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17 November 2025

Interannual Variability of Sea Ice Dirtiness in the East Siberian Sea Based on Satellite Data

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1
Arctic and Antarctic Research Institute, St. Petersburg 199397, Russia
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Space Research Institute RAS, Moscow 117997, Russia
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Institute for Water and Environmental Problems SB RAS, Barnaul 656038, Russia
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Author to whom correspondence should be addressed.
Geomatics2025, 5(4), 66;https://doi.org/10.3390/geomatics5040066 
(registering DOI)
This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications
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Abstract

Sea ice dirtiness is an important characteristic that is a marker of many processes occurring in sea ice cover throughout the period of ice formation. Data on dirty ice in the Arctic are scarce; the observations are spatially limited as they usually obtained during ship-based expeditions. There are also automated methods for dirty ice detection from satellite data. The paper presents, for the first time, maps of ice dirtiness in the East Siberian Sea based on four-class classification, drawn manually using satellite images in the visible range for the entire available period of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2025. The spatial and temporal variability of dirty ice, as well as the conditions and causes of its formation, are studied. The study reveals that there are sea areas where the ice is always heavily dirty. At the same time, the area and location of dirty ice in the sea varies greatly from year to year. Our analysis of the interannual variability of dirty ice in the East Siberian Sea reveals an increase in dirty ice area, which is associated with the intensification of dynamic processes leading to ice contamination during its formation. The study finds that vast areas of dirty ice are formed immediately after strong wind-wave activity, which induces resuspension of sediments in the shallow water. The influx of ice from the Chukchi Sea also makes a significant contribution to the amount of dirty ice in the East Siberian Sea.

1. Introduction

As early as 1924, the Canadian paleontologist E.M. Kindle [] reviewed the contributing factors for “dirty” ice formation based on observations made by explorers in the North American Arctic. The considered mechanisms for particle entrainment into sea ice included adfreezing of sediments to the fast-ice bottom in shallow waters, slumping of coastal cliffs onto fast ice, spilling of sediment-laden river water on fast ice in the coastal zone, plowing action of ice keels, as well as wind transport from coast onto ice cover. Later, the entrainment processes for sea ice were studied in detail in numerous expeditions to the Arctic and Antarctic regions. Particle entrainment into sea ice due to water turbulence in shallows during fall storms has been studied in Hudson Bay [,] and off the coast of Alaska []. Mechanisms of suspension freezing and incorporation into the bottom of fast ice were studied during the Sixteenth Russian Antarctic Expedition [] and in the Beaufort Sea [,,]. Entrainment due to aeolian processes has been observed in the Canadian fjords [], as well as off the northern coast of Alaska [,] and in the Eurasian Arctic Basin []. The contamination of sea ice by river sediments was considered in []. The formation of dirty sea ice on the Siberian shelf and its transport by the Transarctic Drift was discussed in [] and was also investigated during the international TRANSDRIFT expeditions to the Laptev Sea [] and the MOSAIC expedition [].
Thus, the entrainment processes are described in detail in the above-mentioned works. However, the spatial distribution of dirty ice, together with an indication of the factors that contribute to particle entrainment into a particular area of sea ice, as well as the interannual variability in the location and distribution of dirty ice, remain insufficiently covered.
Currently, no Ice Service in the world produces regular ice maps of dirty ice in the Arctic, as opposed to maps of sea ice concentration, sea ice age, and ice floe size [,,,]. The mapping of dirty ice from satellite images can be performed manually based on visual interpretation. Manual visual interpretation is a labor-intensive process that requires both skills in interpreting sea ice from satellite data and field experience. Therefore, many research teams develop methods for automatic ice interpretation from satellite data to processing of large amounts of information more quickly. In recent years, a number of authors have presented methods for the automatic mapping of dirty ice [,,,]. However, using maps of sea ice concentration and ice age as an analogy, although automatic methods of interpretation exist, the maps compiled by expert analysis are still considered the most reliable. This is due to the fact that there are still no satellite images that cover the entire Arctic daily with high resolution that do not depend on clouds and other natural factors. Therefore, to generate a map of sea ice dirtiness, similarly to when mapping sea ice concentration and age [], experts must use a combination of data sources and apply his interpretation skills together with knowledge of natural processes and analytics. Validation of automatic products with field data reveals many errors that cannot be foreseen by machines.
Dirty ice covers vast areas of the Arctic. In the winter–spring period, sea ice is covered with snow, and therefore the degree of ice dirtiness can be assessed by ship-based observations of overturned ice floes along the ship’s hull or by drilling and analyzing ice cores. In summer, when snow melts, contaminants appear on the ice surface, and become visible in optical satellite images [].
It should be noted that in the mid-20th century, dirty ice in the Arctic shelf seas was mapped from aircraft. Based on Russian ice aerial reconnaissance data, generalized maps of dirty ice location and the degree of ice dirtiness in the Laptev and East Siberian Seas were compiled for 1953–1976. There is only one work that summarizes this information []. The authors average data over 23 years, which provides a general picture of the dirty ice distribution in this period (Figure 1). Undoubtedly, it is necessary to return to the historical data and analyze them for each year separately for a more detailed analysis and comparison with modern data.
Figure 1. Distribution of ice dirtiness in the Laptev and East Siberian Seas in June (a) and July (b) based on AARI aerial reconnaissance data for the period 1953–1976. The degree of ice dirtiness is defined according to a four-class classification []; a black and white classification scheme is used in our study. Red square marks the area of research.
In this paper, we present novel maps of dirty ice distribution in the East Siberian Sea (Figure 2). The maps are drawn manually by the same methodology that was used for compiling maps during ice aerial reconnaissance. The slight difference in the mapping process is due to differences between the satellite image in the visible range and the real ice cover observed during the reconnaissance. This mapping approach includes the visual interpretation of images for the period from snow melt on ice surface until the landfast ice decay, which resulted in one map of dirty ice for each year. The map presents the degree of ice dirtiness based on a four-class classification. This approach shows the dirtiness of the ice upper layer, i.e., sediment particles entrained at the very beginning of new ice formation.
Figure 2. Bathymetric map of the study region according to the International Bathymetric Chart of the Arctic Ocean (IBCAO).
The aim of this work is to develop a method for the visual interpretation of dirty ice on the East Siberian Sea and to determine its spatial and interannual variability. The distribution of dirty ice is important for a number of scientific and practical problems. The presence of impurities in ice affects the albedo of the ice surface, and, accordingly, the processes of ice destruction in summer. Dirty ice affects shipping along the Northern Sea Route, since incorporations change the strength characteristics of ice. Dirty ice can change the values of sea ice concentration determined from the satellite data, primarily data of microwave radiometers. The lower values of ice concentration during the melting period due to the presence of dirty ice [] underestimate actual ice conditions along the shipping route.

