Ice Types Research Articles

Physical Mechanism and Parameterization for Correcting Radar Wave Velocity in Yellow River Ice with Air Temperature and Ice Thickness

Unfrozen free and non-free water between ice crystals in flat and hummock ice in the Yellow River exists as water films with varying contents based on ice temperature. These contents can affect the radar wave velocity of the ice despite its theoretical dependence on the crystal structure and ice body components. The unfrozen water content in ice depends on the ice temperature, which is controlled by the air temperature, solar radiation, and ice thickness. Winter air temperature and radar-detected ice thickness data observed at the Shisifenzi bend in the Yellow River from 2020 to 2021 were analyzed. The unfrozen water content in the ice was the primary factor influencing the accuracy of flat ice thickness detection. The heat flux at the ice–water interface in the Yellow River was determined. The evolution of ice thickness and temperature were simulated using a one-dimensional (1D) ice thermodynamic model forced by the local weather station data (i.e., air temperature, solar radiation, wind speed, and cloud cover). On this basis, the measured ice thickness data of 13 drill holes were combined to calculate 1251 thermodynamically simulated ice thicknesses consistent with the ice thickness detection time of the radar; therefore, statistical relationships regarding the influence of air temperature and the combined action of air temperature and ice thickness on the radar wave velocity in granular and columnar ice during air temperature increases and decreases were determined. Finally, the statistical relationship between the combined influence of air temperature and ice thickness on radar wave velocity was selected as a parameterization scheme to dynamically correct the radar wave velocity of flat ice. To enhance the radar detection accuracy for flat ice thickness, the radar wave velocity of ice was parameterized as a function. Given the presence of unfrozen frazil ice and accumulated broken ice blocks in the Yellow River, radar is suggested to detect the thickness of different types of ice in future research.

The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice

Abstract. The conical–conical reflectance factor (CCRF) has been calculated as an alternative to the bidirectional reflectance distribution function (BRDF) for three types of bare sea ice with varying surface roughness (σ= 0.1–10) and ice thicknesses (50–2000 cm) over an incident solar irradiance wavelength range of 300–1400 nm. The comprehensive study of the CCRF of sea ice presented here is paramount for interpreting sea ice measurements from satellite imagery and inter-calibrating space-borne sensors that derive albedo from multiple multi-angular measurements. The calculations performed by a radiative-transfer code (PlanarRad) show that the CCRF of sea ice is sensitive to realistic values of surface roughness. The results presented here show that surface roughness cannot be considered independently of sea ice thickness, solar zenith angle and wavelength. A typical CCRF of sea ice has a quasi-isotropic reflectance over the hemisphere, associated with a strong forward-scattering peak of photons. Surface roughness is crucial for the location, size and intensity of the forward-scattering peak. As the surface roughness increases, a spreading of the CCRF peak is observed. The hemisphere was split in to 216 quadrangular regions or quads. The peak remains specular for the smaller surface roughnesses (σ=0.001 to σ=0.01), whereas for larger surface roughness features (above σ=0.05), the peak spreads out over multiple quads with a lower intensity than for smaller roughness features, and the highest value is displaced further out on the solar principal plane. Different types of sea ice have a similar pattern with wavelength: the CCRF increases by 30 % from first-year sea ice to multi-year sea ice at 400 nm and up to 631 % at 1100 nm, 32 % from melting sea ice to multi-year sea ice at 400 nm and a maximum of 98 % at 900 nm, and 11 % from melting sea ice to first-year sea ice at 400 nm and up to 86 % at 800 nm. The CCRF calculations presented in this study form the first set of complete CCRF values as an approximation of the BRDF for bare sea ice with a wide range of configurations.

