Search Results (78)

Freshwater accumulation is one of the most striking observations in the Beaufort Gyre (BG) region in the Arctic Ocean. A 39-year simulation, using the validated high-resolution, geometrical-fitting, unstructured grid Finite-Volume Community Ocean Model for the Arctic Ocean, aimed to investigate the contributions of coastal currents and their interannual variability to this phenomenon. The model reasonably reproduced the interannual variability of freshwater content (FWC) in the BG region. Analysis revealed the constructive role of Ekman pumping in supplying FWC, while the lateral flux generally acts to remove FWC from the region. The disparity between Ekman pumping and lateral flux drives the interannual variability of total FWC, with accumulation occurring when the downward Ekman FWC flux surpasses the net outflow-induced lateral FWC flux. Since 2007, there has been a significant increase in downward Ekman pumping, accompanied by a rise in net outflow lateral flux, indicating heightened variability of FWC in the BG region. The model results suggested that the coastal flow over the Arctic continental shelf underwent dramatic changes, especially during summer, and these changes were partially due to increased freshwater and sea ice melting. Increased lateral FWC flux during summer has become a competitive source for unprecedented seasonal freshwater accumulation in the BG region. Flow intensification over the North American coast is influenced by increased freshwater runoff, including the Firth, Kobuk, and Mackenzie Rivers. Interannual FWC variation in the Beaufort Sea could be influenced by the changes in slope flow, with the water originating in part from the Barents and Kara Seas. Full article

The high peak-to-average power ratio (PAPR) in classical high-speed digital data transmission systems with orthogonal frequency division multiplexing (OFDM) limits energy efficiency and communication range. This paper proposes a method for randomizing OFDM signals via frequency coding using synthesized pseudorandom sequences with improved autocorrelation properties, obtained through machine learning, to minimize PAPR in complex, non-stationary hydroacoustic channels for communicating with underwater robotic systems. A neural network architecture was developed and trained to generate codes of up to 150 elements long based on an analysis of patterns in previously found best short sequences. The obtained class of OFDM signals does not require regular and accurate estimation of channel parameters while remaining resistant to various types of impulse noise, Doppler shifts, and significant multipath interference typical of the underwater environment. The attained spectral efficiency values (up to 0.5 bits/s/Hz) are relatively high for existing hydroacoustic communication systems. It has been shown that the peak power of such multi-frequency information transmission systems can be effectively reduced by an average of 5–10 dB, which allows for an increase in the communication range compared to classical OFDM methods in non-stationary hydrological conditions at acceptable bit error rates (from 10⁻² to 10⁻³ and less). The effectiveness of the proposed methods of randomization with synthesized codes and frequency coding for OFDM signals was confirmed by field experiments at sea on the shelf, over distances of up to 4.2 km, with sea waves of up to 2–3 Beaufort units and mutual movement of the transmitter and receiver. Full article

Interannual climate changes and increasing atmospheric CO₂ (AtmCO₂) have significantly altered sea surface partial pressure of CO₂ (pCO₂) in the Beaufort Sea (BS). Yet, their decadal variability and underlying mechanisms remain inadequately understood. Using observational data and the Regional Arctic System Model (RASM), a decreasing trend transition of the BS summer surface pCO₂ was identified at around 2010–2012. Sensitivity cases reveal that the decadal trend transition in surface pCO₂ (early: 4.12 ± 0.80 μatm/yr, p < 0.05 and late: 1.23 ± 2.22 μatm/yr, p > 0.05) is driven by interannual climate changes. While the long-term increase in AtmCO₂ does not directly drive surface pCO₂ trend transition, it reduces its magnitude. The sensitivity experiment with no interannual AtmCO₂ changes from 1990 reveals that the statistically significant contributor of the decadal trend transition in surface pCO₂ is the concurrent transition in sea ice concentration (SIC, early: −0.0120 ± 0.0037/yr, p < 0.05 and late: 0.0101 ± 0.0063/yr, p > 0.05). The decadal trend transitions in the subsurface and deep layer pCO₂ are negligible compared to that in the sea surface pCO₂ due to the insignificant influence of interannual climate changes on subsurface and deep layer pCO₂. The surface pCO₂ decadal trend transition is significantly correlated with a trend transition of CO₂ sink. On seasonal timescales, the effects of SIC on the decadal trend transition of pCO₂ occur primarily within the duration of open-water (DOW), and align with the decadal trend transitions in the open-water start day, end day, and DOW. The magnitude of sea surface pCO₂ trend transition increases as the magnitude of the DOW trend transition increases. Full article