2. Materials and Methods

2.1. Data

To investigate shallow areas in the East Siberian Sea, which are the main areas of dirty ice formation, we used the latest version 5.0 of the IBCAO data. Data with a resolution of 400 m were acquired from https://www.gebco.net/ (last accessed: 21 July 2025) [].
To produce the maps of sea ice dirtiness in the East Siberian Sea, we used visible range images made with MODIS on board Terra and Aqua satellites (https://worldview.earthdata.nasa.gov/, last accessed: 10 September 2025). We used composite images from visible bands (Band 1: 0.62–0.67 μm; Band 4: 0.545–0.565 μm; Band 3: 0.459–0.479 μm) with imagery resolution 250 m. To improve the analysis results, we used higher-resolution (30 m) data from Landsat-8 Operational Land Imager (OLI) launched on 11 February 2013, from Vandenberg Air Force Base, California, visible images. Landsat-8 OLI data provided by NASA are available at the website of the United States Geological Survey at https://earthexplorer.usgs.gov/ (last accessed: 21 July 2025). Composite images are made from visible bands (0.48–0.66 µm, bands 2, 3, and 4).
Unfortunately, the data obtained in visible range is limited by weather and daylight conditions, so it does not allow for daily monitoring of the sea ice surface. In this regard, the use of microwave data seems to be promising as long as it is independent of illumination and weather (clouds, precipitation) []. Microwave remote sensing methods include microwave radiometry [], active data collection using SAR [], and the GNSS-R technique [,]. These methods are used for the detection of sea ice, the determination of sea ice extent, concentration, and thickness, and the determination of snow depth, as well as the localization of ice objects, such as polynyas, leads, etc. Microwave remote sensing methods are based on the theory of interaction between microwave radiation and heterogeneous dispersed natural media [,]. The main parameter of a natural medium which characterizes the process of this interaction is the complex permittivity []. The complex permittivity of contaminants (sediments, terrigenous deposits) lying on the surface of sea ice has similar values to those of metamorphosed snow and wet snow/ice cover [,]. For this reason, the use of microwave data for identifying dirty ice areas does not seem possible. Therefore, only visible-range data were used in this research for the detection of dirty ice areas.
We also used data from Sentinel-1 radar satellites (http://north.seaice.dk/, last accessed: 10 September 2025) for the visual tracking of sea ice drift during the winter prior to the mapping period to determine the source area and formation conditions of dirty ice.

2.2. Methodology for Determining the Degree of Sea Ice Dirtiness

Dirty ice may be of a different color and color intensity. The Russian nomenclature of sea ice classifies sea ice into four classes according to the degree of ice dirtiness; the degree of ice dirtiness indicates the area occupied by dirty ice and is estimated visually: 0—clean ice, only minor traces of impurities are observed; 1—low dirtiness/slightly dirty ice, where the area of dirty ice is less than 1/3 of the observed ice cover area; 2—medium dirtiness/moderately dirty ice, where the area of dirty ice is from 1/3 to 2/3 of the observed ice cover area; 3—high dirtiness/heavily dirty ice, where more than 2/3 of ice cover surface is dirty []. The visual interpretation of ice dirtiness is a semi-quantitative estimation. The error in visual observation is considered to be 10% according to []. The classification of ice dirtiness was developed for ice aerial reconnaissance in the mid-20th century. In our study, we use the same classification because the visual ice estimations from both aircraft and visible-light satellite images are similar; furthermore, the same approach allows for a future comparison of current level of ice dirtiness with ice aerial reconnaissance data from the pre-satellite period.
Table 1 presents examples of different classes of ice dirtiness from expedition photographs and Landsat and MODIS satellite imagery.
Table 1. Classes of sea ice dirtiness with examples.
Dirty ice is mapped via visual interpretation from Terra MODIS images in the visible range (https://worldview.earthdata.nasa.gov/, last access data: 15 July 2025). All maps of ice dirtiness for the entire period from 2000 to 2025 were performed by a single expert and subsequently verified by the second expert. Both experts have extensive experience working in the Arctic (specialized ship-based ice observations from icebreakers and ice-class vessels) and producing ice charts for the Arctic and Antarctic Research Institute (AARI).
The generalized maps were produced using the same methodology as the AARI ice charts [], but the information was aggregated for a period of 1 month instead of 3 days. Based on satellite images, one generalized map of dirty ice in the East Siberian Sea was performed per year for the period from 2000 to 2025.
Dirty ice becomes distinctive in visible images only with the onset of summer melt, when the snow cover melts away from the ice surface. In the East Siberian Sea, the melting begins in the coastal areas in the south and gradually extends to the northern boundary of the sea over 3–4 weeks. It should be taken into account that cloudless images of the entire sea area are extremely rare. Thus, the process of depicting each year is carried out, on average, based on images for the period from 1 June to 10 July. Before 1 June, the ice surface is still covered with snow, and after 10 July, intensive ice melting begins, and break-out events and ice drift occur, so it is extremely difficult to identify dirty areas. The generalized map of ice dirtiness is compiled by analyzing cloudless areas of the sea from visible satellite images for different days.
Figure 3, Figure 4 and Figure 5 show the workflow for interpreting sea ice dirtiness in the East Siberian Sea for the year 2025. In the studied region, snow cover conceals any dirty ice until early June. Most of the dirty ice area becomes visible in mid-June and reaches its maximum in late June–early July. Within this period, the East Siberian sea ice cover still contains both landfast and drifting ice. Drifting ice forms during fall increase throughout the winter and remain within the sea almost in the area of their formation. From early July, the ice starts to break up and moves northward, away from the East Siberian Sea. Therefore, the images from June to July provide the most complete information for identifying the maximum area of dirty ice extent in the East Siberian Sea, because this is the only period when dirty ice can be clearly seen in satellite images. Until the landfast ice breakup begins in June, dirty ice is still located in the region where it entrained sediments and other contaminants. That is why we can learn the prevalent conditions during the period of ice contamination. Therefore, the maps of ice dirtiness can be used for further study of dirty ice formation, the various degrees of dirtiness, and the intensity of these process.
Figure 3. Photographs from Khatanga Bay on 31 March 2017 (photo by V.A. Borodkin): photograph (a) shows a large stamukha with dirty-ice blocks; red square highlights the area, which is enlarged in photograph (b) to illustrate the dirty-ice blocks at a larger scale; photograph (c) shows an ice core sample drilled 100 m from the stamukha; red square highlights the dirty ice layer of the core, which is enlarged in photograph (d).
Figure 4. Study area in Khatanga Bay in March–May 2017 (red polygon) in the Landsat image (a) from 20 June 2017, and the MODIS image (b) from 28 June 2017. Red circle highlights the location of the sample site presented in Figure 3.
Figure 5. Delineated areas with various degrees of dirtiness in the East Siberian Sea in 2021: (a)—manually delineated areas of dirty ice overlain on MODIS image from 6 June 2025; (b)—manually delineated areas of dirty ice overlain on MODIS image from 20 June 2025; (c)—final map of ice dirtiness in 2021. 0—clean ice, 1—slightly dirty ice, 2—moderately dirty ice, 3—heavily dirty ice. Gray color scale is the same as on the Figure 1.
The interpretation of sea ice dirtiness was developed and implemented in several stages:
  • The development of a visual interpretation methodology. For this purpose, ice experts visually examined ice photographs obtained from icebreakers and polar stations, and associated them with satellite images. Examples are presented in Table 1 and Figure 3 and Figure 4. A quantitative comparison of the dirty ice area in the images was not performed. Instead, we accumulated experience in identifying sea ice of various degrees of dirtiness in satellite images. Figure 4 shows the area in Khatanga Bay in the Laptev Sea, where sea ice studies were carried out. Unfortunately, similar studies were not conducted in the East Siberian Sea; however, the interpretation of sea ice surface dirtiness does not depend on the region where the method was developed and tested. From March to May 2017, several ice sampling sites were established in Khatanga Bay to study stamukhas and pressure ridges. The study area is shown in Figure 4. Photographs of sea ice on one of the sample sites (highlighted by the red circle in Figure 4) are shown in Figure 3 as an example. Figure 3a,b show a large stamukha consisting of blocks with dirty layers of up to 50 cm thick. Figure 3c,d show an ice core drilled 100 m from the stamukha. The core contains a layer of dirty ice at a depth of 15 cm from the surface. This layer becomes visible in satellite images after the total melting of snow cover and the upper surface layer. Figure 4a presents a Landsat image showing dirty ice in the study area. The same areas of dirty ice in the MODIS image have a lighter tone (Figure 4b). Landfast ice in this region is stable throughout the season, from fall formation to summer decay.
  • In this study, Terra and Aqua MODIS imagery from an open source (see Section 2.1) were used to compile maps of ice dirtiness in the East Siberian Sea. A GIS-based visual analysis of satellite images was performed using manual adjustments to contrast and brightness. For each year, MODIS satellite images were downloaded from 1 June to 10 July. The most informative images were selected using the following criteria: (i) melting process had begun, but intensive landfast ice breakup had not yet started, and (ii) the image had cloud-free areas. Since landfast ice is stationary and the drifting ice is still very compact, the main dirty ice zones occupied the same position within the landfast ice or almost the same position within the drifting ice throughout the whole period. Therefore, cloud-free areas from different images over several weeks were delineated and aggregated to interpret dirty ice. Figure 5 shows the workflow of identifying and delineating dirty ice in the East Siberian Sea in 2021.
The interpretation of dirty ice in 2021 was particularly successful because cloud coverage was almost absent over the East Siberian Sea on 21 June. That day, the ice upper layer had already melted significantly but landfast ice had not yet broken up. Therefore, two images were primarily used to compile the map: one from 21 June, showing most of the dirty ice area, and an earlier image from 6 June, showing landfast ice in the southeastern part. Ice dirtiness in some parts of the sea was improved using additional images —from 21 June, 1 July, and 2 July. Thus, five images were used in 2021 for the period from 6 June to 2 July. In other years, cloud cover was much more extensive, so the map had to be compiled from smaller parts using a larger number of images.

