This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales (MPAS)-ocean/sea ice model within energy exascale earth system model (E3SM) version 2.1 (E3SMv2-MPAS) at 60 km to 10 km resolution. Multi-source observational data are utilized to validate sea surface temperature/salinity, sea ice, three-dimensional thermal-saline structures, mixed layer depth, ocean heat content, and sea surface height. Key results show the following: (1) E3SMv2-MPAS captures seasonal-to-decadal variability in surface fields and sea ice, but shows systematic biases in sea surface temperature of western boundary currents (inadequate eddy parameterization) and Arctic sea surface salinity (misrepresented freshwater fluxes and mixing processes). (2) The model robustly represents three-dimensional climate variability, yet underestimates mixed layer depth in key regions (Antarctic Circumpolar Current and North Atlantic), revealing deficiencies in extreme mixing. (3) Ocean heat content distributions are well-simulated. (4) Sea surface height spatial patterns and interannual variability are accurately reproduced. This work identifies critical refinements for unstructured-mesh models: mesoscale eddy parameterization, polar ocean-sea ice coupling, and multi-scale energy processes, advancing high-resolution climate model development and laying the groundwork for improved ocean forecasting systems.

In the 21st century, global climate change and geopolitical risks are interconnected, presenting new challenges and influencing the trade dynamics between China and Europe. This paper investigates the effects of climate change and geopolitical risks on the trade route between China and Europe using bibliometric analysis and review. It seeks to identify potential trends in this trade path for the 21st century. Research indicates that, influenced by climate change and geopolitical factors, trade routes between China and Europe are transitioning from traditional single trade channels to multiple trade channels, such as the joint Arctic Northeast Passage and the China-Europe Railway Express. This process generates opportunities, including enhanced efficiency and stability in Sino-Europe trade, due to the development of new trade channels. However, it introduces difficulties, including unpredictability in climate change forecasts and weaknesses arising from geopolitical tensions. This compels China and Europe to take proactive measures. The melting of Arctic sea ice presents new commercial opportunities related to climate change. Consequently, the observed variations in navigable durations and ecological sensitivity have led China and Europe to implement proactive adaptation strategies. This includes collaborative ice monitoring networks and standardized green ship technology, designed to mitigate the effects of environmental risks on supply chains. Geopolitical factors, including trade frictions and regional conflicts, have led to heightened insurance costs and disruptions at critical nodes. This scenario has caused a structural transformation within the trade system across three key areas: trade demand, logistics architecture, and trade distribution. In response, China and Europe have created strategic backup channels and enhanced multilateral mechanisms along with regional cooperation. This paper highlights the necessity of quantifying the mechanisms by which climate change and geopolitical risks influence the trajectory of China-EU trade, aiming to address blind spots in decision-making throughout the transition process. This framework facilitates the prediction of future trends in China-EU trade, considering the influence of these dual drivers. The goal is to address uncertainty, describe the trade pattern’s response, and assess and optimize the response measures of Sino-EU countries, ultimately fostering the sustainable development of Sino-EU trade.

Internal solitary waves (ISWs) are commonly observed in stratified oceans and may cause sudden and dangerous depth drops for underwater vehicles. Although the hydrodynamic responses of vehicles encountering ISWs have been extensively investigated, effective control strategies for mitigating ISW-induced disturbances remain insufficient. In this study, a numerical wave tank based on the extended Korteweg-de Vries (eKdV) equation is developed within the STAR-CCM + framework to reproduce ISW propagation in a density stratified environment. The Joubert BB2 submarine is selected as the test model, and fully coupled simulations incorporating hull motion, propeller rotation, and stern rudder deflection are performed using overset mesh and sliding mesh techniques. A proportional-derivative (PD) controller is employed to regulate the stern rudder angle for depth control. The results indicate that when the submarine operates below the density interface, the PD controller provides limited suppression of depth drop because ISW-induced adverse horizontal velocity reduces forward speed and rudder effectiveness. To address this issue, a proportional-integral (PI) controller is further introduced to adjust the propeller rotational speed in real time, thereby compensating for thrust loss and maintaining the desired cruising speed. Under the combined PD-PI control strategy, the maximum depth drop is reduced to 29% of that in the uncontrolled case. Additional simulations for submarines located at and above the density interface further confirm the effectiveness of the proposed control approach. The present study provides practical guidance for the design of control systems for underwater vehicles operating in ISW affected environments.

