Search Results (28)

Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea ice growth using various parameters, a combination of up to 13 radar and physical parameters including surface-based C-band NRCS values in VV, HH, and HV polarizations, air temperature, surface temperature, Cumulative Freezing Degree Moments, humidity, wind speed, surface cover salinity, ice surface salinity, bulk ice salinity, frost flower height and snow depth were input to the four multivariate models in two time series datasets. The results showed that Random Forest was the superior model, with =0.01 cm, for thicknesses of 1–8 cm and 27–47 cm. Using the Permutation Importance method, the role of the employed parameters in the thickness prediction process were ranked and showed that the key parameters were Cumulative Freezing Degree Moment, salinity parameters (surface cover, ice surface, and bulk ice salinities), and C-band co-polarized radar backscattering. The results of this study enhance thickness prediction capacity and accuracy, while providing insights for future research and real-time sea ice thickness prediction in Arctic regions. Full article

Ice accretion from marine icing events accumulating on structures poses a significant hazard to ship and offshore operations in cold regions, being relevant for offshore activities like oil explorations, offshore wind, and shipping in arctic regions. The adhesion strength of such ice is a critical factor in predicting the build-up of ice loads on structures. While the adhesion strength of freshwater ice has been extensively studied, knowledge about sea spray ice adhesion remains limited. This study intends to bridge this gap by investigating the adhesion strength of sea spray icing under controlled laboratory conditions. In this study, we built a new in situ ice adhesion test setup and grew ice at −7 °C to −15 °C on quadratic aluminium samples of 3 cm to 12 cm edge length. The results reveal that sea spray ice adhesion strength is in a significantly lower range—5 kPa to 100 kPa—compared to fresh water ice adhesion and shows a low dependency on the temperature during the spray event, but a notable size effect and influence of the brine layer thickness on the adhesion strength. These findings provide critical insights into sea spray icing, enhancing the ability to predict and manage ice loads in marine environments. Full article

Underwater explosion ice-breaking technology is critical for Arctic development and ice disaster prevention due to its high efficiency, yet it faces challenges in understanding the coupled dynamics of shock waves, pulsating bubbles, and heterogeneous ice fracture. This review synthesizes theoretical models, experimental studies, and numerical simulations investigating damage mechanisms. Key findings establish that shock waves initiate brittle fracture via stress superposition while bubble pulsation drives crack propagation through pressure oscillation; optimal ice fragmentation depends critically on charge weight, standoff distance, and ice thickness. However, significant limitations persist in modeling sea ice heterogeneity, experimental replication of polar conditions, and computational efficiency. Future advancements require multiscale fluid–structure interaction models integrating brine migration effects, enhanced experimental diagnostics for transient processes, and optimized numerical algorithms to enable reliable predictions for engineering applications. Full article

The Upper Carboniferous strata in the eastern Qaidam Basin, comprising several hundred meters of thick, mixed clastic–carbonate successions that have been little reported or explained, provide an excellent geological record of paleoenvironmental and paleo-sea level changes during the Late Carboniferous icehouse period. This tropical carbonate–clastic system offers critical constraints for correlating equatorial sea level responses with high-latitude glacial cycles during the Late Paleozoic Ice Age. Based on detailed outcrop observations and interpretations, five facies assemblages, including fluvial channel, tide-dominated estuary, wave-dominated shoreface, tide-influenced delta, and carbonate-dominated marine, have been identified and organized into cyclical stacking patterns. Correspondingly, four third-order sequences were recognized, each composed of lowstand, transgressive, and highstand system tracts (LST, TST, and HST). LST is generally dominated by fluvial channels as a result of river juvenation when the sea level falls. The TST is characterized by tide-dominated estuaries, followed by retrogradational, carbonated-dominated marine deposits formed during a period of sea level rise. The HST is dominated by aggradational marine deposits, wave-dominated shoreface environments, or tide-influenced deltas, caused by subsequent sea level falls and increased debris supply. The sequence stratigraphic evolution and geochemical records, based on carbon and oxygen isotopes and trace elements, suggest that during the Late Carboniferous period, the eastern Qaidam Basin experienced at least four significant sea level fluctuation events, and an overall long-term sea level rise. These were primarily driven by the Gondwana glacio-eustasy and regionally ascribed to the Paleo-Tethys Ocean expansion induced by the late Hercynian movement. Assessing the history of glacio-eustasy-driven sea level changes in the eastern Qaidam Basin is useful for predicting the distribution and evolution of mixed cyclic succession in and around the Tibetan Plateau. Full article