2.3. Wind Wave Modeling

Wind wave parameters were calculated using the 3D generation model WAVEWATCH III with the ST1 source term package [,] for the Arctic Ocean at spatial resolution of 24’ × 6’ and for the East Siberian Sea in a nested grid at a spatial resolution of 9’ × 3’.
The WAVEWATCH III model was developed at the US National Centers for Environmental Prediction (NCEP) by H. Tolman et al. [,]. The model is based on the numerical solution of the wave action density balance equation, N = S((σ,θ))/ω (where N is the wave action density and S is the wind wave spectrum), as shown in Equation (1):
∂N/∂t + ∂(Cx,N)/∂x + ∂(Cy,N)/∂y + ∂(Cω,N)/∂ω + ∂(Cθ,N)/∂θ = G/ω,
where Cx, Cy, Cω, and Cθ are the corresponding components of the group velocity, and G is the source term package.
The input wind data for the wave model forcing are the U and V components of wind speed (projections of the wind velocity in the geographical coordinate system, where U is the eastward component and V is the northward component) at a height of 10 m from the ERA5 reanalysis []. The data have a spatial resolution of 0.25° and a 3 h time step. The simulation was performed for the ice-free sea, which was determined from daily sea ice concentration maps produced from Advanced Microwave Scanning Radiometer 2 (AMSR2) data, using the same algorithm as the Advanced Microwave Scanning Radiometer EOS (AMSR-E), with data from 2013 onwards (ARTIST Sea Ice (ASI AMSR2) ver. 5.4. Grid 6.25 km, https://data.seaice.uni-bremen.de/, last accessed: 10 September 2025).

3. Results

3.1. Distribution of Dirty Ice of Various Degrees in the East Siberian Sea

Based on satellite images in the visible range, we produced generalized maps of ice dirtiness in the East Siberian Sea for the entire available period of MODIS data from 2000 to 2025 (Figure 6).
Figure 6. Maps of sea ice dirtiness in the East Siberian Sea for the period from 2000 to 2025, produced by the visual interpretation of MODIS satellite images in visible range.
Figure 6 reveals some general patterns of dirty ice distribution that do not change much from year to year. Heavily dirty ice is formed every year in the Dmitry Laptev Strait and further along the coast to Ayon Island, as well as between the islands of the New Siberian archipelago and to the east of them.
The area and location of dirty ice vary significantly. The interannual variability in the area of dirty ice is shown in Figure 7.
Figure 7. Variability of dirty ice area in the East Siberian Sea (as a proportion of the total water area of the sea) for the period from 2000 to 2025. Variability of ice cover with different degrees of ice dirtiness is shown in gray: light gray—low dirtiness (class 1); gray—medium dirtiness (class 2); dark gray—high dirtiness (class 3). Variability of ice area of any degree of dirtiness and the linear trend (with the linear regression equation and R-squared value) are shown in red.
Figure 7 indicates that the total area of dirty ice in the East Siberian Sea tends to increase, which is associated with the intensification of dynamic processes in the last 20 years. Based on the results, the period 2000–2025 can be divided into years with a small, medium, and large area of dirty ice (Table 2). When analyzing data series for 2000–2025, the obtained values of dirty ice area are divided into three groups using the standard deviation (±0.8 σ from the mean value of the dataset (Xm)). According to the methodology widely used at the Arctic and Antarctic Institute for the analysis of historical data, the values Xn < (Xm − 0.8 σ) correspond to years with a small area of dirty ice, the values Xn > (Xcp + 0.8 σ) correspond to years with a large area of dirty ice, and (Xcp − 0.8 σ) < Xn > (Xcp + 0.8 σ) correspond to years with a medium area of dirty ice [].
Table 2. Years with small, medium, and large areas of dirty ice in the East Siberian Sea (area of dirty ice is shown on the maps of sea ice dirtiness in Figure 6 and estimated as a proportion of the total area of the sea).
In the research period, abnormal values of the area of dirty ice were observed in 2024. In this year, strong wind-wave activity was observed in shallows in the western part of the sea the day before the freeze-up. According to the calculated data, mean wave height was up to 2 m that day. Due to turbid water, an extensive zone of heavily dirty ice was formed. This year is considered separately in Section 2 and Section 3.

