Ocean Modelling最新文献_第3页

Prediction of three-dimensional ocean temperature, salinity and current fields based on fourier neural operators 基于傅里叶神经算子的三维海洋温度、盐度和洋流场预测

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

Ocean Modelling

Pub Date : 2025-12-13 DOI: 10.1016/j.ocemod.2025.102674

Guangjun Xu , Yucheng Shi , Xueming Zhu , Zhao Jing , Shuyi Zhou , Jiexin Xu , Huabing Xu , Guancheng Wang , Dongyang Fu , Changming Dong

Accurate, simultaneous prediction of three-dimensional (3D) ocean temperature, salinity, and current fields is vital for understanding ocean dynamics and informing marine applications. This study introduces a Fourier Neural Operator (FNO)-based model specifically designed for this 3D multi-variable task, leveraging Fourier transforms to efficiently capture complex multi-scale spatio-temporal dependencies within the ocean state. Evaluated on multi-year data from the South China Sea, the FNO model demonstrates strong predictive skill. Compared against the Copernicus Marine Environment Monitoring Service (CMEMS) operational forecast product, our model achieved significant average reductions in Root Mean Square Error (RMSE) by 43.07 % and Mean Absolute Error (MAE) by 46.18 % (averaged across all four variables and the full 10-day forecast horizon). The FNO particularly excels in short-term predictions (1–3 days), outperforming conventional deep learning benchmarks (such as U-Net) in accuracy for key variables. Spectral analysis reveals this outperformance is linked to FNO's superior ability to represent the energy of multi-scale oceanic features, indicating a more faithful capture of their structures, while also offering substantial computational efficiency compared to traditional numerical simulations. While forecast accuracy decreases over longer periods, this work highlights the considerable potential of FNOs as a scalable and effective data-driven approach for advancing 3D oceanographic forecasting.

准确、同步预测三维（3D）海洋温度、盐度和洋流场对于理解海洋动力学和为海洋应用提供信息至关重要。本研究引入了专为该3D多变量任务设计的基于傅里叶神经算子（FNO）的模型，利用傅里叶变换有效捕获海洋状态中复杂的多尺度时空依赖关系。对南海多年数据的评估表明，FNO模型具有较强的预测能力。与哥白尼海洋环境监测服务（CMEMS）业务预报产品相比，我们的模型实现了显著的均方根误差（RMSE）平均降低43.07%，平均绝对误差（MAE）平均降低46.18%（在所有四个变量和整个10天预测范围内的平均值）。FNO尤其擅长短期预测（1-3天），在关键变量的准确性上优于传统的深度学习基准（如U-Net）。光谱分析表明，这种优异的性能与FNO在表示多尺度海洋特征能量方面的优越能力有关，这表明FNO更忠实地捕获了海洋特征的结构，同时与传统的数值模拟相比，FNO也提供了可观的计算效率。虽然预测精度在较长时间内会下降，但这项工作强调了FNOs作为一种可扩展和有效的数据驱动方法，在推进三维海洋预报方面的巨大潜力。

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引用次数: 0

Three decades of interannual variability in modeled primary production in the Peruvian Upwelling Region (1993–2022) 秘鲁上升流区模拟初级产量的三十年年际变化（1993-2022）

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

Ocean Modelling

Pub Date : 2025-12-13 DOI: 10.1016/j.ocemod.2025.102669

Rodrigo Mogollón , François Colas , Vincent Echevin , Jorge Tam , Dante Espinoza-Morriberón

This study explores three decades (1993–2022) of interannual variability in primary production (PP) using a coupled physical–biogeochemical model. A long-term positive trend in the vertically-integrated PP was found in both the central and northern domains, exceeding 6 mol C m⁻² yr⁻¹ per decade, highlighting an increasing contribution of these regions to overall productivity. Over the study period, the region produced a cumulative 8.6 billion metric tons of carbon, underscoring its substantial role as a carbon sink despite its relatively small spatial extent. Interannual climatic events strongly modulated PP. Negative PP anomalies during El Niño events and positive PP anomalies during La Niña phases were primarily constrained within the first 10 m depths. The sensitivity analysis demonstrated that biological drivers, particularly chlorophyll concentration and phytoplankton biomass, dominated PP variability, accounting for over 95% of the explained variance. Physical factors, such as light availability, temperature, played secondary but significant roles during extreme events, modulating PP alongside biological processes. Overall, the findings reveal a resilient yet highly dynamic system, with long-term increases in productivity counterbalanced by episodic disruptions tied to interannual climatic variability. These results emphasize the importance of biological drivers in sustaining productivity and provide valuable insights into the factors shaping the variability and trends in this highly productive marine ecosystem.