The Arctic climate has exhibited a warming and wetting trend in recent decades. Based on Arctic temperature and precipitation data (1940-2023), this study employed climate classification methods to examine Arctic climate states and their variations. Since traditional rule-based classification schemes inadequately represent the climate over the Arctic Ocean, we employed a data-driven k-means approach. This method objectively identified four distinct Arctic climate types: cold-dry, semi-cold-dry, semi-warm-wet, and warm-wet. Spatiotemporal analysis reveals strong contrasts between polar day and night. During the polar day, the semi-cold-dry climate shrank progressively over the high-latitude waters of the Arctic Ocean. In contrast, during the polar night, this climate type expanded and became dominant across the Eurasian Sea, from the East Siberian Sea to the Kara Sea. Transitions among these Arctic climate types were accompanied by the strengthened synchronization between temperature and precipitation. Attribution analyses indicate that polar-day transitions were driven primarily by local warming feedback and secondarily by external forcing. Conversely, polar-night changes were dominated by external forcing. The Coupled Model Intercomparison Project phase 6 (CMIP6) projections show that under high-emission scenarios, the semi-warm-wet type is projected to become the dominant climate over the Arctic Ocean by the late twenty-first century. This study examines Arctic climate classification and evolutionary patterns, contributing to the understanding and prediction of global climate change.

The Arctic, which is highly sensitive to climate change, is experiencing unprecedented transformations. Cyclones, as extreme weather events, significantly impact sea ice through dynamic and thermodynamic forcings. This study quantifies the relative roles of dynamic, atmospheric thermodynamic, and oceanic thermodynamic forcings on sea ice changes from Arctic cold-season cyclones (1993–2020) throughout distinct phases of cyclone passage (4 days prior to the day of, 1–4 days after, and 5–7 days after cyclone passage) in the northern Barents Sea and the southwestern Kara Sea. It is found that sea ice concentration decreases significantly before cyclone passage and gradually increases thereafter. Across all phases, dynamic forcing dominates the sea ice response, while atmospheric thermodynamic forcing plays a secondary role. Oceanic thermodynamic forcing contributes minimally and is primarily active in regions with pronounced Atlantic Water influence. Notably, we identify a new oceanic thermodynamic mechanism: cyclones reduce the distance between mixed layer depth and warm-layer upper boundary (WL-MLD), enhancing upward ocean heat flux and then influencing sea ice. In the northern Barents Sea, dynamic forcing reduces sea ice effective thickness by 0.57 cm/day before cyclone passage and increases it by 0.68 cm/day after it passes, which, like all following values, is defined as the difference between the “cyclone’’ and “non-cyclone’’ scenarios. Before cyclone passage, atmospheric thermodynamic forcing suppresses thickness increase by 0.49 cm/day, while oceanic thermodynamic forcing mitigates thickness loss by 0.17 cm/day. In the southwestern Kara Sea, dynamic forcing leads to a reduction of 0.79 cm/day before cyclone passage, followed by an increase of 0.42 cm/day after passage.

Accelerated Arctic sea-ice melt is profoundly reshaping global shipping patterns and trade networks, with particularly significant implications for the China–Europe maritime corridor. To assess the resilience of China–EU maritime trade under these evolving conditions, this study develops a dual-dimensional scenario analysis framework that integrates both climate-change and geopolitical risks. We introduce an innovative coupled GTAP-P_D (Global Trade Analysis Project- Equivalent Probability of Delivery Cost) model to simulate the synergistic effects of the Northeast Passage (NEP) and the traditional Suez Canal on bilateral trade flows. Multi-scenario simulations yield three key findings: (1) As Arctic sea ice continues to retreat, the NEP provides substantial strategic backup value during Suez Canal disruptions arising from geopolitical conflict, thereby stabilizing trade flows and enhancing system resilience; (2) Realizing dual-channel synergies depends strongly on aligning cargo time-sensitivity elasticity with route characteristics, with highly time-sensitive goods benefiting the most from improved Arctic navigability; and (3) Distinct tipping-point effects emerge across climate pathways. For example, under the SSP5-8.5 scenario, the NEP could become the primary navigation route in the presence of high-intensity geopolitical risks by approximately 2060. Overall, this study offers quantitative evidence to support sustainable Arctic-route development and the resilient advancement of China–Europe maritime trade, while also providing a generalizable framework for evaluating compound climate–geopolitical risks.

