Ocean Modelling最新文献

The seasonal variation of intermediate meridional overturning circulation (IMOC) in the South China Sea (SCS) is investigated using the Simple Ocean Data Assimilation version 2.2.4 (SODA2.2.4) product for the period of 1950–2010. The SCS IMOC displays distinct seasonal features, with a counterclockwise cell dominating the interior SCS (12∼18°N, 200∼700 m) in winter and a broader clockwise cell occupying the region for (7∼20°N, 50∼900 m) in summer. By removing the 12-month average, the main characteristics of the seasonal IMOC is captured deeply. There is a counterclockwise anomaly in winter and a clockwise anomaly in summer occurring in the region for (8∼20°N, 100∼1000 m). And the strongest anomalies of the overturning stream functions are mainly located in (12∼17°N, 200∼400 m) that is taken as the representative region to study the seasonal IMOC. A dynamical decomposition of the IMOC seasonal anomaly allows a further look into the seasonal variation of the SCS IMOC. The IMOC seasonal anomaly is decomposed into three components: the Ekman component, the vertical shear component, and the external component. The Ekman component exhibits a full cell clockwise in winter and counterclockwise in summer with a negative contribution to the IMOC anomaly. The vertical shear component has a strong cell counterclockwise in winter and clockwise in summer occupying most of the areas above 1000 m with a positive contribution to the IMOC anomaly. The external component has a relatively complex structure, and its positive and negative contributions to the IMOC anomaly alternate with increasing latitude at 200∼1000 m. According to the seasonal fractional covariance of these three components on the IMOC anomaly in the representative region, the vertical shear component and the Ekman component have the main contributions to the IMOC seasonal anomaly, and the external component has a limited impact. The vertical shear and its meridional difference can lead to a downward motion at around 12°N and an upward motion at around 17°N in winter, and reverse motions in summer. The seasonal vertical motions will cause an overturning counterclockwise in winter and clockwise in summer. The Ekman component is mainly driven by the monsoon over the SCS that generates the Ekman transport northward in winter and southward in summer. The seasonal Ekman transport and its return flow together form an overturning clockwise in winter and counterclockwise in summer. And the external component counterclockwise in winter and clockwise in summer between 14°N and 17°N is associated with the horizontal flow northeastward in winter and southwestward in summer zonally going over shallower or greater depths, which can induce seasonal reverse upwelling and downwelling at different latitudes.

The prediction of dissolved oxygen (DO) concentration in seawater is a mixed multivariate time series measurement task that is affected by many factors. In order to timely understand the status of seawater quality and reduce the losses caused by seawater pollution, it is of great significance to accurately predict the dissolved oxygen concentration in the water body. In this paper, a seawater dissolved oxygen prediction model MEMD-WGAN_IGP based on hybrid multivariate empirical mode decomposition (MEMD) and generative adversarial network (GAN) is proposed.The multivariate data after removing outliers are decomposed using multivariate modal decomposition, and the data are reconstructed into high-frequency components, low-frequency components, and trend terms by sample entropy, and then added to the improved generative adversarial network to obtain the final prediction results. The feasibility of the improved model is demonstrated by ablation experiments and compared with the classical time series data prediction model, the error MSE of the prediction results reaches 0.074, and the fitting degree R2 reaches 0.970, which is the best performance in the experiments, which proves that the model shows better prediction accuracy and stability in the marine data prediction problem.

Arctic sea ice leads to a significant dissipation of tidal energy, necessitating its inclusion in global tidal models. However, most global tidal models either neglect or only partially incorporate the impact of sea ice on tides. This study proposes a method to model the dissipative forces exerted by sea ice on tides without directly coupling to a sea ice model, yet utilizing sea ice parameters such as thickness and concentration. Our approach involves (re)-categorizing the sea ice cover into regions dominated either by the velocity difference between sea ice and tides (Vertical Shear (VS)) or by the shear from drifting sea ice on tides (Horizontal Shear (HS)), which primarily govern the energy dissipation between tides and sea ice. The subdivision and resulting areas of these HS and VS regions are based on a nondimensional number referred to as the Friction number, which is the ratio of the internal stress of the sea ice field to the ice–water frictional stress, and directly depends on the thickness and concentration of the sea ice. The new parameterization is validated through a performance assessment comparing it to a commonly used approach of assuming all the sea ice to be stationary (landfast). The seasonal modulation of the M₂ tidal component, quantified as the March–September difference, serves as the performance metric, demonstrating that the new parameterization has better agreement with observations from altimeter- and tide gauge-derived seasonal modulation. The results indicate that the physics of ice–tide interaction is better represented with the new parameterization of sea ice-induced dissipation, making it suitable for investigating the effects of declining sea ice thickness on tides.