本研究利用物理-生物地球化学耦合模型探讨了初级生产力（PP）的30年（1993-2022）年际变化。在中部和北部地区，垂直整合PP呈长期正趋势，超过6 mol C m−2 yr−1 / 10年，突出表明这些地区对整体生产力的贡献越来越大。在研究期间，该地区累计产生了86亿吨碳，尽管其空间范围相对较小，但仍强调了其作为碳汇的重要作用。年际气候事件强烈调节了PP。El Niño期的负PP异常和La Niña期的正PP异常主要局限于前10 m深度。敏感性分析表明，生物驱动因素，特别是叶绿素浓度和浮游植物生物量，主导了PP变异，占解释方差的95%以上。物理因素，如光可用性、温度，在极端事件中起次要但重要的作用，调节PP和生物过程。总体而言，研究结果揭示了一个有弹性但高度动态的系统，生产力的长期增长被与年际气候变化相关的间歇性中断所抵消。这些结果强调了生物驱动因素在维持生产力方面的重要性，并为形成这一高产海洋生态系统的变异性和趋势的因素提供了有价值的见解。

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引用次数: 0

Modelling wave dissipation and mean water level over salt marshes 模拟盐沼上的波浪耗散和平均水位

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

Ocean Modelling

Pub Date : 2025-12-12 DOI: 10.1016/j.ocemod.2025.102672

Laura Lavaud , Xavier Bertin , Kévin Martins

Besides its well-known capacity to dissipate wave energy, salt marsh vegetation can also affect wave setup, although this mechanism has been much less studied and quantified so far. This study reports on a field experiment conducted under moderate energy conditions across a French Atlantic salt marsh. The data analysis is complemented with numerical simulations performed with the 3D fully-coupled wave–current modelling system SCHISM. While the model could already resolve vegetation-induced drag on mean currents and turbulence, it was here extended to account for vegetation intrawave drag effects and the wave force associated with vegetation-induced dissipation. Using published lab data, we first verify the model’s capacity to reproduce wave dissipation by vegetation and its effect on mean water levels, namely a reduction in wave setup, which is controlled by wave–current-vegetation interactions including intrawave processes. In the field, the model also demonstrates good predictive skills in simulating wave parameters across vegetation and suggests that vegetation can decrease the wave setup. However, this last process was too modest to be measured with pressure transducers, calling for future field experiments under storm conditions. This capacity of vegetation to reduce nearshore mean water levels should be thoroughly considered when evaluating the potential of salt marshes as nature-based coastal protection. This study places the SCHISM model as a state-of-the-art, efficient tool to simulate 3D multi-scale wave–current processes over vegetation ecosystems. Our results finally highlight that vegetation and depth-induced breaking induce a frequency-dependent dissipation, whose representation in phase-averaged models is presently limited and will require future research.

除了众所周知的耗散波浪能量的能力外，盐沼植被还可以影响波浪的形成，尽管迄今为止对这一机制的研究和量化都要少得多。本研究报告了在法国大西洋盐沼的中等能量条件下进行的现场试验。数据分析与三维全耦合波流模拟系统SCHISM的数值模拟相辅相成。虽然该模型已经可以解决平均海流和湍流中植被引起的阻力，但这里将其扩展到考虑植被波内阻力效应和与植被引起的耗散相关的波浪力。利用已发表的实验室数据，我们首先验证了该模型再现植被耗散波的能力及其对平均水位的影响，即波浪设置的减少，这是由波-流-植被相互作用控制的，包括波内过程。在野外，该模型在模拟跨植被的波浪参数方面也显示出良好的预测能力，并表明植被可以减少波浪的设置。然而，最后一个过程过于温和，无法用压力传感器测量，需要在未来的风暴条件下进行现场实验。在评估盐沼作为基于自然的海岸保护的潜力时，应充分考虑到植被降低近岸平均水位的能力。本研究将SCHISM模型作为模拟植被生态系统上三维多尺度波流过程的最先进、最有效的工具。我们的研究结果最后强调，植被和深度诱发的断裂引起频率相关耗散，其在相位平均模型中的表示目前是有限的，需要进一步的研究。