Freak waves, which are characterized by their large wave heights and significant energy, can severely damage marine structures. The evolution of these freak waves is nonlinear, making it difficult to describe them with basic wave parameters. This paper analyzes the statistical characteristics of freak waves during the evolution. The freak waves are generated in a physical wave tank based on the two-wave train superposition method. An advanced coupled High-Order Spectral methods (HOS) and viscous Computational Fluid Dynamics (CFD) method is proposed to generate high quality freak waves. As the results of numerical methods agree well with the experimental results, the correlation between the statistical characteristics and freak wave mechanism is provided. The results reveal that the kurtosis of freak waves is related to the maximum wave crest, while the skewness is both influenced by the maximum wave height and the stream velocity at the wave crest. The wave propagation mechanisms are analyzed by Empirical Mode Decomposition (EMD). The results show that the location with the largest energy in low-frequency region sometimes occurs after the peak wave height, which may cause drift motion of floating structures and lead to hazards.

Sea surface salinity (SSS) is a crucial variable in understanding ocean dynamics, climate change, and marine ecosystems. However, purely data-driven deep learning models often lack integration with physical laws, which limits their interpretability and prediction accuracy. To address this issue, this study develops a physically informed deep learning framework that incorporates spatiotemporal patterns from Empirical Orthogonal Function (EOF) analysis of SSS into deep learning architectures. Two distinct methods are proposed: one integrates EOF-based constraints into the loss function (LEOF), and the other employs a Siamese network branch to explicitly embed physical constraints within the model (SEOF). The models utilize multi-source satellite data and various factors (such as sea surface temperature) to produce daily SSS prediction. The results demonstrate that the inclusion of EOF constraints significantly improves predictive accuracy, reducing Root Mean Square Error and Mean Absolute Error by up to 25.51% and 53.63%, respectively. The SEOF-RAUnet model, which combines EOF-based constraints with an attention mechanism, achieves the best performance, particularly in high-variability regions such as the eastern Pacific under the influence of the Equatorial Countercurrent. The proposed framework establishes a basis for forthcoming research focused on the integration of physical constraints with AI-driven SSS prediction, thereby enhancing applications in marine environment monitoring and prediction.

In recent decades, the ocean's influence on Arctic sea ice has become increasingly pronounced. The surface mixed layer (SML) serves as a critical interface between the underlying ocean and sea ice, mediating heat transfer between them. However, the links between the SML and seasonal sea ice changes, as well as the dominant mechanisms governing this interaction, remain insufficiently understood. This study uses in situ sea-ice drift data and the Liang-Kleeman information flow to quantify the causal relationship between the SML and sea ice thickness on the Eurasian Basin side, revealing a clear two-way cause-effect relationship. Furthermore, the links between seven heat sources in the SML and seasonal sea-ice melt are also quantified. Vertical turbulence emerges as the primary contributor to heat transfer in the SML near Fram Strait and north of Svalbard, where the halocline is weak and Atlantic Water (AW) remains relatively warm. When the SML heat content is high, horizontal advection dominates, while vertical advection becomes more important only when AW is ventilated upward. In contrast, in the Nansen and Amundsen basins, where the influence of AW heat is weaker, the relationship between the SML and seasonal sea-ice melt is primarily driven by sea-ice melting, with the SML itself being influenced mainly by vertical turbulence and ice-ocean heat forcing.