3.2. History of Formation of Dirty Ice in the East Siberian Sea

The East Siberian Sea is the shallowest among the Arctic shelf seas. The sea depth does not exceed 60 m almost over the entire sea, and the depth of most of the western part of the sea is less than 20 m (Figure 2). The impact of shallow depths on the ice regime of the sea resulted in extensively fast ice and a large number of stamukhas located in the shallows.
Fast ice formation in the East Siberian Sea began along the coasts of islands and the continent coastline in October. Sea depth in the area of fast ice formation varies from 3 to 16 m. This area is located in a shallow zone and is influenced by the river waters. Fast ice begins to form when the thickness of young ice is 6–8 cm; in the first days of ice formation, it can expand or break up within the large range depending on the wind speed and direction [].
Our study presents the history of fast-ice formation using the year 2024 as an example (Figure 8). The comparison of landfast ice edges on different days in late 2023 and early 2024 (Figure 8a) with the map of ice dirtiness in 2024 (Figure 8b) reveals similar patterns. This indicates that the contamination was entrained at the very beginning of the ice formation both near the coast (landfast ice) and in the seaward part (drifting ice). Thus, we can study the history of sea ice formation and the accompanying hydrometeorological conditions to learn the conditions for the formation of ice with a particular degree of dirtiness. And, conversely, studying dirty ice at the beginning of the summer melting period provides knowledge of the dynamic processes associated with the formation and development of sea ice cover.
Figure 8. Ice chart showing landfast ice edges at different stages during the ice season 2023/2024 (a)—red line indicates the edge of landfast ice on 24 October 2023; blue line indicates the edge of landfast ice in 21 November 2023. Figure (b) shows the aggregated map of ice dirtiness in June 2024.

3.3. Interrelation with Data on Wind Generated Waves

Unfortunately, there is scarce in situ data on the processes occurring in the water column in the Arctic seas during the winter period. A reconstruction of the conditions of fast ice formation can be obtained using model calculations.
Together with mapping sea ice dirtiness, we calculated wind waves for the same seasons from the beginning of landfast ice formation until the sea was fully covered with ice. An example of this calculation is shown in Figure 9.
Figure 9. Calculated wave height on 22 October 2023 (a) and sea ice dirtiness map for June 2024 (b). Waves are calculated for the ice-free water area (ice cover is marked in yellow). Numbers on the isolines indicate the wave height in meters.
Based on model calculations of wind waves during the fall ice formation of the previous year and summer maps of ice dirtiness, it is proven that large areas of dirty ice are formed immediately after wind-wave activity in the water area.
Figure 9 provides a good example of such a phenomenon. On 22 October 2023, that is, the day before the freeze-up of this water area, a sufficiently strong wind-wave activity with a wave height of 1.5–2 m was observed. This strong wave led to the resuspension of sediments in shallow water, and, accordingly, to favorable conditions for dirty ice formation.
Sentinel-1 and MODIS satellite images are used to confirm that the dirty ice in this area in October 2023 did not drift from other parts of the sea during the winter–spring season. Based on these images, we defined the trajectory of individual ice fields from the second ten-day period of October 2023 to the second ten-day period of June 2024. Retrospective visual analysis of the drift confirms that the anomalous area of ice with high ice dirtiness in the western part of the sea was formed in that area in the second ten-day period of October and persisted throughout the season, slightly moving to the north.
The initial analysis of the calculated wave height during the ice formation period shows that in the shallow-water areas of the East Siberian Sea with high dirtiness, a wave height of more than 1.5 m was observed 1–3 days prior to the ice formation. The question of the conditions that promote dirty ice formation in different sea areas requires careful analysis, which will be carried out in our further research.

4. Discussion

Maps of ice dirtiness provide great opportunities for analyzing the dynamics and formation of sea ice throughout the season. Ice dirtiness is one of the clearly visible characteristics of sea ice. Sediment or other particles are incorporated into ice during the freeze-up period, which can be used to learn the history of ice formation, as well as other ice characteristics. The aggregated maps of ice dirtiness in the East Siberian Sea can be used for a thorough analysis of the hydrometeorological processes that occurred during each season from the beginning of ice formation to learn the following: the features of ice formation in each area of the sea, the causes and conditions of dirty ice formation, the contribution of both local contamination and rafted by drifting ice from other areas, the interannual variability of ice dirtiness in each area, and the impact of climate change on the area of dirty ice, etc.
In this article, we present a preliminary analysis of the results based on the available data.