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引用次数: 0

Linking ROMS with watershed models for simulating hydrodynamics and thermohaline dynamics in a coastal lagoon affected by extreme weather events 将ROMS与流域模型相结合，用于模拟受极端天气事件影响的沿海泻湖的水动力学和热盐动力学

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

Ocean Modelling

Pub Date : 2025-12-12 DOI: 10.1016/j.ocemod.2025.102673

Francisco Pereira , Francisco López-Castejón , Félix Francés , Andrés Alcolea , Joaquín Jiménez-Martínez , João Miguel Dias , Javier Gilabert

Reproduction of hydrodynamic and hydrologic processes in complex coastal lagoons requires the development and calibration of linked numerical model implementations, that can show accuracy even in extreme weather scenarios. To achieve that, robust datasets for a wide variety of parameters are needed to validate the model. This study aimed to develop and validate a hydrodynamic model linked to a groundwater and a watershed model, for the microtidal Mar Menor coastal lagoon located in the southeastern Spain. Special concern was given to flash flood events, which, although infrequent, are proved to trigger mass mortality of species inside the lagoon. To achieve that, a ROMS numerical implementation was developed and linked to atmospheric (HARMONIE-AROME), groundwater (SUTRA), and watershed (TETIS) models. The model results were compared with a robust dataset with hydrodynamic, salinity, and water temperature data. Special attention was given to the September 2019 Cut-off Low (CoL) flash flood event. The model demonstrated high accuracy in reproducing the lagoon’s dynamics under normal conditions, including the currents in the narrow inlets connecting the lagoon with the Mediterranean Sea. After the CoL event, an extraordinary hydrological scenario developed — characterized by strong vertical stratification that persisted for over a month — explained by the lack of sufficient shear instability to overcome buoyancy forces induced by density gradients, despite the occurrence of a two-layer opposite direction flow. Runoff associated with the CoL event also led to a nearly 20 % reduction in the lagoon’s Water Renewal Time.

在复杂的沿海泻湖中再现水动力和水文过程需要开发和校准相关的数值模式实施，即使在极端天气情况下也能显示出准确性。为了实现这一目标，需要各种参数的鲁棒数据集来验证模型。本研究旨在开发和验证一个与地下水和流域模型相关的水动力学模型，该模型适用于位于西班牙东南部的Mar Menor沿海微潮泻湖。特别关切的是突发洪水事件，这种事件虽然不经常发生，但已证明会引起泻湖内物种的大量死亡。为了实现这一目标，开发了一种ROMS数值实施方法，并将其与大气（HARMONIE-AROME）、地下水（SUTRA）和流域（TETIS）模型联系起来。模型结果与包含水动力、盐度和水温数据的稳健数据集进行了比较。特别值得关注的是2019年9月的截止下限（CoL）山洪事件。该模型在正常条件下（包括连接泻湖与地中海的狭窄入口的水流）重现泻湖动态的准确性很高。CoL事件发生后，形成了一种非同寻常的水文情景，其特征是持续一个多月的强烈垂直分层，尽管出现了两层相反方向的流动，但缺乏足够的剪切不稳定性来克服密度梯度引起的浮力。与寒冷事件相关的径流也导致泻湖的水更新时间减少了近20%。

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引用次数: 0

Impact of storm-induced morphological changes on extreme wave runup and overtopping of sandy beaches and dunes 风暴引起的形态变化对沙滩和沙丘极端浪涌和漫顶的影响