This study employs large eddy simulation and the Boussinesq approximation to investigate the characteristics of wakes generated by prolate spheroid with different aspect ratios (length-to-diameter ratios, L/D = 1.0, 1.5, 2.0, 3.0) in a linear stratified flow, with the Reynolds number (Re) of 3700 and the Froude number (Fr) of 3. The research primarily focuses on the effects of different aspect ratios on defect velocity, wake length scales, root mean square values of velocity components, power spectra, wake energy, and turbulent kinetic energy (TKE). The findings show that the defect velocity along the wake centerline follows the relationships: u similar to (x/D)(-0.08) (L/D = 1.0), as the aspect ratio increases, the exponent gradually decreases, indicating a shorter mean lifespan of the wake. After the starting position of the accelerated collapse stage, the half-width, half-height, the ratio of half-height to half-width, and the influence area of the wake oscillate periodically. With the aspect ratio increases, the half-width, half-height, the ratio of half-height to half-width, and the influence area of the wake are gradually becoming smaller, and the Reynolds stress gradually decreases in magnitude and becomes concentrated near the centerline of the wake. The turbulent kinetic energy for different aspect ratios follows the relationship TKE similar to (x/D)(-1.19). The mean kinetic energy, turbulent kinetic energy, and turbulent potential energy (TPE) of the wake all decrease with increasing aspect ratio. Both the energy of wake and transport, advection, and buoyancy terms in turbulent kinetic energy budget exhibit periodic oscillations, with the oscillation wavelength corresponding to half a buoyancy period (piFr). All terms in turbulent kinetic energy budget decrease with the aspect ratio increases.

To enhance understanding of the flow characteristics around a sphere in both stratified and unstratified (UNS) fluids, large eddy simulations (LES) were conducted using a temperature-dependent density model at Re = 3 700. The simulations were performed for flow around a sphere under UNS and stratified conditions (Fr = 3). Horizontal and vertical vorticity, velocity, and streamline distributions were compared, and the evolution of vortex structures in the wake was analyzed. Furthermore, we quantified the velocity deficit, the root mean square (rms) of velocity components in all directions, and the turbulent kinetic energy (TKE) distribution. Additionally, the horizontal and vertical wake lengths were examined. The results demonstrate that the employed numerical simulation method accurately captures the behavior of stratified fluids, with outcomes in close agreement with experimental and numerical findings from previous studies. In the case of homogeneous fluid, a lower density value results in a faster decay of the velocity deficit. In stratified fluids, the vortex structures in the wake evolve through three distinct stages: 3-D, non-equilibrium (NEQ), quasi-two-dimensional (Q2D). For x / D > 2, the rms velocity in the vertical direction exceeds that in the other two directions. In UNS fluid, the TKE distribution forms a vertically elongated spindle shape, while in stratified fluid, it assumes an elliptical shape, being vertically compressed and horizontally expanded. The vertical extent of the density and density gradient distributions surpasses that of the wake.

Reliable prediction of short-term Arctic sea ice variation is crucial for ensuring the safety of navigation on Arctic shipping routes. While deep-learning models have demonstrated potential in improving the accuracy of sea ice predictions, many data-driven approaches focus solely on individual aspects of sea ice without considering the interrelationships and underlying physical laws governing various sea ice factors. To address this limitation, we introduce a dual-task prediction model that simultaneously targets sea ice concentration (SIC) and sea ice motion (SIM). Our approach incorporates a novel loss function that enforces dynamic constraints derived from the sea ice control equation, ensuring that predictions of both SIC and SIM are consistent with physical dynamics. We conduct comprehensive comparative experiments to identify the optimal model structure for predicting SIC and SIM. Our findings reveal that a dual-task branching architecture is particularly effective for this purpose, with a post-decoder branch network structure exhibiting the best performance in predicting both SIC and SIM. By integrating the sea ice dynamics equation into the loss function, our models demonstrate enhanced alignment with physical laws, leading to improved predictability and accuracy in SIC and SIM prediction.

