Search Results (115)

The surface albedo, $\alpha$, is one of the key climate parameters since it regulates the shortwave radiation absorbed by the Earth’s surface. An accurate determination of the albedo is crucial in the polar regions due to its variations associated with climate change and its role in the strong feedback mechanisms. In this work, satellite and in situ measurements of broadband surface albedo at the Thule High Arctic Atmospheric Observatory (THAAO) on the northwestern coast of Greenland (76.5°N, 68.8°W) are compared. Measurements of surface albedo were started at THAAO in 2016. They show a large variability mainly in the transition seasons, suggesting that THAAO is a very interesting site for verifying the satellite capabilities in challenging conditions. The comparison of daily ground-based and MODIS-derived albedo covers the period July 2016–October 2024. The analysis has been conducted for all-sky and cloud-free conditions. The mean bias and mean squared difference between the two datasets are −0.02 and 0.09, respectively, for all sky conditions and −0.03 and 0.06 for cloud-free conditions. Very good agreement is found in summer in snow-free conditions, when the mean albedo is 0.17 in both datasets under cloud-free conditions. On the contrary, the capability to determine the surface albedo from space is largely reduced in the transition seasons, when significant differences between ground- and satellite-based albedo estimates are found. Differences for all-sky conditions may be as large as 0.3 in spring and autumn. These maximum differences are significantly reduced for cloud-free conditions, although a negative bias of MODIS data with respect to measurements at THAAO is generally found in spring. The combined analysis of the albedo, cloudiness, air temperature, and precipitation characteristics during two periods in 2023 and 2024 shows that, although satellite observations provide a reasonable picture of the long-term albedo evolution, they are not capable of following fast changes in albedo values induced by precipitation of snow/rain or temperature variations. Moreover, as expected, cloudiness plays a large role in affecting the satellite capabilities. The use of MODIS albedo data with the best value of the quality assurance flag (equal to 0) is recommended for studies aimed at determining the daily evolution of the surface radiation and energy budget. Full article

This study presents the first subfossil beetle (Coleoptera) records from Lithuania, from Late Glacial organic deposits. Bulk sediment samples were collected from the Pamerkiai and Zervynos Outcrops in SE Lithuania, and from the Ventė Outcrop at the eastern coast of the Curonian Lagoon, W Lithuania. Radiocarbon dating determined that the studied sediments accumulated between ~15,000–11,300 cal BP. The beetle assemblages (29–177 individuals per sample) consist of many cold-adapted species that are common from Late Glacial deposits in the British Isles, Southern Sweden, and continental Europe. True arctic species are absent from the assemblages, and it is likely that the Lithuanian beetle fauna was most similar to nearby southern regions (e.g., Poland) during the Late Glacial. Besides a variety of aquatic species and typical wetland species, many beetle species living in open environments and on sandy soils were identified. In almost all the samples, taxa associated with pine trees, willows, and birches were found, confirming previous reconstructions of a sparsely forested landscape during the climatic periods GI-1e–GI-1a (Bølling-Allerød). The species assemblages from the youngest samples, associated with GS-1 (Younger Dryas), indicate the disappearance of large aquatic macrophytes and decreasing temperatures in Southern Lithuania, but a persistence of trees in the region. Full article

The paper presents the results of a study on littoral algae communities along the Murmansk coast from 2021–2024. The emphasis is on fucus algae and green algae communities as the most abundant ones. For the first time, an annual monitoring of littoral algae distribution in the bays of the Barents Sea was performed using a set of methods, allowing a better understanding of the dynamics of their biomass. Unlike most classical studies, which only focus on biomass and population structure, this work shows the results of using UAV-based remote sensing in combination with traditional coastal sampling techniques. The features and limitations of this approach in Arctic latitudes are discussed. According to the monitoring results, an increase in fucus algae biomass is observed in the study area, which may be associated with an increase in summer temperatures and water salinity. Fucus serratus and Pelvetia canaliculata populations remain stable. Ulvophycean algae show seasonal peaks of development with abnormally high biomass in areas of anthropogenic impact, which may indicate local eutrophication. The map of algae spatial distribution is presented. The results are important for understanding the structure and functioning of the Arctic ecosystem and for assessing the environmental impact in the region. Full article