4.1. Specific Features of Sea Ice Formation in Different Parts of the Sea; Causes and Conditions of Dirty Ice Formation

Based on the means of entrainment of solid mineral particles into the ice, the entrainment processes can be divided into two distinct types: “from the top”, that is, from the air, and “from the bottom”, that is, from the water.
The entrainment “from the top” is associated with the particles’ transport and deposition on the ice surface and includes dust particles from the atmosphere, mineral particles coming from the coast with wind (land erosion), and particles that originated from volcanic eruptions, as well as cosmic dust and anthropogenic pollution from industrial and domestic facilities, transport, etc. These sources of particles are characterized by heterogeneous surface coverage and varying concentrations of inclusions per unit area. The influx of soil particles as a result of land erosion provides the major contribution to the entrainment from the top. However, the impact of this entrainment is limited from several tens of meters to several kilometers off the coastline. In winter, the intensity of such entrainment decreases due to snow cover on the land surface. However, despite the fact that land erosion is the most significant source of entrainment from the top, its contribution is insignificant compared to the entrainment of particles from the bottom [,].
The entrainment of particles from the bottom, i.e., from water, occurs through the arrival of mineral particles to the crystallization front at the ice bottom, where they are frozen into the ice as inclusions [,,,]. Mineral particles can be uplifted to the ice bottom directly from the water or together with frazil ice on the bottom or in the water column [,,,,]. The number of particles entrained by ice directly from the water depends on the amount of suspended matter in the water column, which is determined by the turbidity of water. Greater water turbidity due to the presence of clay or sand particles leads to the enrichment of mineral solid particles in the ice. As a rule, in cases of enhanced turbidity, the distribution of particles within the entire thickness of the ice floe is more or less homogenous. Sea ice that forms under these conditions has a higher level of dirtiness in the newly formed ice layer [].
In the estuary areas, the water turbidity increases with the increase in the turbidity in the river upstream, especially during periods of precipitation, as well as spring and fall floods. In the coastal part, the increase in water turbidity is due to wave activity. The presence of ice and ice pressure ridges promotes increasing water turbidity in the areas where the ice is grounded. Ice keels can extend several meters below the water level and plow bottom in shallows while moving, raising waves of silt and solid mineral particles from the bottom. A similar process is created by iceberg fragments moving along shallows or along the shore. The shift in stamukhas along the bottom due to tidal fluctuations and wind-induced surges also increases local turbidity. All of the above factors contribute to the flow of mineral solid inclusions from below and promote areas of local areas of enhanced dirtiness in ice cover.
The manifestation of dirtiness on the ice surface in spring and summer does not occur simultaneously, but is extended in time. At first, melting snow cover hides the particles that have fallen on the ice surface. Solid particles absorb heat and begin to heat the underlying ice, making a network of thawing holes, which weakens the ice cover. After the snow melts and the ice begins to melt, dirty layers from the underlying ice horizons begin to appear on the surface []. As the ice in the upper layers melts, more and more impurities rise to the surface, join together with the already visible dirty areas, and thus form vast fields of dirty ice. These dirty ice fields are already well identified from satellite images.
The maps of dirty ice in the East Siberian Sea (Figure 6) therefore indicate the dirtiness of the upper layers of ice. In each year studied, there is a strip of heavily dirty ice along the continental coast and between the islands of the New Siberian Archipelago, which is caused by the increased water turbidity in shallow areas due to waves and the discharge of the large Kolyma and Indigirka rivers. The configuration of dirty ice zones off the coast changes significantly from year to year as a result of the different conditions of ice formation in open water, i.e., prevailing drift direction in the winter–spring period and strong wave activity prior to and during ice formation. In the southwestern part of the sea, a large number of stamukhas are formed annually, which are initially formed during intensive dynamic processes, and subsequently contribute to the turbulence of water around them during the entire period of their existence in shallow water.

4.2. Contribution of Local Dirty Ice Formation and Dirty Material Rafted by Drifting Ice from Other Areas: Interannual Variability

In addition to the dirty ice of local formation, a significant contribution to the amount of dirty ice in the East Siberian Sea is made by the influx of dirty ice from the Chukchi Sea. Such ice exported from the east is observed every year with varying intensity. The objectives of this study do not include tracking all trajectories of dirty ice drift, since it is necessary to define complex ice drift throughout the Arctic, but not to consider one sea separately. However, even at the first approximation, a vast zone of dirty ice in the Chukchi Sea, with a tongue entering the East Siberian Sea through the Long Strait and the north of Wrangel Island is clearly visible on satellite images (Figure 10). It is likely that areas of dirty ice may form within the Chukchi Sea, but the authors [] point out that the Beaufort Sea serves as a significant source of dirty ice in the Chukchi Sea.
Figure 10. Dirty ice drifting from the Chukchi Sea to the East Siberian Sea: (a) Terra MODIS image from 14 June 2019; (b) Terra MODIS image from 27 June 2019. Red arrows show the drift of individual clearly identified ice floes over 13 days from 14 June to 27, red line depicts the area of dirty ice drifted from the Chukchi Sea.
It is also interesting to compare the obtained maps of ice dirtiness for the period 2000–2025 with the maps of ice dirtiness for the period 1953–1976 [] that were based on aerial ice reconnaissance data (Figure 1). The distribution of dirty ice in June 1953–1976 (Figure 1a) has common patterns with the period 2000–2025 (Figure 6). In June, fast ice and drifting ice, which accumulated throughout the winter period, still remain in the East Siberian Sea and occupy most of the sea; accordingly, dirty ice in June is mainly located in the same areas where it formed. In July, fast ice is intensively destroyed and rafted north by drifting ice. In Figure 1b, it is clear in July that the strip of the most heavily dirty ice crosses the sea from west to east. This strip in the southwestern part consists of ice that moved from the south of the continental coast, and the northwestern part of this strip is ice that drifted from the east from the Chukchi and Beaufort Seas. This is confirmed by the general scheme of Arctic ice circulation presented in the work of Gordienko and Laktionov [] (Figure 11).
Figure 11. Generalized sea-ice circulation in the Arctic Ocean, after Gordienko and Laktionov []. Dark gray area indicates potential sediment source areas (shallow areas < 30 m).
Comparison of the periods 1953–1976 and 2000–2025 reveals that, in 1953–1976, the most dirty ice drifted from the east considerably northward, to the north of Wrangel Island. In 2000–2025, a more intense export of dirty ice occurred farther south, toward Long Strait. Most of the dirty ice that entered Long Strait probably originated from Kotzebue Sound, while the ice drifting westwards north of Wrangel Island originated from the Beaufort Sea. The Chukchi Sea is a deep-sea, hence it cannot serve as the main source of dirty ice. Thus, in 1953–1976, the ice captured by the Beaufort Gyre moved more intensively to the northern part of the East Siberian Sea than in 2000–2025. In 2000–2025, the southern transport from Kotzebue Sound through Long Strait to the East Siberian Sea was more intense. Reference [] states that negative trends in ice drift velocity are observed in the Chukchi Sea and in some parts of the East Siberian Sea, but due to the lack of satellite data before 1979, the authors consider only the period since 1979.
Based on the produced maps of sea ice dirtiness in the East Siberian Sea, the areas of dirty ice to the east of the 170 E were calculated separately (Figure 12). In the eastern part of the sea, the depths are greater and less dirty ice formed. Most of the dirty ice in this part of the sea originates from the Chukchi Sea and the Beaufort Sea.
Figure 12. Area of dirty ice in the entire East Siberian Sea (solid red line; dashed line depicts linear trend with the linear regression equation and R-squared value) and in the part of the sea east of 170 E (solid blue line; dashed line depicts linear trend with the linear regression equation and R-squared value). The area is calculated as a percentage of the total area of the East Siberian Sea.
Figure 12 shows that the amount of dirty ice in the eastern part of the East Siberian Sea tended to increase throughout the study period. An analysis of the configuration of dirty ice zones from the maps (Figure 6) indicates that the enhanced area of dirty ice in the east is due to drift, and not to local ice formation in this area.
The obtained results on the interannual variability of ice dirtiness are consistent with studies that indicate the intensification of dynamic processes in the Arctic due to climate change and a decrease in the extent of sea ice [,].

