Search Results (155)

Sea ice and the resulting loads are major safety concerns for vessels operating in ice-covered regions. This study presents a tailored sea ice and local ice load monitoring system specifically designed for polar cargo vessels. The system employs shipboard cameras coupled with a DeepLab v3+-based algorithm to achieve real-time ice concentration identification, demonstrating 90.68% accuracy when validated against historical Arctic Sea ice imagery. For structural load monitoring, we developed a hybrid methodology integrating numerical simulations, full-scale strain measurements, and classification society standards, enabling the precise evaluation of ice-induced structural responses. The system’s operational process is demonstrated through comprehensive case studies of characteristic ice collision scenarios. Furthermore, this system serves as an exemplary implementation of a navigation assistance framework for polar cargo vessels, offering both real-time operational guidance and long-term reference data for enhancing ice navigation safety. Full article

Polynyas play a crucial role in polar ecosystems, influencing biodiversity, climate regulation, and oceanic processes. This study employs Synthetic Aperture Radar (SAR) data to determine the optimal sea ice concentration threshold for polynya identification, which is established at 75%. We present a dataset of daily polynya distribution in the Arctic and Antarctic from 2013 to 2022, analyzing their spatial patterns, interannual variability, and seasonal dynamics. Our results indicate that coastal polynyas, primarily located near landmasses, dominate both polar regions. The total polynya area in the Antarctic remained relatively stable, averaging approximately 1.86 × 10⁸ km² per year, with an interannual fluctuation of −3.1 × 10⁵ km² per year. In the Arctic, the average polynya area is around 1.59 × 10⁸ km² per year, with an interannual fluctuation of −7.1 × 10⁵ km² per year. Both regions exhibit distinct seasonal cycles: Arctic polynyas peak in May and reach their minimum in September, whereas Antarctic polynyas expand in November and contract to their smallest extent in February. The polynya formation and development result from a complex interplay of multiple factors, with no single variable fully explaining variations in polynyas’ extent. Additionally, the polynya area in the NOW, and Weddell Sea polynyas, exhibit consistent trends with chlorophyll-a concentration, highlighting their role as critical habitats for primary productivity in polar regions. These findings provide key insights into polynya dynamics and their broader implications for climate and ecological processes in polar regions. Full article

The Arctic Ocean has a uniquely complex system associated with tightly coupled ocean–ice–atmosphere–land interactions. The Arctic Ocean is considered to be highly susceptible to global climate change, with the potential for dramatic environmental impacts at both regional and global scales, and its spatial differences particularly have been exacerbated. A comprehensive understanding of Arctic Ocean environmental responses to climate change thus requires classifying the Arctic Ocean into subregions that describe spatial homogeneity of the clusters and heterogeneity between clusters based on ocean physical properties and implementing the regional-scale analysis. In this study, utilizing the long-term optimum interpolation sea surface temperature (SST) datasets for the period 1982–2023, which is one of the essential indicators of physical processes, we applied the K-means clustering algorithm to generate subregions of the Arctic Ocean, reflecting distinct physical characteristics. Using the variance ratio criterion, the optimal number of subregions for spatial clustering was 12. Employing methods such as information mapping and pairwise multi-comparison analysis, we found that the 12 subregions of the Arctic Ocean well represent spatial heterogeneity and homogeneity of physical properties, including sea ice concentration, surface ocean currents, SST, and sea surface salinity. Spatial patterns in SST changes also matched well with the boundaries of clustered subregions. The newly identified physical subregions of the Arctic Ocean will contribute to a more comprehensive understanding of the Arctic Ocean’s environmental response to accelerating climate change. Full article

The Laptev Sea, as a marginal sea and a key source of sea ice for the Arctic Ocean, has a profound influence on the dynamic processes of sea ice evolution. Under a 2 °C global warming scenario, the accelerated ablation of Arctic sea ice is projected to greatly impact Arctic warming. The ocean regulates global climate through its interactions with the atmosphere, where sea surface temperature (SST) serves as a crucial parameter in exchanging energy, momentum, and gases. SST is also a key driver of sea ice concentration (SIC). In this paper, we analyze the spatiotemporal variability of SST and SIC, along with their interrelationships in the Laptev Sea, using daily optimum interpolation SST datasets from NCEI and daily SIC datasets from the University of Bremen for the period 2004–2023. The results show that: (1) Seasonal variations are observed in the influence of SST on SIC. SIC exhibited a decreasing trend in both summer and fall with pronounced interannual variability as ice conditions shifted from heavy to light. (2) The highest monthly averages of SST and SIC were in July and September, respectively, while the lowest values occurred in August and November. (3) The most pronounced trends for SST and SIC appeared both in summer, with rates of +0.154 °C/year and −0.095%/year, respectively. Additionally, a pronounced inverse relationship was observed between SST and SIC across the majority of the Laptev Sea with correlation coefficients ranging from −1 to 0.83. Full article