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

Ocean Modelling

Pub Date : 2025-12-12 DOI: 10.1016/j.ocemod.2025.102671

Mohammad Jamous, Reza Marsooli

Extreme wave runup and overwash are among the most common natural hazards that threaten coastal regions by causing severe erosion and flooding. Sea level rise is expected to exacerbate these hazards, as deeper water allows for more energetic waves to reach shorelines. Physics-based modeling is a robust approach for quantifying these extreme wave hazards, yet it remains a challenging task due to the presence of various coupled physical processes, including rapid morphological changes and their effects on hydrodynamics and waves. In this work, we propose a modeling framework to simulate erosion, runup, and overwash of sandy beach-dune systems during extreme wave events while accounting for the effects of morphological changes. The framework consists of a hierarchy of hydrodynamic (XBeach-Non-Hydrostatic), morphodynamic (XBeach-Surfbeat), and spectral wave and ocean circulation models (ADCIRC+SWAN) that simulate total water levels, frequency-direction wave spectrum, morphological changes of beach-dune systems, and runup and overwash of individual waves. The framework is applied to Hurricane Sandy to simulate wave hazards at beach-dune systems on the Barrier Islands of New Jersey in the U.S. To demonstrate the importance of morphological changes on wave hazards, we perform simulations under various scenarios of sea level rise. We find that excluding morphological changes results in wave overwash volumes being overestimated at dunes that are not severely eroded during the storm, while underestimated at dunes that are severely eroded. Besides the effects of morphological changes, the results also show a substantial shift in storm impact regimes under future sea level rise scenarios.

极端浪涌和冲过是最常见的自然灾害，它们会造成严重的侵蚀和洪水，威胁沿海地区。海平面上升预计会加剧这些危险，因为更深的水允许更强的海浪到达海岸线。基于物理的建模是量化这些极端波浪危害的强大方法，但由于各种耦合物理过程的存在，包括快速形态变化及其对水动力学和波浪的影响，它仍然是一项具有挑战性的任务。在这项工作中，我们提出了一个建模框架来模拟极端波浪事件期间沙滩-沙丘系统的侵蚀、冲刷和冲过，同时考虑形态变化的影响。该框架由水动力（XBeach-Non-Hydrostatic）、形态动力（XBeach-Surfbeat）和频谱波浪和海洋环流模型（ADCIRC+SWAN）组成，这些模型模拟了总水位、频率-方向波浪谱、海滩-沙丘系统的形态变化以及单个波浪的上升和冲过。将该框架应用于飓风桑迪，模拟美国新泽西州堰洲岛海滩-沙丘系统的波浪危害。为了证明形态变化对波浪危害的重要性，我们在各种海平面上升情景下进行了模拟。我们发现，排除形态变化会导致风暴期间侵蚀不严重的沙丘的波浪冲过量被高估，而严重侵蚀的沙丘的波浪冲过量被低估。除了形态变化的影响外，研究结果还表明，在未来海平面上升的情景下，风暴影响机制也会发生实质性的变化。

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引用次数: 0

West-East asymmetry in the South Pacific Western subtropical mode water 南太平洋西副热带模态水的东西不对称性

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

Ocean Modelling

Pub Date : 2025-12-11 DOI: 10.1016/j.ocemod.2025.102670

Xueying Wang, Yiyong Luo, Yingying Wang, Ruiyi Chen

This study investigates the spatio-temporal variability and forcing mechanisms of the South Pacific western subtropical mode water (SPWSTMW) using the RG-Argo observations and the eddy-resolving GLORYS12 reanalysis from 2004 to 2023. The SPWSTMW exhibits pronounced zonal asymmetries in both its variability and forcing processes. To better understand these west-east asymmetries, we divide the SPWSTMW into the West (150°E–160°E) and East (160°E–170°W) types. On a seasonal timescale, the West type forms approximately one month earlier than the East type, primarily due to enhanced heat convergence from the mean flow and associated eddies of the East Australian Current. These oceanic processes effectively offset winter surface heat loss, accelerating upper-ocean restratification and subduction. On an interannual timescale, the East type volume correlates strongly with El Niño-Southern Oscillation (ENSO) through direct atmospheric forcing. However, the West type volume shows no significant correlation with ENSO, resulting from the complex interaction of surface heat flux, mean flow-induced heat convergence, and eddy-induced heat convergence. These findings underscore the critical role of regional ocean dynamics in modulating SPWSTMW variations.