5. Conclusions

Based on a manual interpretation of satellite images in the visible range for the melting periods 2000–2025, we produced generalized maps of dirty sea ice in the East Siberian Sea. The East Siberian Sea was chosen for the study because it is the shallowest arctic sea with the largest area of landfast ice. Landfast ice is of primary interest in the context of dirty ice, since it is possible to learn the conditions at the beginning of ice formation and the entrainment processes throughout the winter–spring season in the study area.
We have developed a method for mapping sea ice of varying degrees of dirtiness on a four-class classification using satellite images. The method combines the principles of ice aerial reconnaissance, which was carried out in the Arctic seas in the mid-20th century, together with the principles of compiling the generalized ice charts. The resulting maps of ice dirtiness in the East Siberian Sea make it possible to study the spatial and temporal variability of dirty ice for the first time.
The study reveals regions with heavily dirty ice that occur every year—this is the Dmitry Laptev Strait and the region further along the mainland coast to Ayon Island, between the New Siberian Islands and to the east of them. At the same time, the area and location of dirty ice in the sea vary greatly from year to year.
The interannual analysis showed that the total area of dirty ice tends to increase, which is associated with the intensification of dynamic processes in the Arctic over the past 20 years. The years for the period from 2000 to 2025 are divided into years with small, medium, and large areas of dirty ice in the East Siberian Sea. A small area of dirty ice was observed in 2000, 2003, 2006–2008, and 2011; a medium area was observed in 2001, 2004–2005, 2009, 2010, 2012, 2014–2017, 2022, and 2025; and a large area was observed in 2002, 2013, 2017, 2019–2021, and 2023–2024. It should be noted that the area of dirty ice in 2024 was abnormally large, accounting for 68% of the entire area of the East Siberian Sea.
The dirty ice that is visible in satellite images during the melting period is formed at the very beginning of landfast ice formation during fall. Dirty ice forms the upper layer of the ice floe. Thus, the history of landfast ice formation and the accompanying hydrometeorological information can show suitable conditions for the formation of sea ice of a specific degree of dirtiness. We calculated wind wave for each year from the beginning of landfast ice formation until the total cover of sea area with ice. It is shown that large areas of dirty ice are formed immediately after strong waves in the water area, such as in 2024. Strong waves lead to sediment resuspension in shallow water. More suspended particles in the water promote the greater suspension of freezing and enrichment of particles in newly forming ice. Suspended sediments in shallow water are not the only cause of dirty ice, as there are a number of other sources, such as river runoff, coastal erosion, uplifted anchor ice, stamukhas, etc. However, it is suspension freezing in shallow water that leads to the formation of large areas of dirty ice, which are clearly visible in optical satellite images.
The analysis of the area of dirty ice in the East Siberian Sea over the period 2000–2025 also reveals an increase in dirty ice area, both locally sourced and exported from the Chukchi Sea.
This study offers great opportunities for learning the processes within the ice. Dirtiness in this case acts as a marker of these processes. The authors will continue to interpret the dirty ice zones in other Arctic seas and study the processes that affect the entrainment of particles into ice. The involvement of many other available hydrometeorological data, both calculated and in situ, will allow for a detailed study of the conditions of dirty ice formation and the drift patterns of dirty ice in the Arctic.