Wind power generation ships (WPG ships), which combine rigid sails for propulsion and underwater turbines for onboard power generation, have attracted increasing attention as a promising concept for utilizing renewable energy at sea. This study presents an integrated assessment of a WPG ship by combining towing-tank experiments, CFD simulations using ANSYS Fluent, and theoretical analysis to evaluate the coupled performance of sails, hull, and underwater turbines. First, sail thrust and bare-hull resistance were quantified to identify the effective operating-speed range under Beaufort 6–8 wind conditions, and the optimal number of rigid sails was determined. Based on a thrust–resistance balance at a representative rated operating point, two turbine configurations (two and four turbines) were preliminarily sized. The results show that ten rigid sails can provide near-maximum thrust without excessive aerodynamic interference, and the installation of turbines significantly reduces the feasible operating range compared to the bare-hull case. For the two-turbine configuration, a common effective ship-speed range of 6.58–8.0 m/s is obtained, whereas the four-turbine configuration is restricted to 6.58–7.44 m/s due to wake losses, additional appendage drag, and near-free-surface effects. The four-turbine configuration exhibits approximately 30% lower total power output than the two-turbine configuration. These findings demonstrate that an integrated, system-level evaluation is essential for WPG ship design and indicate that the two-turbine configuration offers a more favorable balance between power generation capability and operational flexibility. Full article

Amplified Arctic warming has led to a pervasive decline in sea ice cover over recent decades; yet, the pattern and governing mechanisms of sea ice concentration (SIC) state transitions remain unclear. This study reveals pronounced regional contrasts in mean-state SIC during the transition from 1979–2006 to 2007–2024, concurrent with a reduction in the sea ice extent over the same period. The September-mean sea ice in the 70° N–80° N Arctic belt retreated significantly from 1979–2006 to 2007–2024, while the Barents and Greenland Seas exhibited persistent ice loss in March. Enhanced ice-albedo feedback, together with concurrent rises in the 2 m air temperature and sea surface temperature, dominate these ice loss processes. Dynamical processes exert distinct regulatory roles in September and March. The strengthened Beaufort High induces sea ice convergence to partially offset the September thermodynamically driven ice loss, while the positive-phase Arctic Dipole in March amplifies the transpolar airflow and winds over the Greenland and Barents Seas and triggers rapid sea ice export and significant loss in these regions. These findings underscore the spatial heterogeneity of Arctic SIC transitions and highlight the complex interplay of thermodynamic and dynamic processes shaping them. Full article

The Beaufort Sea has experienced significant sea ice retreat in recent decades, driven by both thermodynamic and dynamic processes. This study investigates the drivers and predictability of summer sea ice retreat in the Beaufort Sea by integrating an ocean–sea ice model with satellite-derived sea ice concentration data and atmospheric reanalysis products. Model diagnostics from 1994 to 2019 reveal that thermodynamic processes dominate annual sea ice loss (approximately 90%), with vertical heat flux accounting for roughly 85% of total oceanic heat input. The summer sea ice minimum area and the day of opening, derived from either model results and satellite observations, have a strong correlation with R² = 0.60 and R² = 0.77, respectively, enabling regression equations based solely on remote sensing data. Further multiple linear regression incorporating preceding winter (January to April) accumulated temperature and easterly wind yields moderately robust forecasts of minimum sea ice area (R² = 0.49) during 1998–2020. Additionally, analysis of reanalysis wind data shows that the timing of minimum sea ice area is significantly influenced by the frequency and intensity of sub-seasonal easterly wind events during melt season. These results demonstrate the critical importance of remote sensing in monitoring Arctic sea ice variability and enhancing seasonal prediction capability under a rapidly changing climate. Full article