利用2004 - 2023年RG-Argo观测资料和gloys12涡旋再分析资料，研究了南太平洋西部副热带模态水（SPWSTMW）的时空变化及其强迫机制。SPWSTMW在变率和强迫过程中都表现出明显的纬向不对称性。为了更好地理解这些东西不对称，我们将SPWSTMW分为西（150°E - 160°E）和东（160°E - 170°W）两类。在季节时间尺度上，西型比东型早形成大约一个月，主要是由于东澳大利亚洋流的平均流和相关漩涡增强了热辐合。这些海洋过程有效地抵消了冬季地表热损失，加速了上层海洋的再冰化和俯冲。在年际尺度上，东型体积通过大气直接强迫与El Niño-Southern涛动（ENSO）密切相关。而西型体积与ENSO的相关性不显著，这是地表热通量、平均流致热辐合和涡旋致热辐合复杂相互作用的结果。这些发现强调了区域海洋动力学在调节SPWSTMW变化中的关键作用。

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引用次数: 0

Assessing basin scale modelling for projecting storm surge extremes under climate change scenarios in northwest Ireland 评估在爱尔兰西北部气候变化情景下预测极端风暴潮的流域尺度模型

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

Ocean Modelling

Pub Date : 2025-12-03 DOI: 10.1016/j.ocemod.2025.102660

Tasneem Ahmed , Andrea Cucco , Giovanni Quattrocchi , Leo Creedon , Iulia Anton , Michele Bendoni , Stefano Taddei , Carlo Brandini , Salem S Gharbia

This study evaluates the performance of the SHYFEM (System of HydrodYnamic Finite Element Modules) ocean model in simulating storm surges within Donegal Bay (northwest Ireland) for climate projection applications. A high-resolution Basin Scale Model (BSM) configuration of SHYFEM, spanning the North Atlantic is employed in barotropic mode accounting exclusively for atmospheric forcing with no tidal contribution included. To evaluate its accuracy, the BSM is compared against a Limited Area Model (LAM) configuration of SHYFEM implemented at the same study site.

The LAM includes tidal constituents through the downscaling of sea surface height (SSH) from a calibrated deep-water ocean model provided by the Copernicus Marine Environment Monitoring Service (CMEMS). Comparison is performed to quantify the impact of non-linear tide-surge interaction on residual water levels computation.

On average the LAM achieves 3 cm greater accuracy than the BSM in reproducing the time series of residual water levels measured by four tide gauges within the bay. Nevertheless, although both models tend to underestimate the extreme values, the BSM better captures the climatological statistics of storm surge events, closely matching the observed return levels associated with 5, 10, 25, and 50 year return periods.

Further improvements in return level estimates and residual water level error metrics are obtained through iterative calibration of main model parameters, validating the BSM’s effectiveness in simulating storm surges despite the absence of tide-surge interaction.

A Chi-squared significance test applied to tide gauge observations confirms that tide-surge interaction is statistically non-significant within Donegal Bay for surge thresholds at the 99th, 99.95th, and 99.99th percentiles. These findings support the use of BSM, driven exclusively with atmospheric fields (without including tides), for reliable simulation of storm surges and their climatological statistics in this region.

本研究评估了SHYFEM（水动力有限元模块系统）海洋模式在模拟多尼戈尔湾（爱尔兰西北部）风暴潮中的气候预测应用的性能。采用横跨北大西洋的高分辨率盆地尺度模式（BSM）配置，在正压模式下只考虑大气强迫，不包括潮汐贡献。为了评估其准确性，将BSM与在同一研究地点实施的SHYFEM的有限区域模型（LAM）配置进行了比较。通过哥白尼海洋环境监测服务（CMEMS）提供的校准深水海洋模型，通过降低海面高度（SSH）的比例，LAM包括潮汐成分。通过比较来量化非线性潮涌相互作用对剩余水位计算的影响。在重现湾内四个潮汐计所测得的剩余水位时间序列时，LAM的准确度比BSM平均高3厘米。然而，尽管两种模式都倾向于低估极端值，但BSM更好地捕捉了风暴潮事件的气候统计数据，与观测到的与5年、10年、25年和50年的回归期相关的回归水平密切匹配。通过对主要模型参数的迭代校准，进一步改进了回归水位估计和剩余水位误差指标，验证了BSM在没有潮涌相互作用的情况下模拟风暴潮的有效性。应用于验潮仪观测的卡方显著性检验证实，在第99、99.95和99.99百分位的潮涌阈值上，多尼戈尔湾内的潮涌相互作用在统计上不显著。这些发现支持使用仅由大气场（不包括潮汐）驱动的BSM来可靠地模拟该地区的风暴潮及其气候统计。