Over the past three decades, increasing atmospheric CO₂ (AtmCO₂) has led to climate warming, sea ice reduction and ocean acidification in the Beaufort Sea (BS). Additionally, the effects of upwelling on the carbon cycle and acidification in the BS are still unknown. The Regional Arctic System Model (RASM) adequately reflects the observed long-term trends and interannual variations in summer sea ice concentration (SIC), temperature, partial pressure of CO₂ (pCO₂) and pH from 1990 to 2020. Multiple linear regression results from a control case show that surface (0–20 m) pH decline is significantly driven by AtmCO₂ and SIC, while AtmCO₂ dominates in subsurface (20–50 m) and deep layers (50–120 m). Regression results from a sensitivity case show that even if the AtmCO₂ concentration remained at 1990 levels, the pH would still exhibit a long-term decline trend, being significantly driven by SIC only in the surface layers and by SIC and net primary production (NPP) in the subsurface layers. In contrast to the nearly linearly increasing AtmCO₂ over the last three decades, the ocean pH shows more interannual variations that are significantly affected by SIC and mixed layer depth (MLD) in the surface, NPP and Ekman pumping velocity (EPV) in the subsurface and EPV only in the deep layer. The comparison of results from high and low SIC years reveals that areas with notable pH differences are overlapping regions with the largest differences in both SIC and MLD, and both cause a statistically significant increase in pCO₂ and decrease in pH. Comparison of results from high and low EPV years reveals that although stronger upwelling can lift up more nutrient-rich seawater in the subsurface and deep layers and lead to higher NPP and pH, this effect is more than offset by the higher DIC lifted up from deep water, leading to generally lower pH in most regions. Full article

Sea ice thickness is an important component of the Arctic environment, bearing crucial significance in investigations pertaining to global climate and environmental changes. This study employs data from the HaiYang-2B satellite altimeter (HY-2B ALT) for the estimation of Arctic Sea ice thickness from November 2021 to April 2022. The HY-2B penetration coefficient is calculated for the first time to correct the freeboard in areas with sea ice concentration greater than 90%. The estimation accuracy is improved by enhancing the data on sea ice density, seawater density, snow depth, and snow density. The research analyzed the effects of snow depth and penetration coefficient on sea ice thickness results. The results of sea ice type classification were compared with OSI-SAF ice products, and the sea ice thickness estimation results were compared with four satellite ice thickness products (CryoSat-2 and SMOS (CS-SMOS), Centre for Polar Observation and Modelling Data (CPOM), CryoSat-2 (CS-2), and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS)) as well as two validation ice thickness data sets (Operation IceBridge (OIB) and ICEBird). The accuracy of sea ice classification exceeds 92%, which is in good agreement with ice type product data. The RMSD of sea ice thickness estimation is 0.56 m for CS-SMOS, 0.68 m for CPOM, 0.47 m for CS-2, 0.69 m for PIOMAS, and 0.79 m for validation data. Full article

In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the Spatio-Temporal Decomposition Network (STDNet), to predict the daily SIC in the Arctic. The model is based on the Seasonal and Trend decomposition using Loess (STL) decomposition idea to decompose the model into trend and seasonal components. In addition, we have designed the Global Sparse Attention Module (GSAM) to help the model extract global information. STDNet not only extracts seasonal signals and trend information with periodical correspondence from the data but also obtains the spatio-temporal dependence features in the data. The experimental methodology involves predicting the next 10 days based on the first 10 days of data. The prediction results provided the following metrics for the 10-day forecast of STDNet: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination of 1.988%, 3.541%, 5.843%, and 0.979, respectively. The average Binary Accuracy (BACC) at the beginning of September for the period 2018–2022 reached 93.85%. The proposed STDNet model outperforms and is lighter than existing deep-learning-based SIC prediction models. Full article

Sea ice concentration (SIC) monitoring in the Arctic using microwave radiometer data is a well-established method with numerous published accuracy studies. For the Baltic Sea, accuracy studies have not yet been conducted. In this study, we evaluated five different SIC products over the Baltic Sea using MODIS (250 m) and Sentinel-2 (10 m) open water–sea ice classification charts. The selected SIC products represented different SIC algorithm types, e.g., climate data records and near-real-time products. The one-to-one linear agreement between the radiometer SIC dataset and the MODIS/Sentinel-2 SIC was always quite poor; the slope of the linear regression was from 0.40 to 0.77 and the coefficient of determination was from 0.26 to 0.80. The standard deviation of the difference was large and varied from 15.5% to 26.8%. A common feature was the typical underestimation of the MODIS/Sentinel-2 SIC at large SIC values (SIC > 60%) and overestimation at small SIC values (SIC < 40%). None of the SIC products performed well over the Baltic Sea ice, and they should be used with care in Baltic Sea ice monitoring and studies. 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