During the movement of underwater vehicles, hydrodynamic wakes are formed along with long-distance thermal wakes caused by the release of thermal wastewater. By capturing and imaging the thermal wake generated behind the underwater vehicle in real time, it is possible to determine the size, speed, and location of the vehicle. Investigating the hydrodynamic wake and thermal wake of underwater vehicle requires the integration of various physical fields, including fluid mechanics and temperature. Current research is centered on analyzing the impact of factors like the underwater vehicle's speed, temperature and flow rate of thermal wastewater on the wake. However, there is a lack of comprehensive studies in this area, as well as limited attention given to the effects of attachments and propellers on thermal wake. This study conducted numerical simulations using the RANS method to analyze the hydrodynamic and thermal wake of an underwater vehicle with a fully attached body. The research considered various speeds, propeller speeds, temperatures and flow rates of thermal wastewater. The results revealed that the propeller significantly influences the core region of the thermal wake. The temperature decays rapidly when closer to the vehicle and slower at greater distances. Moreover, higher speeds and propeller rotation speeds lead to a larger affected area by temperature. Additionally, increasing the temperature and flow rate of thermal wastewater strengthens the thermodynamic signal in the wake behind the underwater vehicle.

This paper uses the theory of self-affine fractal functions to model the dynamic flight graphs of starling flocks, integrating the fractional calculus of self-affine fractal functions to quantitatively characterize the intrinsic nonlinear dynamics and memory effects within the system, employing statistical inference methods to find the fractal fit for the images. The changes in box dimensions over time could characterize the phase transition process of the starling flight flocks. By analyzing the rate of change of fractal dimensions, we identify critical points corresponding to phase transitions during collective flight behavior. During the flight of the starling flocks, a real-time phase transition process for evading attacks and effective advancement has been identified. Experimental data confirms the effectiveness of controlling the phase transition.

Advancing high-resolution Arctic ocean–sea ice modeling is critical for understanding polar amplification and improving climate projections but faces challenges from computational limits and cross-scale interactions. The simulation capabilities of the ocean–sea ice coupled model (E3SMv2-MPAS) from the Energy Exascale Earth System Model (E3SM) 2.1 for the Arctic ocean–sea ice system are systematically evaluated using multi-source observational data. The model employs a latitudinally varying mesh, with resolution increasing from 60 km in the Southern Hemisphere to 10 km in the Arctic. This design balances computational efficiency with the accurate integration of low-latitude oceanic influences, while the unstructured mesh also enhances the geometric representation of Arctic straits. Together, these features form a simulation framework capable of resolving processes from seasonal to decadal timescales. Numerical results demonstrate E3SMv2-MPAS's superior Arctic simulation performance: (1) accurate reproduction of spatial heterogeneity in sea ice concentration, thickness, and sea surface temperature, including their 1995–2020 trend patterns; (2) faithful reproduction of both the freshwater content and transports through key Arctic gateways; (3) successful reconstruction of three-dimensional thermohaline structures within the Atlantic Water layer, capturing Atlantic Water's decadal warming trends and accelerated Atlantification processes – specifically mid-layer shoaling, heat content amplification, and reduced heat transfer lag times in the Eurasian Basin. Persistent systematic biases are identified: 0.5–1 m sea ice thickness overestimation in the Canadian Basin; Coordinated sea surface temperature/salinity underestimation and sea ice concentration overestimation in the Greenland and Barents Seas; Atlantic Water core temperature overestimation; Regional asymmetries in decadal thermohaline field evolution.

To address the challenge of obtaining ocean mesoscale eddies' refined three-dimensional (3D) structure, we propose a novel 3D structure reconstruction model that combines physical process models with data-driven machine learning. First, based on the universal structure of mesoscale eddies, the 3D density structure of eddies is reconstructed using satellite observations and individual Argo profile observations. These eddy density profiles, along with eddy elements (polarity, eddy center, and radius) and sea surface elements (temperature, salinity, and dynamic height), serve as input data to construct a data-driven machine learning algorithm, which can reconstruct the 3D temperature and salinity structure of the eddies. Using observations of oceanic mesoscale cyclonic and anticyclonic eddies in the northwest Pacific Ocean, we demonstrate that both types of eddies' reconstructed temperature, salinity, and density structures align well with the observations. The root mean square errors (RMSEs) for the anticyclonic eddy are 0.361 degrees C, 0.0271 PSU, and 0.0570 kg/m(3), and for the cyclonic eddy, they are 0.372 degrees C, 0.0904 PSU, and 0.144 kg/m(3), respectively. The correlation coefficients exceed 0.98. Compared to multi-source fusion data (ARMOR 3D) and dynamical statistics data (MODAS), the reconstructed 3D structure from this study shows the closest alignment with observed structures. Furthermore, incorporating physical process model inputs significantly enhances the accuracy of the data-driven machine learning reconstruction of the eddy thermohaline structure, reducing the RMSEs by >40 % and 60 %, respectively.