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引用次数: 0

Evolution of wind-generated shallow water waves in a Benney–Luke equation Benney-Luke方程中风力产生的浅水波浪的演化

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

Ocean Modelling

Pub Date : 2025-12-02 DOI: 10.1016/j.ocemod.2025.102659

Montri Maleewong , Roger Grimshaw

In our recent papers Maleewong and Grimshaw (2024b, 2025), we used the Korteweg–de Vries (KdV) equation and its two-dimensional extension, the Kadomtsev–Petviashvili (KP) equation to describe the evolution of wind-driven water wave packets in shallow water. Both equations were modified to include the effect of wind forcing, modelled using the Miles critical level instability theory. In this paper that is extended to a Benney–Luke (BL) equation, similarly modified for wind forcing. The motivation is that the BL equation is isotropic in the horizontal space variables, unlike the KP model, and noting that the KdV model is one-dimensional. The modified BL equation is studied using wave modulation theory as in our previous work on the forced KdV and KP equations, and with comprehensive numerical simulations. Despite the very different spatial structure the results show that under the right initial conditions and parameter settings, solitary wave trains again emerge.

在我们最近的论文Maleewong和Grimshaw （2024b, 2025）中，我们使用了Korteweg-de Vries （KdV）方程及其二维扩展Kadomtsev-Petviashvili （KP）方程来描述浅水中风力水波包的演化。两个方程都进行了修改，以包括风强迫的影响，使用迈尔斯临界水平不稳定理论建模。本文将其推广为对风强迫进行类似修正的Benney-Luke （BL）方程。动机是BL方程在水平空间变量中是各向同性的，不像KP模型，并且注意到KdV模型是一维的。与之前研究强制KdV和KP方程一样，本文采用波调制理论对修正后的BL方程进行研究，并进行了全面的数值模拟。结果表明，在适当的初始条件和参数设置下，孤波列再次出现。

引用次数: 0

DB-SICNet: A dual-branch model for predicting Arctic sea ice concentration DB-SICNet：预测北极海冰浓度的双分支模式

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

Ocean Modelling

Pub Date : 2025-12-01 DOI: 10.1016/j.ocemod.2025.102658

Ling Tan , Jinlong Xu , Wei Zhang , Wenjia Chen , Jingming Xia

In the context of global climate warming, the changes in Arctic sea ice have garnered significant attention. Traditional models face challenges in predicting sea ice concentration (SIC) due to the complexity and interdependence of meteorological factors, which make it difficult to quantify their impacts on sea ice variability. Temporal dynamics of sea ice concentration changes are underutilized, while the limitations of single-model predictions worsen the issue. To address these challenges, this paper proposes a novel dual-branch Arctic sea ice concentration forecasting method, called DB-SICNet. This method integrates OSI-SAF sea ice data and ERA5 meteorological data, employing a multi-scale feature fusion module to extract key features from the meteorological factors. A dynamic temporal weighting mechanism captures periodic variation patterns by assigning weights to data points over time, and the model combines ConvLSTM and UNet in a dual-branch integrate to improve prediction accuracy. Comprehensive experimental evaluations demonstrate that, compared to popular models such as CMIP6 and IceNet, DB-SICNet provides more accurate forecasts of Arctic sea ice coverage for the upcoming month. The study also employs DeepLIFT attribution analysis to identify the critical role of sea surface temperature in the prediction of SIC. The findings of this research can offer robust support for navigation planning and sea ice-related applications in the Arctic region.