Variability in the Laptev Sea–East Siberian Sea circulation system modulates freshwater circulation in the Arctic Ocean, yet details of these wind-driven mechanisms remain poorly understood. Based on in situ observations from the 2018 Sino-Russian joint Arctic expedition, this study investigates the modulatory influence of wind on circulation structures and freshwater transport in the study area and examines the long-term variation characteristics of this circulation and its inherent connection with the Arctic wind. In situ measurements confirm two freshwater transport pathways: a coastal-current route and a geostrophic slope-current route. As the Beaufort High moves toward the Canadian Basin, it shifts wind patterns from anticyclonic to cyclonic, which regulates the transport of shelf water by influencing the prevailing wind direction. Furthermore, our analysis identifies two main modes of long-term changes in summer surface circulation: the first mode characterizes the coastal-current architecture, while the second mode delineates slope-current configurations. Crucially, large-scale modes of the Arctic wind play an important role in regulating circulation. Its first mode corresponds to the summer anticyclonic circulation pattern of the Arctic Ocean Oscillation, which drives the eastward strengthening of the coastal current, while the third mode presents a mechanism similar to the Arctic Dipole, which promotes the development of the slope current by enhancing the convergence of the polar current and wind. This has led to the long-term strengthening of the slope current. Full article

Wildland fires are a common and destructive natural disaster in Alaska. Recent active fires in Alaska were assessed and analysed for their associated synoptic-scale climatic conditions in this study. Hotspot (HS) data from satellite observations over the past 20 years since 2004 (total number of HS = 300,988) were used to identify active fire-periods, and the occurrence of Rossby wave breaking (RWB) was examined using various weather maps. Analysis results show that there are 13 active fire-periods of which 7 active fire-periods are related to RWB. The total number of HSs during the seven RWB-related fire-periods was 164,422, indicating that about half (54.6%) of the recent fires in Alaska occurred under fire weather conditions related to RWB. During the RWB-related fire-periods, two hotspot peaks with different wind directions occurred. At the first hotspot peak, southwesterly wind blew from high-pressure systems in the Gulf of Alaska. In the second hotspot peak, the Beaufort Sea High (BSH) supplied strong easterly wind into Interior Alaska. It was suggested that changes in wind direction during active fire-period and continuously blowing winds from BSH may affect fire propagation. It is hoped that this study will stimulate further research into active fires related to RWBs in Alaska. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

In the context of global warming, polar tourism is developing rapidly, and the demand for polar cruise travel in the Northwest Passage continues to increase, while sea ice has long been a key factor limiting the development of polar cruise tourism. This study focuses on the operational risk of sea ice on cruise ships in the Northwest Passage (NWP), aiming to provide a scientific basis for ensuring the safety of cruise ship navigation and promoting the sustainable development of polar tourism. Based on ice data from 2015 to 2024, this study used the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology recommended by the International Maritime Organization (IMO) to establish three scenarios for the route of ice class IC cruise ships: light ice, normal ice, and heavy ice. The navigable windows were systematically analyzed and critical waters along the route were identified. The results indicate that the navigable windows for IC ice-class cruise ships under light ice conditions are from mid-July to early December, while the navigable period under normal ice conditions is only from mid- to late September, and navigation is not possible under heavy ice conditions. The study identified Larsen Sound, Barrow Strait, Bellot Strait and Eastern Beaufort Sea as critical waters on the NWP cruise route. Among them, Larsen Sound and Eastern Beaufort Sea have a more prominent impact on voyage scheduling because their navigation weeks overlap less with other waters. This study provides a new idea for the risk assessment of polar cruise ships in ice regions. The research results can provide an important reference for the safe operation of polar cruise ships in the NWP and the decision-making of relevant parties. 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