Seafloor communities along the eastern Svalbard coast remain poorly studied. To address this gap, we sampled benthic organisms on the soft sediments of Storfjord in 2017 and 2019, a large fjord predominantly influenced by cold Arctic waters, to study the local fauna and identify the key environmental drivers shaping community structure. In total, 314 taxa were recorded, with an increase in abundance (from 3923 to 8977 ind. m⁻², mean 6090 ind. m⁻²) and a decline in biomass (ranging from 265 to 104 g m⁻², mean 188 g m⁻²) toward the outer part of the fjord. However, no clear spatial trends were observed for alpha diversity (approximately 100 species per 0.3 m²) or the Shannon index (mean 3 per station). The primary factors influencing benthic abundance were the duration of the ice-free period (IFP) and the degree of siltation (DS), both of which are proxies for trophic conditions. The prevailing taxa displayed a high tolerance to temperature fluctuations and seasonal variability in nutrient inputs. Benthic biomass showed a negative relationship with IFP, DS, and water depth, but it was positively correlated with the proportion of fine-grained sediment. The Yoldia hyperborea community (mean abundance: 3700 ind. m⁻², mean biomass: 227 g m⁻²) was associated with Arctic waters characterized by higher inorganic suspension loads. In contrast, areas with reduced or weaker sedimentation were dominated by the communities of Maldane sarsi (6212 ind m⁻², 226 g m⁻²) and Maldane sarsi + Nemertini g.sp. (5568 ind m⁻², 165 g m⁻²). The Spiochaetopterus typicus community (7824 ind m⁻², 139 g m⁻²) was observed in areas under moderate influence of Atlantic waters, characterized by low sedimentation rates and increased fresh detritus flux. Full article

Fine-scale maps of ground ice and related surface features are critical for permafrost-related modelling and management. However, such maps are lacking across almost the entire Arctic. Machine learning provides the potential to automate regional fine-scale ground ice mapping using remote sensing and topographic data. Here, we evaluate the predictive skill of XGBoost models for identifying (1) ice wedge and (2) top-5m visible ground ice in the Tuktoyaktuk Coastlands. We find high predictive skill for ice wedge occurrence (ROC AUC = 0.95, macro F1 = 0.80), with the most important predictors being slope, distance to the coast, and probability of depression. The model accurately predicted regional and local trends in ice wedge occurrence, with an increase in ice wedge polygon (IWP) probability towards the coast and in poorly drained depressions. The model also captured IWP in well-drained uplands of the study area, including locations with poorly visible troughs not contained in the training data. Spatial transferability analyses highlight the regional variability of ice wedge probability, reflecting contrasting climatic and surface conditions. Conversely, the low predictive skill for visible ground ice (ROC AUC = 0.67, macro F1 = 0.53) is attributed to limitations in training data and weak associations with the remotely sensed predictors. The varying predictive accuracy highlights the importance of high-quality reference data and site-specific conditions for improving ground ice studies with data-driven modelling from remote sensing observations. Full article

Numerical simulation studies of the dispersion of dimethyl sulfide (DMS) in the air have increased over the last two decades in parallel with the interest in understanding its role as a precursor of non-sea salt aerosols in the lower to middle levels of the troposphere. Here, we review recent numerical modeling studies that have included DMS emissions, their atmospheric oxidation mechanism, and their subsequent impacts on air quality at regional and global scales. In addition, we discuss the available methods for estimating sea–air DMS fluxes, including parameterizations and climatological datasets, as well as their integration into air quality models. At the regional level, modeling studies focus on the Northern Hemisphere, presenting a large gap in Antarctica, Africa, and the Atlantic coast of South America, whereas at the global scale, modeling studies tend to focus more on polar regions, especially the Arctic. Future studies must consider updated climatologies and parameterizations for more realistic results and the reduction in biases in numerical simulations analysis. Full article

The retreat of Arctic sea ice has opened new maritime routes, offering faster shipping opportunities; however, these routes present significant navigational challenges due to the harsh ice conditions. To address these challenges, this paper proposes a deep learning-based Arctic ice risk management architecture with multiple modules, including ice classification, risk assessment, ice floe tracking, and ice load calculations. A comprehensive dataset of 15,000 ice images was created using public sources and contributions from the Canadian Coast Guard, and it was used to support the development and evaluation of the system. The performance of the YOLOv8n-cls model was assessed for the ice classification modules due to its fast inference speed, making it suitable for resource-constrained onboard systems. The training and evaluation were conducted across multiple platforms, including Roboflow, Google Colab, and Compute Canada, allowing for a detailed comparison of their capabilities in image preprocessing, model training, and real-time inference generation. The results demonstrate that Image Classification Module I achieved a validation accuracy of 99.4%, while Module II attained 98.6%. Inference times were found to be less than 1 s in Colab and under 3 s on a stand-alone system, confirming the architecture’s efficiency in real-time ice condition monitoring. Full article

