（5-30公里）产生的波浪参数与各种验证观测数据集一致，其水平与恒分辨率网格（3公里）相当，后者的运行成本高出6倍。优化后的可变分辨率、非结构化三角形网格忠实于陆地几何形状和波浪变换梯度，同时在梯度通常不太明显的较深区域放松焦点。通过卫星测高、固定浮标网络和漂流浮标测量的波浪参数不仅证明了在粗糙、恒定分辨率网格（40 km）上的性能大幅提高，RMSE从0.28 m降至0.14 m，相关系数（CC）从0.92提高到0.98，而且性能水平与经过完全收敛分析的网格相当。与浅层区域和气旋风暴附近的性能比较突出了解决相关几何形状的重要性。近岸数据RMSE从0.29 m提高到0.13 m， CC从0.89提高到0.98；浅层RMSE为0.29 ~ 0.15 m， CC为0.88 ~ 0.97；气旋条件下RMSE为0.62 ~ 0.35 m， CC为0.93 ~ 0.98。进一步分析了使用变分辨率网格的波浪模型结果，以提供研究区六个月研究期间波浪气候的详细总结，包括风浪和涌浪分区。

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引用次数: 0

The influence of tidal currents and sea ice on wave dynamics in Cook Inlet, Alaska 阿拉斯加库克湾潮汐流和海冰对波浪动力学的影响

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-09-23 DOI: 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.

阿拉斯加的库克湾是一个独特的潮汐口，潮汐状态极端，季节性冰层覆盖。本研究通过现场波浪观测分析和谱波模型模拟，探讨Cook Inlet的波浪动力学。该分析首先评估了现有数据集的波浪气候，将低能波条件显示为上下入口的平均状态。接下来，分析了入口内的波浪观测，以揭示潮汐成分的调制作用。最后，对存在海流和冰强迫的风暴事件运行一个特定区域的、海洋环流耦合的谱波模式。模拟结果表明，在极端风条件下，进风口上下段的大浪可以超过2 m和6 m。模拟结果表明，由于波流相互作用对反向电流梯度的影响，有效波高增加了1 m。这一分析提供了对潮汐相位如何在入口的大范围内放大或减少波浪能量以及海冰在限制区域波浪能量方面所起作用的深入了解。这些结果表明了环境变量电流、水位和冰对波浪动力学的综合影响，并强调了在适用的波浪建模框架中实施它们的重要性。

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引用次数: 0

A structure-preserving nonstaggered central scheme for shallow water equations with wet–dry fronts and Coriolis force on triangles 具有干湿锋面和三角形科里奥利力的浅水方程的保结构非交错中心格式

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-09-12 DOI: 10.1016/j.ocemod.2025.102626

Jian Dong , Xu Qian , Huizan Wang

This work introduces a structure-preserving nonstaggered central scheme for the two-dimensional shallow water equations with wet–dry fronts and Coriolis force on triangular meshes. A key innovation of our approach is the development of a novel discretization method for source terms that exploits the geometric properties of the mesh within staggered cells. This method effectively overcomes the limitations of existing central schemes, which often exhibit a lack of well-balanced property in configurations that involve wet–dry fronts. In particular, the defined numerical fluxes not only utilize information from the central points but also from the vertex points. We rigorously show that the proposed numerical scheme maintains both positivity-preserving and well-balanced properties, essential attributes that ensure the physical validity and stability of the simulations. To verify our theoretical results, we conduct comprehensive numerical experiments that encompass a variety of scenarios. The results highlight the method’s exceptional performance in accurately modeling complex fluid dynamics associated with wet–dry fronts and Coriolis force.

本文介绍了一种保留结构的非交错中心方案，用于三角网格上具有干湿锋面和科里奥利力的二维浅水方程。我们方法的一个关键创新是开发了一种新的源项离散化方法，该方法利用了交错单元内网格的几何特性。这种方法有效地克服了现有中央方案的局限性，这些方案在涉及干湿锋面的配置中往往表现出缺乏良好的平衡特性。特别是，所定义的数值通量不仅利用了中心点的信息，而且利用了顶点点的信息。我们严格地证明了所提出的数值格式既保持正性又保持平衡性，这是确保模拟的物理有效性和稳定性的基本属性。为了验证我们的理论结果，我们进行了包含各种场景的综合数值实验。结果表明，该方法在精确模拟与干湿锋面和科里奥利力相关的复杂流体动力学方面具有优异的性能。

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引用次数: 0

Impact of islands on the cross-shelf transport among the Pearl River Estuary, its adjacent coast and inner shelf 岛屿对珠江口及其邻近海岸和内陆架间跨陆架运输的影响

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-09-12 DOI: 10.1016/j.ocemod.2025.102634