Ground-ice origin and age on Herschel Island (Qikiqtaruk), Yukon, Canada

Glacial legacies preserved in permafrost such as buried glacial ice of the last ice age are of increasing concern in the Western Canadian Arctic. Permafrost collapse due to melting ground ice largely follows the margins of the maximum Laurentide Ice Sheet extent and therefore predetermines the postglacial landscape evolution in this region. Another type of ground ice, (i.e., wedge ice) of late Pleistocene and Holocene age associated with permafrost aggradation, is also widespread here. Our study on Herschel Island (Qikiqtaruk, Beaufort Sea) re-examines previous data and applies stable-isotope and dissolved organic carbon (DOC) analyses as well as radiocarbon dating on DOC and particulate plant macrofossil remains preserved in massive ground ice, wedge ice and host deposits.Newly obtained DOC ages of the massive ice span from 32 220 to 25 830 cal BP and extend the only previously available direct age determination (CO2-derived radiocarbon age of 21 290 cal BP) properly into the Last Glacial Maximum as the formation time of the massive ice. Its newly obtained isotopic composition exhibits mean values of −33.1 ± 0.6‰ in δ18O, of −257 ± 4‰ in δD and 7.6 ± 0.9‰ in deuterium excess (d) fitting into previously reported respective data ranges. The very low (negative) stable isotope composition of the massive ice, the numerous enclosed spherical air bubbles as well as the very low mean DOC content of 0.7 ± 0.1 mg L−1 provide strong evidence for an origin as glacier ice that was buried and has survived since deglaciation.The newly studied wedge ice on Herschel Island formed during the Holocene between 9220 and 3470 cal BP, and shows a distinct isotopic composition with mean values of −18.4 ± 1.1‰ in δ18O, −138 ± 9‰ in δD and a mean deuterium excess (d) of 8.8 ± 1.4‰. Such isotopic wedge-ice record as well as its mean DOC concentration of 4.3 ± 2.1 mg L−1 fall within the range of previously studied Holocene ice wedges in the region. The directly-dated stable isotope record of ice-wedge growth on Herschel Island indicates a winter cooling trend towards the mid-Holocene, even though most available records attribute this cooling rather to the warm season while the winter temperatures over this period are not constrained yet.The extraordinarily rich ground-ice inventory of Herschel Island offers insights into the glacial and postglacial landscape evolution of the Western Canadian Arctic. Here, paleoenvironmental research highlights ice sheet-permafrost interactions over glacial, deglacial and postglacial timescales.

Geochemical Features of Ground Ice from the Faddeevsky Peninsula Eastern Coast (Kotelny Island, East Siberian Arctic) as a Key to Understand Paleoenvironmental Conditions of Its Formation

Understanding paleoenvironmental conditions of the permafrost formation allows us to estimate the permafrost carbon pool and its behavior upon thawing in a changing climate. In order to classify different types of ground ice and to reconstruct paleoenvironments, we examined geochemical data of ice wedges (IWs), tabular ground ice (TGI), and lens ice from the eastern coast of the Faddeevsky Peninsula (East Siberian Arctic). We analyzed isotope and ion composition, molecular composition of the gas phase, bulk biogeochemical parameters and dissolved organic matter (DOM) composition in ground ice samples. IWs formed in the Late Pleistocene under the coldest winter conditions and in the Holocene in proximity to the sea. The Holocene IWs have the highest mean d-excess (11–13‰) and a heavier isotope composition by an average of 6‰ compared with the Late Pleistocene IWs. We observe predominance of sea-salt fractions in ion composition of the Holocene IWs, while the Late Pleistocene IW shows enrichment in non-sea-salt component of SO42− (nssSO42−), which is probably associated with mineral leaching of deposits. Higher dissolved organic carbon (DOC) content in the Late Pleistocene IW (to 17.7 mg/L) may indicate more favorable vegetation conditions or lower degree of organic matter mineralization compared to Holocene IWs and TGI. CH4 concentrations were relatively low with a maximum value of 2.27 μmol/L. DOM composition, supposed to record the paleoenvironment of the freezing process, was for the first time tried as a biomarker for paleoenvironmental reconstructions of ground ice formation. Parallel factor (PARAFAC) analysis of EEM (Excitation-Emission matrix) of fluorescent DOM decomposes four components: P1–P3, which are related to allochthonous humic-like constituents, and P4, which is relevant to autochthonous fraction associated with microbial activity. The distribution of fluorescent DOM tracked the variability in both paleoclimate conditions of the IW formation (discriminating the Holocene and the Late Pleistocene IWs) and types of ground ice (IW and TGI), which demonstrates the potential of the used approach.

Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic

Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are classified in the Arctic, comprising Nilas, Young Ice, First Year Ice, Old Ice, and Fast Ice. Comparing the results with the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Ice Type dataset (SIT) indicates that the sea ice type classified by HY-2B SCA (Stacking-HY2B) is similar to OSI-SAF SIT with regard to the changing trends in extent of sea ice. We use the Copernicus Marine Environment Monitoring Service (CMEMS) high-resolution sea ice type data and EM-Bird ice thickness data to validate the result, and accuracies of 87% and 88% are obtained, respectively. This indicates that the algorithm in this work is comparable with the performance of OSI-SAF dataset, while providing information of multiple sea ice types.

Machine learning-based ice detection approach for power transmission lines by utilizing FBG micro-meteorological sensors.

Severe icing of transmission lines causes power failures, tower collapses, and other adverse events, which hinders the normal operation of society. Existing icing monitoring methods have problems of irregular monitoring and poor accuracy. In this study, we propose a comprehensive model for predicting hard rime and glaze ice using temperature, humidity, and historical icing data. The results of the experimental verification conducted for nine icing cycles in different years and geographic locations demonstrate that the proposed technique can effectively predict multiple types of icing while ensuring correlation coefficients > 0.99 and mean squared error < 4%.

Ice Sheet and Sea Ice Ultrawideband Microwave radiometric Airborne eXperiment (ISSIUMAX) in Antarctica: first results from Terra Nova Bay

Abstract. An airborne microwave wide-band radiometer (500–2000 MHz) was operated for the first time in Antarctica to better understand the emission properties of sea ice, outlet glaciers and the interior ice sheet from Terra Nova Bay to Dome C. The different glaciological regimes were revealed to exhibit unique spectral signatures in this portion of the microwave spectrum. Generally, the brightness temperatures over a vertically homogeneous ice sheet are warmest at the lowest frequencies, consistent with models that predict that those channels sensed the deeper, warmer parts of the ice sheet. Vertical heterogeneities in the ice property profiles can alter this basic interpretation of the signal. Spectra along the lengths of outlet glaciers were modulated by the deposition and erosion of snow, driven by strong katabatic winds. Similar to previous experiments in Greenland, the brightness temperatures across the frequency band were low in crevasse areas. Variations in brightness temperature were consistent with spatial changes in sea ice type identified in satellite imagery and in situ ground-penetrating radar data. The results contribute to a better understanding of the utility of microwave wide-band radiometry for cryospheric studies and also advance knowledge of the important physics underlying existing L-band radiometers operating in space.

Inter-comparison and evaluation of Arctic sea ice type products

Abstract. Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA – National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×106 to 0.49×106 km2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002–2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of -0.001×106 km2 in first-year ice (FYI) extent and -0.02×106 km2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007–2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscatter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.

First results of Antarctic sea ice type retrieval from active and passive microwave remote sensing data

Abstract. Polar sea ice is one of the Earth's climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014, and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly for MYI. Due to differences in the physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently,no satellite retrieval scheme was ready to monitor the distribution and temporal development of Antarctic ice types, particularly MYI, throughout the freezing season and on timescales of several years. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The core of the retrieval method is a mathematical scheme that needs empirical distributions of the microwave brightness temperature and backscatter input parameters for the different ice types. The first circumpolar, long-term time series of Antarctic sea ice types (MYI, first-year ice, and young ice) is being established, and so far covers the years 2013–2021. Qualitative comparison with (a) synthetic aperture radar data, (b) charts of the development stage of the sea ice, and (c) the Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval allows insight into the interannual evolution and dynamics of Antarctic sea ice types for the first time. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.