This study aims to establish a scientific and methodological basis for predicting shoreline positions using modern data analysis and machine learning techniques. The focus area is a 5 km section of the Ural coast along Baydaratskaya Bay in the Kara Sea. This region was selected due to its diverse geomorphological features, varied lithological composition, and significant presence of permafrost processes, all contributing to complex patterns of shoreline change. Applying advanced data analysis methods, including correlation and factor analysis, enables the identification of natural signs that highlight areas of active coastal retreat. These insights are valuable in arctic development planning, as they help to recognize zones at the highest risk of significant shoreline transformation. The erosion process can be conceptualized as comprising two primary components to construct a predictive model for coastal retreat. The first is a random variable that encapsulates the effects of local structural changes in the coastline alongside fluctuations due to climatic conditions. This component can be statistically characterized to define a confidence interval for natural variability. The second component represents a systematic shift, which reflects regular changes in average shoreline positions over time. This systematic component is more suited to predictive modeling. Thus, modern information processing methods allow us to move from descriptive to numerical assessments of the dynamics of coastal processes. The goal is ultimately to support responsible and sustainable development in the highly sensitive arctic region. Full article

A graphical clustering approach was used to objectively identify prevalent surface circulation patterns in the East/Japan Sea (EJS). By applying a spectral clustering algorithm, three distinct patterns in the East Korea Warm Current (EKWC) and its extension were identified from daily maps of reanalyzed sea surface heights spanning the past 30 years. The results are consistent with previous studies that used manual classification of the EKWC’s Lagrangian trajectories, highlighting the effectiveness of spectral clustering in accurately characterizing the surface circulation states in the EJS. Notably, the recent dominance of northern paths, as opposed to routes along Japan’s coastline or those departing from Korea’s east coast further south, has prompted focused re-clustering of the northern paths according to their waviness. This re-clustering, with additional emphasis on path length, distinctly categorized two patterns: straight paths (SPs) and large meanders (LMs). Notably, SPs have become more prevalent in the most recent years, while LMs have diminished. An autoregression analysis reveals that seasonal anomalies in the cluster frequency in spring tend to persist through to the following autumn. The frequency anomalies in the SPs correlate strongly with the development of pronounced anomalies in the gradient of meridional sea surface height and negative anomalies in the surface wind stress curl in the preceding cold seasons. This relationship explains the observed correlation between a negative Arctic Oscillation during the preceding winter and the increased frequency of SPs in the subsequent spring. The rapid increase in the occurrence of SPs indicates that a reduction in LMs limits the mixing of cold, fresh, northern waters with warm, saline, southern waters, thereby reinforcing the presence of SPs due to a strengthened gradient of meridional surface height and contributing to a slowdown in the regional overturning circulation. Full article

Arctic coastlines are the most vulnerable regions of the Earth, and local communities in those areas are being affected by rising sea levels and temperature. Therefore, Earth Observation combined with up-to-date geoinformation tools offers a dependable, cost-effective, and time-efficient approach to understanding the socioeconomic impact of climate changes in Arctic coastal areas. A promising approach is the Coastal Vulnerability Index (CVI), which takes into account different factors such as geomorphology, sea factors, and shoreline retreat or advance, to estimate the grade of vulnerability of a coastal area. Notwithstanding its potential, its application in the Arctic is still challenging. This study targets to estimate CVI to value the vulnerability of the coastal areas of Norway located in the Arctic. For the application of CVI and specifically for geomorphological and sea factors, data were acquired from international and national institutes. After the collection of all the necessary parameters for CVI was completed, all datasets were imported into a GIS software program (ArcGIS Pro) where the vulnerability classes of CVI were estimated. The results show that most of the coast of Northern Norway is characterized by a low to high degree of vulnerability, while in the island of Tromsø the vulnerability is mainly high and very high. Full article

Based on the WAVEWATCH III wave model, China’s National Marine Environmental Forecasting Center has developed an operational global ocean wave forecasting system that covers the Arctic region. In this study, in situ buoy observations and satellite remote sensing data were used to perform a detailed evaluation of the system’s forecasting results for 2022, with a focus on China’s offshore and global ocean waters, so as to comprehensively understand the model’s forecasting performance. The study results showed the following: In China’s coastal waters, the model had a high forecasting accuracy for significant wave heights. The model tended to underestimate the significant wave heights in autumn and winter and overestimate them in spring and summer. In addition, the model slightly underestimated low (below 1 m) wave heights, while overestimating them in other ranges. In terms of spatial distribution, negative deviations and high scatter indexes were observed in the forecasting of significant wave heights in semi-enclosed sea areas such as the Bohai Sea, Yellow Sea, and Beibu Gulf, with the largest negative deviation occurring near Liaodong Bay of the Bohai Sea (−0.18 m). There was a slight positive deviation (0.01 m) in the East China Sea, while the South China Sea exhibited a more significant positive deviation (0.17 m). The model showed a trend of underestimation for the forecasting of the mean wave period in China’s coastal waters. In the global oceanic waters, the forecasting results of the model were found to have obvious positive deviations for most regions, with negative deviations mainly occurring on the east coast and in relatively closed basins. There were latitude differences in the forecasting deviations of the model: specifically, the most significant positive deviations occurred in the Southern Ocean, with smaller positive deviations toward the north, while a slight negative deviation was observed in the Arctic waters. Overall, the global wave model has high reliability and can meet the current operational forecasting needs. In the future, the accuracy and performance of ocean wave forecasting can be further improved by adjusting the parameterization scheme, replacing the wind fields with more accurate ones, adopting spherical multiple-cell grids, and data assimilation. Full article