Most deep-sea organisms feed on the organic matter produced in surface waters and settle on the seafloor. In polar regions, sea ice algal detritus and phytoplankton detritus are the main food sources for benthic fauna that reach the seafloor in pulses. Climate change affects the extension and duration of sea ice cover, which may affect the quantity and quality of food reaching the seafloor, resulting in less ice algae and more phytoplankton biomass. We conducted onboard pulse-chase experiments using sediment cores collected from Baffin Bay, Amundsen Gulf, and the Beaufort Sea to study how macroinfaunal communities in the Canadian Arctic use both food sources. Dual-labeled (¹³C and ¹⁵N) diatoms, Thalassiosira nordenskioeldii (phytoplankton treatment) and Synedra hyperborea (ice algae treatment), were used as tracers of food consumption by macroinfaunal groups. Community structure was analyzed in each region and differences were found among sites. The total uptake of both food sources was higher in Baffin Bay; the macroinfaunal biomass was the highest, with facultative filter/surface-deposit feeders accounting for more than 70% of the total biomass. The Baffin Bay station was the only location where there were notable variations in the biomass-specific uptake of ice algae and phytoplankton detritus by the bivalves and polychaetes, as well as by the community as a whole. At the same time, both food sources were consumed in equal quantities at the Amundsen Gulf and Beaufort Sea stations. This suggests that ice algae are not preferentially uptaken, and macroinfaunal communities may be resilient to a decrease in ice algal input to the seafloor inflicted by sea ice reduction. Full article

Ship detection, a crucial task, relies on the traditional CFAR (Constant False Alarm Rate) algorithm. However, this algorithm is not without its limitations. Noise and clutter in radar images introduce significant variability, hampering the detection of objects on the sea surface. The algorithm’s theoretically Constant False Alarm Rates are not upheld in practice, particularly when conditions change abruptly, such as with Beaufort wind strength. Moreover, the high computational cost of signal processing adversely affects the detection process’s efficiency. In previous work, a four-stage methodology was designed: The first preprocessing stage consisted of image enhancement by applying convolutions. Labeling and training were performed in the second stage using the Faster R-CNN architecture. In the third stage, model tuning was accomplished by adjusting the weight initialization and optimizer hyperparameters. Finally, object filtering was performed to retrieve only persistent objects. This work focuses on designing a specific methodology for ship detection in the Peruvian coast using commercial radar images. We introduce two key improvements: automatic cropping and a labeling interface. Using artificial intelligence techniques in automatic cropping leads to more precise edge extraction, improving the accuracy of object cropping. On the other hand, the developed labeling interface facilitates a comparative analysis of persistence in three consecutive rounds, significantly reducing the labeling times. These enhancements increase the labeling efficiency and enhance the learning of the detection model. A dataset consisting of 60 radar images is used for the experiments. Two classes of objects are considered, and cross-validation is applied in the training and validation models. The results yield a value of 0.0372 for the cost function, a recovery rate of 94.5%, and an accuracy rate of 95.1%, respectively. This work demonstrates that the proposed methodology can generate a high-performance model for contact detection in commercial radar images. Full article

High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region. Full article

With the further establishment of relevant regulations on ship emissions by countries worldwide and the IMO, and the increasing frequency of severe sea conditions in shipping routes, optimizing ship energy efficiency under high wind and wave conditions has become an important research direction. This study establishes a grey-box model for optimizing ships’ energy consumption under severe sea conditions, with wave heights above two meters and a Beaufort scale score above five, based on the principle of ship–engine–propeller matching and a non-dominated sorting optimization algorithm. Using historical navigation data from a case ship under severe sea conditions, a white-box model and a black-box model for ship fuel consumption were established. These models were combined to create a grey-box model for ship fuel consumption. The K-Medoids clustering algorithm was used to cluster severe sea conditions. The optimization variables were the main engine’s speed, with the fuel consumption per nautical mile and the ship’s speed being used as optimization objectives. The non-dominated sorting genetic algorithm was optimized for each sea condition, resulting in the best speed for each sea state. The results indicate that the model developed in this paper reduced the main engine’s fuel consumption per nautical mile by 21.9% and increased the speed by 16.7% under the most severe sea conditions. Therefore, the proposed model effectively optimizes ship energy efficiency and reduces navigation time under severe sea conditions, providing an effective solution for operations in actual severe sea conditions. Full article