The transformation of wave height is of paramount significance in coastal engineering and the design of coastal structures. Considering the influence of air bubbles, this study devised an optimal dissipation model for accurately calculating changes in significant wave height (H_m0) and wave set-up for irregular waves undergoing breaking. Existing regular wave breaking models, which consider the effects of air bubbles, were adapted for direct application to irregular waves by deriving novel formulations. The proposed models leverage the probability of the fraction of broken waves. H_m0 was computed using the energy balance equation, while the wave set-up was calculated based on the momentum balance equation. A wide range of test scenarios, incorporating diverse scales (small and large) and experimental field data, was considered for validation. One of the proposed models, namely model-I (M-I), particularly demonstrated superior performance, manifesting lower error indices (P20), root-mean-square relative error (RMSRE), and Brier skill score (BSS) values in computing both H_m0 and wave set-up. Therefore, utilising M-I is strongly recommended for the precise estimation of H_m0 and set-up transformation.

In polar regions, sea ice linear kinematic features (LKFs) play a critical role in the exchange of mass and energy between the ocean and atmosphere. These features also serve as an important reference for navigation decision, highlighting the growing need to accurately monitor and simulate their changes. An identification and labeling method using artificial intelligence (AI) to detect LKFs based on Synthetic Aperture Radar (SAR) data is proposed in this study. This approach uses sea ice deformation data derived from sea ice drift in the SAR observations and employs a specialized encoder–decoder convolutional neural network, known as LadderNet, to segment these fine-grained LKFs. A post-processing algorithm utilizing connected region detection further assigns markers for individual LKFs. Results show that our detection method has a higher accuracy with F1 Scores ranging between 0.6 and 0.7 than that using UNET architecture. Training the AI model with seasonal data effects the detection results slightly. Compared to the classical algorithm, our study also demonstrates more consistent detection results for both numerical model and observations regardless of practical parameters after training, which provides a standardized metric for inter-comparisons between models and observations.

The oblique interactions between internal solitary waves frequently occur in the ocean owing to their different propagation directions after originated from more than one potential generation sites, which can further be modulated and reshaped by the varying topography and the Earth’s rotation. Here a variable-coefficient rotational Kadomtsev–Petviashvili (KP) equation is devoted to investigate the interaction of initial X-shaped waves in presence of the respective one-dimensional (1D) and two-dimensional (2D) slope-shelf topography and rotations at different latitudes. Based on the analytical solutions, the long-time results can be classified as three types depending on the initial amplitudes and oblique angles. Then, numerical results suggest that the sufficiently shallow 1D shoaling topography can render polarity change, which reshapes the waveform of oblique interactions to resemble webs composed of straight wave crest lines. If the rotation were also taken into account, then the nonlinear interactions are inhibited resulting in less waves in the eventual long-time wave packets and more junction points in the webs of waveform. More importantly, the combined effect of rotation and localized small and narrow canyons and plateaus resting on 1D shoaling topography can significantly modulate the waveforms induced by oblique interactions to make them more like rank-ordered wavetrains.

Despite tremendous progress in algorithm development, computational efficiency and transition into operations over the past two decades, coastal modeling still lacks scientific rigor due to proliferation of many ‘gray’ areas related to various modeling choices made by modelers. In this paper, we propose some guiding principles for the modeling community to improve performance, and we also debunk commonly held myths that make the coastal modeling lack rigor. Using our own experience in developing seamless cross-scale unstructured-grid based models for the past two decades, we describe in unprecedented detail the end-to-end modeling process (i.e., from digital elevation models (DEMs) to mesh generation to post analysis), and demonstrate that defensible modeling is within reach for any end user by following three guiding principles: (1) Bathymetry is a first order forcing in coastal domains and thus should be respected in all aspects of modeling; (2) Oceanographic processes are driven across multiple spatial scales and so models should enable appropriate resolution as needed; and (3) Model assessment should focus on physical processes. Through qualitative and quantitative model assessments, we demonstrate the fundamental role played by bathymetry/topography as embedded in DEMs in making the results defensible, which is unfortunately glossed over in many modeling studies. Focusing on process-based assessment simplifies the calibration process. A major conclusion of this work is that model developers and operators should maximize the scientific rigor for in silico oceanography by avoiding some common pitfalls that rely on error compensation at the expense of representation of physical system processes. We present some best practice procedures for defensive and trustworthy numerical modeling.