在全球气候变暖的背景下，北极海冰的变化引起了人们的极大关注。由于气象因子的复杂性和相互依赖性，传统模式在预测海冰浓度方面面临挑战，难以量化其对海冰变率的影响。海冰浓度变化的时间动态未得到充分利用，而单一模式预测的局限性使这一问题更加严重。为了解决这些挑战，本文提出了一种新的双分支北极海冰浓度预测方法，称为DB-SICNet。该方法将OSI-SAF海冰数据与ERA5气象数据相结合，采用多尺度特征融合模块从气象要素中提取关键特征。该模型采用动态时间加权机制，通过为数据点分配权重来捕获周期性变化模式，并将ConvLSTM和UNet结合在双分支集成中，以提高预测精度。综合实验评估表明，与CMIP6和IceNet等流行模式相比，DB-SICNet对未来一个月的北极海冰覆盖率提供了更准确的预测。利用DeepLIFT归因分析，确定了海表温度在SIC预测中的关键作用。本研究结果可为北极地区的导航规划和海冰相关应用提供强有力的支持。

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引用次数: 0

Improving Kuroshio forecasts with an eddy-resolving AI prediction system 利用涡流解析人工智能预测系统改进黑潮预测

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

Ocean Modelling

Pub Date : 2025-11-28 DOI: 10.1016/j.ocemod.2025.102657

Junkai Qian , Qiang Wang , Wuhong Guo , Huier Mo , Hui Zhang

The Kuroshio, a powerful western boundary current in the North Pacific, exhibits multi-scale variability that profoundly affects regional weather, climate, marine ecosystems, and fisheries, rendering its accurate prediction indispensable. However, this variability is driven by complex multi-scale physical processes, necessitating high-resolution numerical models that are computationally expensive and often constrained by limited timeliness. In recent years, the emergence of data-driven models has opened new avenues for ocean forecasting, and the global ocean intelligent prediction systems are now approaching or even surpassing traditional numerical models across various metrics. Despite these advances, their performance in the Kuroshio region remains limited. To address this challenge, this study develops an eddy-resolving (1/12°) Kuroshio Intelligent Prediction System (KIPS) based on the Swin Transformer architecture. Specifically designed to capture Kuroshio dynamics, KIPS uses an autoregressive strategy to generate daily forecasts of three-dimensional temperature, salinity, current, and sea surface height, with a lead time of up to 10 days. KIPS achieves higher accuracy compared to existing numerical and AI-based prediction systems, while significantly reducing computational costs. In operational forecasts, KIPS successfully captures several recent eddy shedding and merging events in the southern Kuroshio region of Japan, demonstrating agreement with near-real-time satellite observations. These results underscore the value of integrating prior physical knowledge into region-specific forecast systems to improve fine-scale ocean prediction.

黑潮是北太平洋强大的西边界流，其多尺度变化深刻影响着区域天气、气候、海洋生态系统和渔业，因此对黑潮的准确预测必不可少。然而，这种可变性是由复杂的多尺度物理过程驱动的，需要高分辨率的数值模型，这些模型计算成本很高，而且往往受限于有限的时效性。近年来，数据驱动模式的出现为海洋预报开辟了新的途径，全球海洋智能预报系统在各种指标上正在接近甚至超越传统的数值模式。尽管取得了这些进展，但它们在黑潮地区的表现仍然有限。为了解决这一挑战，本研究开发了一种基于Swin变压器架构的涡流分辨力（1/12°）黑潮智能预测系统（KIPS）。KIPS专为捕捉黑潮动态而设计，使用自回归策略生成三维温度、盐度、洋流和海面高度的每日预报，提前期长达10天。与现有的数值和基于人工智能的预测系统相比，KIPS实现了更高的精度，同时显著降低了计算成本。在业务预报中，KIPS成功捕获了日本黑潮南部地区最近发生的几次涡旋脱落和合并事件，与近实时卫星观测结果一致。这些结果强调了将先验物理知识整合到特定区域的预测系统中以改善精细尺度海洋预测的价值。

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