The “twenty-first-century Maritime Silk Road” is of great significance to the construction of China’s marine environmental security. This paper focuses on the core policy of the Silk Road—the “connectivity.” In order to cluster the countries along the line based on the connectivity, this paper introduces the cloud model theory, puts forward the new concept “semantic clouds distance” and its calculation equation named location- and shape-based cloud distance measurement (LSCM), and then constructs the cluster model. Finally, the results are tested by similarity between the Association of Southeast Asian Nation (ASEAN) countries and the clustering center. The method proposed in this paper can be effectively applied to cluster analysis and research of human problems such as connectivity and provide research ideas for similar problems.

With the accelerated melting of Arctic sea ice in the context of climate change, accurate sea ice thickness observations are needed for long-term climate simulation, short-term prediction, and shipping navigation. Limited by their special geographical location, in-situ data of Arctic sea ice thickness is scarce, and the quality of observation products from existing satellites and models is uneven. In this situation, we selected the key meteorological and marine environmental factors that affect the thickness of sea ice based on the thermodynamic law of sea ice, the traditional method of causal analysis, and the data mining ability of machine learning. Then, we constructed bias correction models combining these factors with geographic and temporal information, integrating random forest, extreme gradient boosting tree, and generalised regression neural network to correct the sea ice thickness data. The segmented regression models of thick and thin ice were constructed according to the characteristics of sea ice thickness products. The results show that the proposed bias correction model of sea ice thickness can effectively improve the quality of existing products and reduce the bias from the in-situ data.

Accurate prediction of short-term fluctuations in Arctic sea ice is important for safe use of Arctic shipping routes. While deep-learning models can improve the accuracy of sea-ice predictions, the accuracy and predictability of short-term sea-ice conditions are limited in most purely data-driven deep-learning models that consider a single aspect of sea ice, without considering the physical variation law between several sea ice factors. We propose dual-task prediction models for sea ice concentration (SIC) and sea ice velocity (SIV), and incorporate a loss function that simultaneously addresses dynamic constraints of SIC and SIV into each model, based on dynamics terms in the sea ice control equation. Comparative experiments are performed to determine which model structure best performs at predicting SIC and SIV. We report a dual-task branching structure to be more suitable for predicting SIC and SIV, and a post-decoder branch network structure to best predict SIC and SIV. Adding a sea ice dynamics equation to the loss function improves the model fit with dynamics law, improving the predictability and prediction accuracy of SIC and SIV.

Grasping the rapidly changing sea ice patterns has become the key to Arctic navigation. Starting from meteorology and oceanography, this study combines the empirical orthogonal function (EOF) analysis with convolutional long short-term memory (ConvLSTM) model to realize short-term sea ice prediction. To further improve the effectiveness and predictability of the ConvLSTM model, we adopt the EOF analysis to incorporate the spatial and temporal patterns of sea ice changes into the ConvLSTM model in three ways, namely, model input factors, model internal network structure, and model loss function. Through the mean absolute error (MAE), anomaly correlation coefficient (ACC), and anomaly metrics, we find that embedding the EOF physical constraints into the model's internal structure fully employs the spatial pattern and time series information, affording an optimal prediction effect. Additionally, the impact of the weight proportion between the physical constraint and the base loss and the effect of combining the three ways are discussed. The experimental results reveal that tuning the weight proportion in the loss function can improve the model's training effect. However, multiple combinations of the same physical law make it difficult to introduce new effective information.