The Lombok Strait shows active large-amplitude internal solitary waves (ISWs) and is also a primary gateway for the Indonesian Throughflow (ITF), which is a critical ocean current affecting the ocean ecosystem. This study collected 858 satellite images from 2018 to 2022, and ISW wave crests were extracted. Analysis shows that ISW 5-year cumulative occurrences reached the highest/ lowest of 136/28 days, with a 29 %/6 % frequency in October/ June. Satellite and reanalysis data revealed that ISW occurrence and propagation speed correlate to ITF variations. Enhanced southward ITF corresponds to less ISW occurrence and decreased/increased northward/southward ISW propagation speed. To understand how ITF modulates ISWs, three-dimensional MITgcm simulations were employed. Results show that when southward ITF decreases, ISWs tend to generate earlier/later, thus leading to a longer/shorter propagation distance for ISWs in the north/ south direction, revealing a suppressive effect on northward ISW generation.

Marine heatwaves (MHWs) are discrete yet persistent events of anomalously warm ocean temperatures which are becoming a hot topic in climate change research due to their extensive disruption of marine ecosystems worldwide. As a consequence, surface MHW events (SMHWs) and their drivers have been characterised worldwide typically under a consolidated common methodology. However, subsurface and bottom events of MHW (BMHWs) are less known due to the limited availability of data. Furthermore, recent advances suggest an improved MHW definition to distinguish the extreme event from the long-term ocean warming. Here, we use high-resolution GLORYS12V1 reanalysis data from 1993 to 2022 to characterise both SMHWs and BMHWs along the Spanish Marine Demarcation (SMD) areas defined within the Marine Strategy Framework Directive. We also broadly analyse their interconnections and ultimately generate a regional assessment of the integrated exposure of SMD to MHWs. We find that both SMHWs and BMHWs were more intense, longer-lasting, and widespread over the last 15 years. We also find that while SMHWs exhibit spatial variation following heat fluxes anomalies in the ocean surface layers, BMHWs roughly scale with ocean bottom depth and persist longer than their surface counterparts. Further, in shallower coastal regions where the mixed layer extends to the ocean bottom, average BMHW intensities can be comparable or even higher than those concurrently overlaying at the surface. Finally, we also demonstrate that both SMHWs and BMHWs are more likely to co-occur with high cumulative intensities in coastal SMD areas, with the 69% of their spatial extent categorised as highly exposed to MHWs. This highlights the imperative need for analysing and integrating SMHWs and BMHWs, especially in coastal zones, when assessing and addressing present and future impacts on wildlife and economies under the expected climate change scenario.

Swell waves, characterized by the long wavelength components generated by distant weather systems or storms, exert a significant influence on various air–sea interaction processes, thereby impacting weather and climate systems. Over recent decades, substantial progress has been achieved in comprehending the dynamics of swell waves and their implications for air–sea interactions. This paper presents a comprehensive review of advancements and key findings concerning surface swell waves and their interactions with the atmosphere. It encompasses a range of topics, including wave growth theory, the effects of swell waves on air–sea momentum, heat, and mass fluxes, as well as their influence on atmospheric turbulence and mixed layer processes. The most important characteristics of the swell impact (where it differs from wind sea conditions) are the wave-induced upward component of the surface stress leading to alteration of total surface stress, generation of a low-level wind maxima or changed wind profile and change of scale and behaviour of turbulence properties (turbulence kinetic energy and integral length scale). Furthermore, the paper explores the modelling of swell dissipation, the integration of swell influences in weather and climate models, and the broader climatic implications of surface swell waves. Despite notable advances in understanding swell processes, persistent knowledge gaps remain, underscoring the need for further research efforts, which are outlined in the paper.