Author Contributions

Conceptualization, T.A. and V.B.; methodology, T.A., E.A. and J.S.; software, V.A. and P.K.; formal analysis, T.A., V.B., E.P., E.A., J.S., V.T. and A.E.; investigation, T.A., V.B., E.P., E.A., J.S. and V.T.; writing—original draft preparation, T.A. and V.B.; writing—review and editing, A.E.; visualization, E.P., E.A. and J.S.; supervision, T.A.; project administration, T.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Russian Science Foundation, grant number 23-17-00161.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Kindle, E.M. Observations on ice-borne sediments by the Canadian and other arctic expeditions. Am. J. Sci. 1924, 7, 249–286. [Google Scholar] [CrossRef]
  2. Campbell, N.J.; Collin, A.E. The discoloration of Foxe Basin ice. J. Fish. Res. Board Can. 1958, 15, 1175–1188. [Google Scholar] [CrossRef]
  3. Barber, D.G.; Harasyn, M.L.; Babb, D.G.; Capelle, D.; McCullough, G.; Dalman, L.A.; Matthes, L.C.; Ehn, J.K.; Kirillov, S.; Kuzyk, Z.; et al. Sediment-laden sea ice in southern Hudson Bay: Entrainment, transport, and biogeochemical implications. Elem. Sci. Anth. 2021, 9, 1. [Google Scholar] [CrossRef]
  4. Eicken, H.; Gradinger, R.; Gaylord, A.; Mahoney, A.; Rigor, I.; Melling, H. Sediment transport by sea ice in the Chukchi and Beaufort Seas: Increasing importance due to changing ice conditions? Deep.-Sea Res. II 2005, 52, 3281–3302. [Google Scholar] [CrossRef]
  5. Cherepanov, N.V.; Kozlovskiy, A.M. Underwater ice in the coastal waters of Antarctica. Sov. Antarct. Inf. Bull. Engl. Transl. 1972, 8, 335–338. [Google Scholar]
  6. Barnes, P.W.; Reimnitz, E.; Fox, D. Ice rafting of finegrained sediment, a sorting and transport mechanism, Beaufort Sea, Alaska. J. Sediment. Petrol. 1982, 52, 493–502. [Google Scholar]
  7. Reimnitz, E.; Kempema, E.W.; Barnes, P.W. Anchor ice, seabed freezing, and sediment dynamics in shallow Arctic seas. J. Geophys. Res. 1987, 92, 14671–14678. [Google Scholar] [CrossRef]
  8. Kempema, E.W.; Reimnitz, E.; Barnes, P.W. Sea ice sediment entrainment and rafting in the Arctic. J. Sediment. Petrol. 1989, 59, 308–317. [Google Scholar]
  9. Gilbert, R. Sedimentary processes of Canadian arctic fjords. Sediment. Geol. 1983, 36, 147–175. [Google Scholar] [CrossRef]
  10. Reimnitz, E.; Maurer, D.K. Eolian sand deflation—A cause for gravel barrier islands in arctic Alaska? Geology 1979, 7, 507–510. [Google Scholar] [CrossRef]
  11. Darby, D.A.; Burckle, L.H.; Clark, D.L. Airborne dust on the Arctic pack ice, its composition and fallout rate. Earth Planet. Sci. Lett. 1974, 24, 166–172. [Google Scholar] [CrossRef]
  12. Pfirman, S.; Gascard, J.C.; Wollenburg, I.; Mudie, P.; Abelmann, A. Particle-laden Eurasian Arctic sea-ice: Observations from July and August 1987. Polar Res. 1989, 7, 59–66. [Google Scholar] [CrossRef]
  13. Gordeev, V.V. Fluvial sediment flux to the Arctic Ocean. Geomorphology 2006, 80, 94–104. [Google Scholar] [CrossRef]
  14. Eicken, H.; Kolatschek, J.; Freitag, J.; Lindemann, F.; Kassens, H.; Dmitrenko, I. A key source area and constraints on entrainment for basin- scale sediment transport by Arctic sea ice. Geophys. Res. Lett. 2000, 27, 1919–1922. [Google Scholar] [CrossRef]
  15. Wegner, C.; Wittbrodt, K.; Hoelemann, J.A.; Janout, M.A.; Krumpen, T.; Selyuzhenok, V.; Novikhin, A.; Polyakova, Y.; Krykova, I.; Kassens, H.; et al. Sediment entrainment into sea ice and transport in the Transpolar Drift: A case study from the Laptev Sea in winter 2011/2012. Cont. Shelf Res. 2017, 141, 1–10. [Google Scholar] [CrossRef]
  16. Krumpen, T.; Birrien, F.; Kauker, F.; Rackow, T.; von Albedyll, L.; Angelopoulos, M.; Belter, H.J.; Bessonov, V.; Damm, E.; Dethloff, K.; et al. The MOSAiC ice floe: Sediment-laden survivor from the Siberian shelf. Cryosphere 2020, 14, 2173–2187. [Google Scholar] [CrossRef]
  17. DMI Danish Meteorological Institute. Available online: http://ocean.dmi.dk/arctic/icecharts.uk.php (accessed on 6 September 2025).
  18. CRYO Norwegian Meteorological Institute. Available online: https://cryo.met.no/ (accessed on 6 September 2025).
  19. Government of Canada. Available online: https://iceweb1.cis.ec.gc.ca/Archive/page1.xhtml (accessed on 6 September 2025).
  20. National Centers for Environmental Information. Available online: https://www.ncei.noaa.gov/ (accessed on 6 September 2025).
  21. Huck, P.; Light, B.; Eicken, H.; Haller, M. Mapping sediment-laden sea ice in the Arctic using AVHRR remote-sensing data: Atmospheric correction and determination of reflectances as a function of ice type and sediment load. Remote Sens. Environ. 2007, 107, 484–495. [Google Scholar] [CrossRef]
  22. Zhang, N.; Wu, Y.; Zhang, Q. Detection of sea ice in sediment laden water using MODIS in the Bohai Sea: A CART decision tree method. Int. J. Remote Sens. 2015, 36, 1661–1674. [Google Scholar] [CrossRef]
  23. Waga, H.; Eicken, H.; Light, B.; Fukamachi, Y. A neural network-based method for satellite-based mapping of sediment-laden sea ice in the Arctic. Remote Sens. Environ. 2022, 270, 112861. [Google Scholar] [CrossRef]
  24. Yuanyang, X.; Tingting, L.; Na, L.; Ruibo, L. Changes in area fraction of sediment-laden sea ice in the Arctic Ocean during 2000 to 2021. Acta Oceanol. Sin. 2024, 43, 81–92. [Google Scholar] [CrossRef]
  25. Afanasyeva, E.V.; Alekseeva, T.A.; Sokolova, J.V.; Demchev, D.M.; Chufarova, M.S.; Bychenkov, Y.D.; Devyataev, O.S. AARI methodology for sea ice charts composition. Russ. Arct. 2019, 7, 5–20. [Google Scholar] [CrossRef]
  26. Alekseeva, T.A.; Sokolova, J.V.; Afanasyeva, E.V.; Tikhonov, V.V.; Raev, M.D.; Sharkov, E.A.; Kovalev, S.M.; Smolyanitsky, V.M. The Contribution of sea-ice contamination to inaccuracies in sea-ice concentration retrieval from satellite microwave radiometry data during the ice-melt period. Izvestiya. Atmos. Ocean. Phys. 2022, 58, 1470–1484. [Google Scholar] [CrossRef]
  27. Smolyanitsky, V.; Karelin, I.; Karklin, V.; Ivanov, B. Ice conditions, albedo, surface contamination and ice mass exchange. In Oceanography of the ESS, Proceedings of the ESSS Workshop in Malaga, Spain, 11–18 October 2003; International Arctic Research Center: Fairbanks, AK, USA, 2003. [Google Scholar]