Pengpeng Hu , Guang Zhang , Suan Hu , Xiuquan Zhu , Heng Zhang , Wenping Gong

Cross-shelf transport is crucial for material exchange in the estuary-coast-shelf continuum. This study employs the Coupled Ocean-Atmospheric-Wave-Sediment Transport (COAWST) modeling system to quantify the cross-shelf transport and investigate the influence of islands on the cross-shelf transport between the Pearl River Estuary (PRE), the adjacent coast and the inner shelf. A budget-based method is applied to calculate the cross-shelf volume transport across key interfaces: the estuary-coast interface (the exit between Lantau Island and Macau at the PRE mouth) and the coast-inner shelf interface (25 m isobath). The results show that the Lantau-Macau exit serves as a key transport gateway for estuary-coast exchange, with a net offshore transport of 1.97 × 10³ m³ s⁻¹ in the dry season and 2.61 × 10³ m³ s⁻¹ in the wet season, respectively. The dynamical analysis shows that, at the estuary-coast interface, the islands strengthen the onshore horizontal advection, increasing the net onshore transport by 41.37 % in the dry season, and augment the offshore barotropic gradient, increasing the net offshore transport by 422 % in the wet season. At the coast-inner shelf interface, the cross-shelf transport is onshore at 11.35 × 10³ m³ s⁻¹ during the dry season and offshore at 0.74 × 10³ m³ s⁻¹ during the wet season. During the dry season, the islands enhance both the onshore and offshore transport through increased bottom pressure torque (BPT) and nonlinear advection, respectively, with the two effects nearly balancing each other. However, in the wet season, the islands strengthen the Joint Effect of Baroclinity And Relief (JEBAR), counteracting the advection and making BPT-driven onshore transport to become dominant, thereby enhancing onshore transport by 63 % at this interface. This study has implications for land-ocean interaction research and effective coastal management.

跨大陆架运输对河口-海岸-大陆架连续体中的物质交换至关重要。本研究采用海洋-大气-波浪-泥沙耦合输运（COAWST）模式系统，对珠江口（PRE）与邻近海岸和内陆架之间的跨陆架输运进行量化，并研究岛屿对跨陆架输运的影响。采用基于预算的方法计算了关键界面的跨大陆架体积运输：河口-海岸界面（大屿山和澳门之间的出口在PRE口）和海岸-内大陆架界面（25 m等深线）。结果表明：大屿山—澳门出口是河口—海岸交换的重要运输门户，干季和湿季的净离岸运输量分别为1.97 × 103 m3 s−1和2.61 × 103 m3 s−1。动力学分析表明，在河口-海岸界面，岛屿增强了陆上水平平流，旱季增加了41.37%的陆上净输送量，增加了海上正压梯度，雨季增加了422%的海上净输送量。在海岸-内陆架界面，陆架间运输在旱季为陆上11.35 × 103 m3 s- 1，在雨季为海上0.74 × 103 m3 s- 1。在旱季，岛屿分别通过增加底部压力扭矩（BPT）和非线性平流来增强陆上和海上运输，两者的作用几乎相互平衡。然而，在雨季，岛屿加强了斜压和下沉联合效应（JEBAR），抵消了平流，使bpt驱动的陆上运输成为主导，从而在该界面增强了63%的陆上运输。该研究对陆地-海洋相互作用研究和有效的海岸管理具有指导意义。

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引用次数: 0

A novel framework for studying oceanic freshwater transports, and its application in discerning the modelled fate of freshwater around the coast of Greenland 一个研究海洋淡水运输的新框架，及其在识别格陵兰海岸周围淡水的模拟命运中的应用

IF 2.9 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Ocean Modelling

Pub Date : 2025-09-01 DOI: 10.1016/j.ocemod.2025.102599

Fraser William Goldsworth

In the sub-polar North Atlantic, the accumulation of fresh meltwaters from Greenland and the Arctic can impact the strength of the climatically important Atlantic Meridional Overturning Circulation. In this study I investigate and map out the processes that contribute to the accumulation of freshwater in four different regions around Greenland, quantifying horizontal transports of freshwater and the expansion and depletion of freshwater reservoirs by surface sources and interior mixing. Rather than using traditional freshwater budgets, whose flaws are well documented, I propose the novel use of the freshwater transformation framework and apply it to outputs from an eddy resolving coupled climate model (10 km atmosphere and 5 km ocean).

Analysing volume transports in salinity space we observe the salinification of the boundary currents surrounding Greenland as they flow from Fram Strait towards the Labrador Sea. Using the freshwater transformation framework we are able to link the salinification to mixing, sea-ice formation or the accumulation of freshwaters stored in the waters surrounding Greenland. The balance changes depending upon the region and season under question. The mixing of freshwaters is found to be stronger during wintertime than in summertime. Furthermore, mixing plays a more dominant role in the freshwater transformation budget off Southern Greenland, where sea-ice cover is low, than off Northern Greenland, where sea-ice cover is high.

在亚极地北大西洋，来自格陵兰岛和北极的新鲜融水的积累可以影响对气候具有重要意义的大西洋经向翻转环流的强度。在这项研究中，我调查并绘制了导致格陵兰周围四个不同地区淡水积累的过程，量化了淡水的水平输送以及地表水源和内部混合导致的淡水水库的扩张和枯竭。我没有使用传统的淡水预算（其缺陷已被充分证明），而是提出了淡水转换框架的新用途，并将其应用于涡解析耦合气候模式（10公里大气和5公里海洋）的输出。通过分析盐度空间的体积输送，我们观察到格陵兰岛周围的边界流从弗拉姆海峡流向拉布拉多海时的盐碱化。利用淡水转化框架，我们能够将盐碱化与混合、海冰形成或储存在格陵兰周围水域的淡水积累联系起来。平衡的变化取决于所讨论的地区和季节。人们发现，冬季淡水的混合比夏季更强烈。此外，在海冰覆盖较少的南格陵兰海域，混合在淡水转化预算中比海冰覆盖较多的北格陵兰海域起着更大的作用。

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