Antarctic navigation encounters substantial challenges due to the dynamic and perilous characteristics of sea ice, which pose threats to vessel safety and operational efficiency. Existing risk assessment methodologies frequently lack real-time adaptability, while strategies for icebreaker convoys remain insufficiently quantified. To address these deficiencies, this study introduces an integrated framework that combines satellite-based sea ice monitoring, operational risk prediction, and icebreaker escort optimization. First, polar research routes and hydrographic conditions are systematically analyzed to enhance navigation planning. Second, a risk assessment system is developed by leveraging satellite-derived sea ice density and thickness data, facilitating a near-real-time hazard assessment (subject to satellite data latency) evaluation with 96.3% accuracy in ice type classification and a 15% improvement in risk prediction precision compared to conventional methods. Finally, kinematic safety criteria for icebreaker-escorted convoys are established, specifying speed-dependent distance thresholds to minimize collision risks, achieving optimal speeds of 1.4–2.3 knots for PC3-class vessels and 10–20% speed improvements for escorted vessels in cleared channels. The findings offer actionable insights into polar route optimization, risk mitigation, and safe ice navigation protocols, thereby directly supporting operational decision making in Antarctic waters. Full article

Statistical optimization methods serve as fundamental tools for studying sea-ice-related phenomena in the polar regions. To comprehensively analyze the spatial distributions of ice ridge keels, including the draft and spacing distributions, a statistical optimization model was developed with the aim of determining the optimal keel cutoff draft, which differentiates ice ridge keels from sea ice bottom roughness. By treating the keel cutoff draft as the identified variable and minimizing the relative errors between the theoretical and measured keel spatial distributions, the developed model aimed to seek the optimal keel cutoff draft and provide a precise method for this differentiation and to explore the impact of the ridging intensity, defined as the ratio of the mean ridge sail height to spacing, on the spatial distributions of the ice ridge keels. The utilized data were obtained from observations of sea ice bottom undulations in the Northwestern Weddell Sea during the winter of 2006; these observations were conducted using helicopter-borne electromagnetic induction (EM-bird). Through rigorous analysis, the optimal keel cutoff draft was determined to be 3.8 m, and this value was subsequently employed to effectively differentiate ridge keels from other roughness features on the sea ice bottom. Then, building upon our previous research that clustered measured profiles into three distinct regimes (Region 1, Region 2, and Region 3, respectively), a detailed statistical analysis was carried out to evaluate the influence of the ridging intensity on the spatial distributions of the ice ridge keels for all three regimes. Notably, the results closely matched the predictions of the statistical optimization model: Wadhams’80 function (a negative exponential function) exhibited an excellent fit with the measured distributions of the keel draft, and a lognormal function proved to effectively describe the keel spacing distributions in all three regimes. Furthermore, it was discovered that the relationship between the mean ridge keel draft and frequency (number of keels per kilometer) could be accurately modeled by a logarithmic function with a correlation coefficient of 0.698, despite considerable data scatter. This study yields several significant results with far-reaching implications. The determination of the optimal keel cutoff draft and the successful modeling of the relationship between the keel draft and frequency represent key achievements. These findings provide a solid theoretical foundation for analyzing the correlations between the morphologies of the sea ice surface and bottom. Such theoretical insights are crucial for improving remote sensing algorithms for ice thickness inversion from satellite elevation data, enhancing the accuracy of sea ice thickness estimations. Full article

The thickness of the Arctic sea ice constitutes one of the crucial indicators of global climate change, and while deep learning has shown promise in predicting sea ice thickness (SIT), the field continues to grapple with the challenge of limited data availability. In this study, we introduce a Wasserstein Generative Adversarial Network–Long Short-Term Memory (WGAN-LSTM) model, which leverages the data generation capabilities of WGAN and the temporal prediction strengths of LSTM to perform single-step SIT prediction. During model training, the mean square error (MSE) and a novel comprehensive index, the Distance between Indices of Simulation and Observation (DISO), are used as two metrics of the loss function to compare. To thoroughly assess the model’s performance, we integrate the WGAN-LSTM model with the Monte Carlo (MC) dropout uncertainty estimation method, thereby validating the model’s enhanced generalization capabilities. Experimental results demonstrate that the WGAN-LSTM model, utilizing MSE and DISO as loss functions, improves comprehensive performance by 51.9% and 75.2%, respectively, compared to the traditional LSTM model. Furthermore, the MC estimates of the WGAN-LSTM model align with the distribution of actual observations. These findings indicate that the WGAN-LSTM model effectively captures nonlinear changes and surpasses the traditional LSTM model in prediction accuracy. The demonstrated effectiveness and reliability of the WGAN-LSTM model significantly advance short-term SIT prediction research in the Arctic region, particularly under conditions of data scarcity. Additionally, this model offers an innovative approach for identifying other physical features in the sea ice field based on sparse data. Full article

Revolutionary advances in artificial intelligence (AI) in the past decade have brought transformative innovation across science and engineering disciplines. In the field of Arctic science, we have witnessed an increasing trend in the adoption of AI, especially deep learning, to support the analysis of Arctic big data and facilitate new discoveries. In this paper, we provide a comprehensive review of the applications of deep learning in sea ice remote sensing domains, focusing on problems such as sea ice lead detection, thickness estimation, sea ice concentration and extent forecasting, motion detection, and sea ice type classification. In addition to discussing these applications, we also summarize technological advances that provide customized deep learning solutions, including new loss functions and learning strategies to better understand sea ice dynamics. To promote the growth of this exciting interdisciplinary field, we further explore several research areas where the Arctic sea ice community can benefit from cutting-edge AI technology. These areas include improving multimodal deep learning capabilities, enhancing model accuracy in measuring prediction uncertainty, better leveraging AI foundation models, and deepening integration with physics-based models. We hope that this paper can serve as a cornerstone in the progress of Arctic sea ice research using AI and inspire further advances in this field. Full article