  28. Alekseeva, T.A.; Sokolova, J.V.; Tikhonov, V.V.; Smolyanitsky, V.M.; Afanasyeva, E.V.; Raev, M.D.; Sharkov, E.A. Analysis of sea-ice areas undetectable by the ASI algorithm ASI algorithm based on satellite microwave radiometry in the Arctic Ocean. Izv. Atmos. Ocean. Phys. 2021, 57, 1690–1704. [Google Scholar] [CrossRef]
  29. Jakobsson, M.; Mohammad, R.; Karlsson, M.; Salas-Romero, S.; Vacek, F.; Heinze, F.; Bringensparr, C.; Castro, C.F.; Johnson, P.; Kinney, J.; et al. The International Bathymetric Chart of the Arctic Ocean Version 5.0. Sci. Data 2024, 11, 1420. [Google Scholar] [CrossRef]
  30. Tedesco, M. Remote Sensing of the Cryosphere; JohnWiley & Sons: Oxford, UK, 2015; 404p. [Google Scholar]
  31. Tikhonov, V.V.; Raev, M.D.; Sharkov, E.A.; Boyarskii, D.A.; Repina, I.A.; Komarova, N.Y. Satellite microwave radiometry of sea ice of polar regions: A review. Izv. Atmos. Ocean. Phys. 2016, 52, 1012–1030. [Google Scholar] [CrossRef]
  32. Dabboor, M.; Olthof, I.; Mahdianpari, M.; Mohammadimanesh, F.; Shokr, M.; Brisco, B.; Homayouni, S. The RADARSAT Constellation Mission Core Applications: First Results. Remote Sens. 2022, 14, 301. [Google Scholar] [CrossRef]
  33. Yan, Q.; Huang, W. Sea Ice Remote Sensing Using GNSS-R: A Review. Remote Sens. 2019, 11, 2565. [Google Scholar] [CrossRef]
  34. Yu, K. Theory and Practice of GNSS Reflectometry; Springer: Berlin/Heidelberg, Germany, 2021. [Google Scholar] [CrossRef]
  35. Tsang, L.; Kong, J.A.; Shin, R.T. Theory of Microwave Remote Sensing; John Wiley & Sons: Hoboken, NJ, USA, 1985. [Google Scholar]
  36. Ulaby, F.T.; Long, D.G. Microwave Radar and Radiometric Remote Sensing; University of Michigan Press: Ann Arbor, MI, USA, 2014; 984p. [Google Scholar]
  37. Bohren, C.F.; Huffman, D.R. Absorption and Scattering of Light by Small Particles; Wiley-Interscience: New York, NY, USA, 1983; p. 530. [Google Scholar]
  38. Lohanick, A.W.; Grenfell, T.C. Variations in Brightness Temperature over Cold First-Year Sea Ice near Tuktoyaktuk, Northwest Territories. J. Geophys. Res. 1986, 91, 5133–5144. [Google Scholar] [CrossRef]
  39. Special ship ice observations. In Methodological Manual; Alekseeva, T.A., Ed.; AARI: St. Petersburg, Russia, 2025; 48p. (In Russian) [Google Scholar]
  40. Tolman, H.L. The Numerical Model WAVEWATCH a Third Generation Model for Hindcasting of Wind Waves on Tides in Shelf Seas; Communications on Hydraulics and Geotechnical Engineering; TU Delft. Report 89-2; Faculty of Civil Engineering, Delft University of Technology: Delft, The Netherlands, 1989; 72p. [Google Scholar]
  41. Tolman, H.L.; Chalikov, D.V. Source Terms in a Third-Generation Wind Wave Model. J. Phys. Oceanogr. 1996, 26, 2497–2518. [Google Scholar] [CrossRef]
  42. The WAVEWATCH III Development Group (WW3DG): User Manual and System Documentation of WAVEWATCH III Version 5.16; Tech. Note 329; NOAA/NWS/NCEP/MMAB: College Park, MD, USA, 2016; Volume 361.
  43. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz, S.J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  44. Brovin, A.I.; Yulin, A.I. On the issue of distinguishing gradations of anomaly in the series of hydrometeorological elements. Tr. AARI 1990, 423, 84–88. (In Russian) [Google Scholar]
  45. Karelin, I.D.; Karklin, V.P. Fast Ice and Near-Ice Polynyas of the Arctic Seas of the Siberian Shelf at the End of the 20th—Beginning of the 21st Centuries; Arctic and Antarctic Research Inst.: Saint Petersburg, Russia, 2012; 180p. [Google Scholar]
  46. Nomura, D.; Nishioka, J.; Granskog, M.A.; Krell, A.; Matoba, S.; Toyota, T.; Hattori, H.; Shirasawa, K. Nutrient distributions associated with snow and sediment-laden layers in sea ice of the southern Sea of Okhotsk. Mar. Chem. 2009, 119, 1–8. [Google Scholar] [CrossRef]
  47. Ledley, T.S.; Pfirman, S. The impact of sedimentladen snow and sea ice in the Arctic on climate. Clim. Change 1997, 37, 641–664. [Google Scholar] [CrossRef]
  48. Darby, D.A.; Myers, W.B.; Jakobsson, M.; Rigor, I. Modern dirty sea ice characteristics and sources: The role of anchor ice. J. Geophys. Res. Ocean. 2011, 116, C09008. [Google Scholar] [CrossRef]
  49. Ito, M.; Ohshima, K.I.; Fukamachi, Y.; Hirano, D.; Mahoney, A.R.; Jones, J.; Takatsuka, T.; Eicken, H. Favorable conditions for suspension freezing in an arctic coastal polynya. J. Geophys. Res. Ocean. 2019, 124, 8701–8719. [Google Scholar] [CrossRef]
  50. Reimnitz, E.; McCornick, M.; McDougall, K.; Brouwers, E. Sediment esport by ice rafting from a coastal polynya, Arctic Alaska, U.S.A. Arct. Alp. Res. 1993, 25, 83–98. [Google Scholar] [CrossRef]
  51. He´quette, A.; Tremblay, P.; Hill, P.R. Nearshore erosion by combined ice scouring and near-bottom currents in eastern Hudson Bay, Canada. Mar. Geol. 1999, 158, 253–266. [Google Scholar] [CrossRef]
  52. Harasyn, M.L.; Isleifson, D.; Barber, D.G. The Influence of Surface Sediment Presence on Observed Passive Microwave Brightness Temperatures of First-Year Sea Ice during the Summer Melt Period. Can. J. Remote Sens. 2019, 45, 333–349. [Google Scholar] [CrossRef]
  53. Dethleff, D. Entrainment and export of Laptev Sea ice sediments, Siberian Arctic. J. Geophys. Res. 2005, 110, 1–17. [Google Scholar] [CrossRef]
  54. Appel’, I.L.; Gudkovich, Z.M. Ice-cover reflectivity during ice melting in the southeastern part of the Laptev Sea. In Polyarnaya ekspeditsiya Sever-76 (nauchnye rezul’taty) (The Sever-76 Polar Expedition (Scientific Results)); Gidrometeoizdat: Leningrad, Russia, 1979; Volume 2, pp. 27–32. [Google Scholar]
  55. Gordienko, P.A.; Laktionov, A.F. Circulation and physics of the Arctic Basin waters. In Annals of the International Geophysical Year, Oceanography; Gordon, A.L., Baker, F.W.G., Eds.; Elsevier: Amsterdam, The Netherlands, 1969; Volume 46, pp. 94–112. [Google Scholar]
  56. Kaur, S.; Ehn, J.K.; Barber, D.G. Pan-arctic winter drift speeds and changing patterns of sea ice motion: 1979–2015. Polar Rec. 2018, 54, 303–311. [Google Scholar] [CrossRef]
  57. Spreen, G.; Kwok, R.; Menemenlis, D. Trends in Arctic sea ice drift and role of wind forcing: 1992–2009. Geophys. Res. Lett. 2011, 38, L19501. [Google Scholar] [CrossRef]
  58. Kwok, R.; Spreen, G.; Pang, S. Arctic sea ice circulation and drift speed: Decadal trends and ocean currents. J. Geophys. Res. Oceans 2013, 118, 2408–2425. [Google Scholar] [CrossRef]
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