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute. Full article

Strontium is a toxic chemical element widely distributed in groundwater. First of all, its appearance in water is associated with the dissolution of sulfate and carbonate rocks. The aim of this study was to assess the characteristics of strontium concentration in ancient aluminosilicate deposits that were filled with sedimentogenic brines and seawater in different geological periods. Studies were conducted on 44 water samples, in which the chemical and isotopic composition was determined with the subsequent assessment of saturation indices in relation to the main rock-forming minerals and the residence time of groundwater in the aquifer. It was found that minimal strontium concentrations are characteristic of the least mineralized waters and arise mainly due to the dissolution of carbonates. After their saturation in relation to calcite, the process of carbonate dissolution was replaced by their precipitation and an increase in silicate dissolution with an increase in strontium concentration in more mineralized waters. The incongruent dissolution of aluminosilicates resulted in the appearance of new clay minerals in the aquifer, which together with iron hydroxides and newly formed calcium carbonates created opportunities for sorption and ion exchange processes. The contribution of seawater consisted of an increase in strontium concentrations by approximately 15–20%. The effect of the duration of the water–rock interaction on strontium concentrations in groundwater was expressed in the fact that over a thousand years they increased by 0.1 mg/L, which is 20–30 times less than in the waters of carbonate deposits located 100 km to the east. An assessment of the non-carcinogenic risk to human health of contact with the groundwater showed the safety of using the studied groundwater for drinking purposes. Full article

There now exists two decades of basin-wide coverage of Arctic sea ice from three dedicated polar-orbiting altimetry missions (ICESat, CryoSat-2, and ICESat-2) launched by NASA and ESA. Here, we review our retrieval approaches and discuss the composite record of Arctic ice thickness (2003–2023) after appending two more years (2022–2023) to our earlier records. The present availability of five years of snow depth estimates—from differencing lidar (ICESat-2) and radar (CryoSat-2) freeboards—have benefited from the concurrent operation of two altimetry missions. Broadly, the dramatic volume loss (5500 km³) and Arctic-wide thinning (0.6 m) captured by ICESat (2003–2009), primarily due to the decline in old ice coverage between 2003 and 2007, has slowed. In the central Arctic, away from the coasts, the CryoSat-2 and shorter ICESat-2 records show near-negligible thickness trends since 2007, where the winter and fall ice thicknesses now hover around 2 m and 1.3 m, from a peak of 3.6 m and 2.7 m in 1980. Ice volume production has doubled between the fall and winter with the faster-growing seasonal ice cover occupying more than half of the Arctic Ocean at the end of summer. Seasonal ice behavior dominates the Arctic Sea ice’s interannual thickness and volume signatures. Full article

Along the Arctic and sub-Arctic coasts of Alaska, Siberia, north-eastern China, Hokkaido, Scandinavia and Iceland, people have dressed in clothes or worn shoes made of fish skin for millennia. (Within this article, the terms fish skin and fish leather are used to indicate different processes of the same material. Fish skin: Skin indicates the superficial dermis of an animal. Fish skin is referred to as the historical raw material that is tanned following traditional methods such as mechanical, oiling and smoking tanning, using materials such as bark, brain, urine, fish eggs and corn flour. Fish leather is used to refer that the fish skin has passed one or more stages of industrial vegetable or chrome tanning production and is ready to be used to produce leather goods). These items are often decorated with a rich colour palette of natural dyes provided by nature. In this study, minerals and raw materials of plant origin were collected from riverbanks and processed by Arctic seamstresses who operated as designers, biochemists, zoologists, and climatologists simultaneously. During our research, an international team of fashion, tanning and education specialists used local Arctic and sub-Arctic flora from Sweden, Iceland, and Japan to dye fish leather. Several plants were gathered and sampled on a small scale to test the process and determine the colours they generated based on the historical literature and verbal advice from local experts. This paper describes the process and illustrates the historical use of natural dyes by the Arctic groups originally involved in this craft, building on the traditional cultural heritage that has enabled us to develop sustainable dyeing processes. The results are promising and confirm the applicability of these local plants for dyeing fish skins, providing a basis for a range of natural dye colours from local Arctic flora. The aim is to develop a moderate-sized industrial production of fish leather in this colour palette to replace current unsustainable chemical dyeing processes. This project represents an innovation in material design driven by traditional technologies, addressing changes in interactions between humans and with our environment. The results indicate that new materials, processes, and techniques are often the fruitful marriage of fashion and historical research of traditional methods, helping the industry move towards a more sustainable future. Full article