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage. Full article

The modern Arctic is characterized by a decreased ice cover and significant interannual variability. However, the reaction of the High Arctic ecosystem to such changes is still being determined. This study tested the hypothesis that the key drivers of changes in phytoplankton are the position and intensity of Atlantic water (AW) flow. The research was conducted in August 2017 in the northern part of the Barents Sea and in August 2020 in the Nansen Basin. In 2017, the Nansen Basin was ice covered; in 2020, the Nansen Basin had open water up to 83° N. A comparative analysis of phytoplankton composition, dominant species, abundance, and biomass at the boundary of the ice and open water in the marginal ice zone (MIZ) as well as in the open water was carried out. The total biomass of the phytoplankton in the photic layer of MIZ is one and a half orders of magnitude greater than in open water. In 2017, the maximum abundance and biomass of phytoplankton in the MIZ were formed by cold-water diatoms Thalassiosira spp. (T. gravida, T. rotula, T. hyalina, T. nordenskioeldii), associated with first-year ice. They were confined to the northern shelf of the Barents Sea. The large diatom Porosira glacialis grew intensively in the MIZ of the Nansen Basin under the influence of Atlantic waters. A seasonal thermocline, above which the concentrations of silicon and nitrogen were close to zero, and deep maxima of phytoplankton abundance and biomass were recorded in the open water. Atlantic species—haptophyte Phaeocystis pouchettii and large diatom Eucampia groenlandica—formed these maxima. P. pouchettii were observed in the Nansen Basin in the Atlantic water (AW) flow (2020); E. groenlandica demonstrated a high biomass (4848 mg m⁻³, 179.5 mg C m⁻³) in the Franz Victoria trench (2017). Such high biomass of this species in the northern Barents Sea shelf has not been observed before. The variability of the phytoplankton composition and biomass in the Franz Victoria trench and in the Nansen Basin is related to the intensity of the AW, which comes from the Frame Strait as the Atlantic Water Boundary Current. Full article

We apply the Canny edge algorithm to imagery from the Utqiaġvik coastal sea ice radar system (CSIRS) to identify regions of open water and sea ice and quantify ice concentration. The radar-derived sea ice concentration (SIC) is compared against the (closest to the radar field of view) 25 km resolution NSIDC Climate Data Record (CDR) and the 1 km merged MODIS-AMSR2 sea ice concentrations within the ∼11 km field of view for the year 2022–2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analyses in total) covering a range of ice conditions with landfast ice, drifting ice, and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼−1.0 and 0.7%, respectively, with an averaged inter-analyst error of ±3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter, including those during the freeze-up events (reduction in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence of fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. The proposed technique allows for the derivation of the SIC from CSIRS data at spatial and temporal scales that coincide with those at which coastal communities members interact with sea ice. Moreover, by measuring the SIC in nearshore waters adjacent to the shoreline, we can quantify the effect of land contamination that detracts from the usefulness of satellite-derived SIC for coastal communities. Full article

In efforts to fulfill the objectives of taking part in pragmatic cooperation in the Arctic, constructing the “Silk Road on Ice”, and ensuring ships’ safety and risk assessment in the Arctic, the two biggest hazards, which concern ships’ navigation in the Arctic, are wind and sea ice. Sea ice can result in a ship being besieged or crashing into an iceberg, endangering both human and property safety. Meanwhile, light winds can assist ships in breaking free of a sea-ice siege, whereas strong winds can hinder ships’ navigation. In this work, we first calculated the spatial and temporal characteristics of a number of indicators, including Arctic wind speed, sea-ice density, the frequency of different wind directions, the frequency of a sea-ice density of less than 20%, the frequency of strong winds of force six or above, etc. Using the ERA5 wind field and the SSMI/S sea-ice data, and applying statistical techniques, we then conducted a joint analysis to determine the correlation coefficients between the frequencies of various wind directions, the frequency of strong winds and its impact on the density of sea ice, the frequency of a sea-ice concentration (SIC) of less than 20%, and the correlation coefficient between winds and sea-ice density. In doing so, we determined importance of factoring the wind’s contribution into sea-ice analysis. Full article

Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction model, SICFormer, which can realise end-to-end daily sea ice concentration prediction. Specifically, the model uses a 3D-Swin Transformer as an encoder and designs a decoder to reconstruct the predicted image based on PixelShuffle. This is a new model architecture that we have proposed. Single-day SIC data from the National Snow and Ice Data Center (NSIDC) for the years 2006 to 2022 are utilised. The results of 8-day short-term prediction experiments show that the average Mean Absolute Error (MAE) of the SICFormer model on the test set over the 5 years is 1.89%, the Root Mean Squared Error (RMSE) is 5.99%, the Mean Absolute Percentage Error (MAPE) is 4.32%, and the Nash–Sutcliffe Efficiency (NSE) is 0.98. Furthermore, the current popular deep learning models for spatio-temporal prediction are employed as a point of comparison given their proven efficacy on numerous public datasets. The comparison experiments show that the SICFormer model achieves the best overall performance. Full article

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies. Full article