Sea ice, as an important component of the Earth’s ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model’s generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data. Full article

Sea ice concentration and thickness are key parameters for Arctic shipping routes and navigable potential. This study focuses on the changes in shipping routes and the estimation of navigable potential in the Arctic Northern Sea Route and Transpolar Sea Route during 2021–2050 based on the sea ice data predicted by eight CMIP6 models. The Arctic sea ice concentration and thickness vary among the eight models, but all indicate a declining trend. This study indicates that, under the two scenarios, the least-cost route will migrate more rapidly from the low-latitude route to the high-latitude route in the next 30 years, showing that the Transpolar Sea Route will be navigable for Open Water (OW) and Polar Class 6 (PC6) before 2025, which is advanced by nearly 10 years compared to previous studies. The sailing time will decrease to 16 and 13 days for OW and PC6 by 2050, which saves 3 days compared to previous studies. For OW, the navigable season is mainly from August to October, and the Northern Sea Route is still the main route, while for PC6, the navigable season is mainly from July to January of the following year, and the Transpolar Sea Route will become one of the important choices. Full article

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance. Full article

With the shrinking of Arctic sea ice due to global climate change, potential access to Arctic waters has increased for non-typical icebreaking or strengthened ships. Numerous studies have been conducted on hull form designs and ice resistance predictions for ships with typical icebreaking bows, but published research for ships with non-icebreaking bows in ice is still rare. The objective of this study was to investigate the ice resistance of a naval surface ship with a non-icebreaking bow through model tests in an ice tank. The naval surface combatant concept DTMB 5415 was used as the ship model. The tests were conducted under different levels of ice thicknesses and speeds. During the tests, the total resistance of the model ship was measured, accompanied by monitoring of the ice load at the stem area with a flexible tactile sensor sheet. Compared with the test results of icebreaker models in former studies, the total ice resistance, as well as the stem ice load, of the present ship was significantly higher. The ice crushing resistance component in the stem area accounted for more than 60% of the total resistance in the ice. Discussions on the applicability of a semi-empirical formula for predicting the ice resistance of the present ship are also presented. Keinonen’s formula was found to be relatively more consistent with the predictions produced by model tests, and a preliminary modification was proposed to obtain more accurate predictions. Full article

The decline of sea ice in the Arctic region is a critical indicator of rapid global warming and can also influence the feedback processes in the Arctic, so the prediction of sea ice extent and thickness plays an important role in climate modeling and prediction. This paper uses machine learning methods to predict the sea ice extent, and by adjusting the methods and factors, which include the climate variables, the past sea ice extent, and the simple linear-regression-simulated sea ice extent, then we found the best combination to give the result with the highest R² score. We noticed that with longer periods of past sea ice extent data and shorter periods of climate data, the results appeared to be better. This might be related to the difference in climate and ocean memory. The sub-region sea ice extent prediction shows that the regions with whole-year ice cover are easier to predict and that those regions with sudden weather changes and significant seasonal variability appear to have lower R² scores in the sea ice extent prediction. Full article

Thermodynamic parameters play a crucial role in determining polar sea ice thickness (SIT); however, modeling their relationship is difficult due to the complexity of the influencing mechanisms. In this study, we propose a self-attention convolutional neural network (SAC-Net), which aims to model the relationship between thermodynamic parameters and SIT more parsimoniously, allowing us to estimate SIT directly from these parameters. SAC-Net uses a fully convolutional network as a baseline model to detect the spatial information of the thermodynamic parameters. Furthermore, a self-attention block is introduced to enhance the correlation among features. SAC-Net was trained on a dataset of SIT observations and thermodynamic data from the 2012–2019 freeze-up period, including surface upward sensible heat flux, surface upward latent heat flux, 2 m temperature, skin temperature, and surface snow temperature. The results show that our neural network model outperforms two thermodynamic-based SIT products in terms of accuracy and can provide reliable estimates of SIT. This study demonstrates the potential of the neural network to provide accurate and automated predictions of Arctic winter SIT from thermodynamic data, and, thus, the network can be used to support decision-making in certain fields, such as polar shipping, environmental protection, and climate science. Full article

This article examines the relationship between interannual changes in the sea ice extent and thickness in the Kara Sea with climate change in the region and with sea surface temperature in the tropical North Atlantic. The data from observations at meteorological stations, ERA5 reanalysis, and data on the sea ice from the AARI website for 1979–2021 were used. The growth of ice in winter is most influenced by air temperature and downward long-wave radiation. In summer, interannual changes in sea ice extent are closely related to air temperature. The remote influence of the sea surface temperature anomalies in the tropics of the North Atlantic on the summer (July–September) sea ice in the Kara Sea is discovered 33–35 months later. A significant correlation between climate and sea ice anomalies can serve as the basis for predicting up to four years ahead. Full article