Structure freezing in the ice

Freezing into ice is a complex phenomenon that presents challenges and risks in both individual and construction contexts. Work provides a concise overview of the key aspects surrounding freezing in ice. It highlights the dangers. For construction, the risks lie in compromised structural integrity, limited stability, and access difficulties. Calculating freezing in ice involves considering temperature, material properties, freezing time, ice formation, and expert consultation. Different types of freezing into ice are identified, and the freezing process is shown to be influenced by the type of ice, such as freshwater ice or sea ice. Dealing with freezing in ice requires strategic approaches, including following established procedures, utilizing proper equipment, seeking professional assistance, and adapting to changing conditions. Engineering construction aspects encompass planning, design, ground preparation, foundation design, construction techniques, material selection and monitoring. Understanding and managing freezing in ice is crucial for ensuring safety and successful construction projects in icy environments.

Thermal scattering law for ice based on neutron time-of-flight experiments carried out at the SEQUOIA spectrometer at the Oak Ridge National Laboratory

Precise estimation of neutron thermalization in moderators relies on high-fidelity thermal scattering cross-section data governed by the thermal scattering law (TSL). The Institut de Radioprotection et de Sûreté Nucléaire (IRSN) has been working on the development of improved TSL for light water ice. Many polymorphic phases of light water ice exist depending on the thermodynamic conditions. The most common type of ice at standard pressure and temperature below water freezing point (273.15 K) is ice-Ih. It is essential to have high-resolution experimental double differential data for developing and/or validating TSL for moderator materials.Existing experimental double-differential scattering data for ice-Ih are extremely sparse and of limited quality. New high-quality double-differential measurements for ice-Ih over multiple temperatures and incident neutron energies would directly support the validation and improvement of ice-Ih TSL models for criticality safety applications. Series of time-of-flight (TOF) inelastic neutron scattering experiments on ice-Ih at temperatures starting at 271 K and down to 6 K, have been carried out at the SEQUOIA spectrometer at the Spallation Neutron Source (SNS) at the Oak Ridge National Laboratory (ORNL), United States. The experiments have been performed for incident neutron energies, Ei= 11, 55, 160, 250, and 600 meV, to explore different excitation energies in the vibrational phonon spectrum. This paper presents the thermodynamic conditions and the details of the TOF measurements on ice-Ih and the derived phonon spectrum from the experimentally measured double differential data. A study of the variation of the phonon spectrum of ice-Ih as a function of temperature is highlighted, and a preliminary TSL evaluation is developed based on the experimental phonon spectrum.

Automatic High-Accuracy Sea Ice Monitoring in the Arctic Using MODIS Data

High accuracy sea ice monitoring is of great significance in responding to global climate change and guiding safe navigation. The MODIS sensors carried by Aqua and Terra satellites have great potential in high-resolution monitoring of sea ice in polar regions. This study developed an automatic high-accuracy sea ice concentration (SIC) detection algorithm for the MODIS data. We used a machine learning algorithm that combines spectral and texture features to obtain high-precision sea ice recognition results. Then, according to the cloud characteristics in MODIS images and the full coverage of AMSR2 SICs, outlier removal and cloud area filling are carried out. Finally, the SIC result was calculated according to the distribution of ice types. We assessed the accuracy of our high-accuracy SIC product by comparing with the MOD29/MYD29 products and validating with the Landsat 8/9 images. We found that the deviation increased with the decrease of SIC value. The deviation are larger in the ice melting area and the ice edges area, but smaller in the area completely covered by ice. Compared with MOD29/MYD29 SICs, the SICs obtained in this study have higher accuracy, with an average RMSD of 7.64%. It can detect the details of relatively small leads, ice edges and fragmented ice areas. The high-accuracy SIC product we obtained is expected to provide long-term SIC records of high quality.

Estimation of Arctic Winter Snow Depth, Sea Ice Thickness and Bulk Density, and Ice Freeboard by Combining CryoSat-2, AVHRR, and AMSR Measurements

Information on snow depth on sea ice and bulk sea ice density is required to convert CryoSat-2 radar freeboard ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h_f</i> ) into sea ice thickness (SIT). It is difficult to obtain their information on an Arctic basin scale; therefore, most CryoSat-2 SIT products largely rely on the distributions of snow depth and bulk sea ice density derived from parameterizations, which are based on sea ice type and climatological values. Several observational studies have found that the distributions of parameterized variables are inaccurate compared to the actual distributions. This study aims to develop a new type of retrieval algorithm for snow depth, SIT and bulk density, and ice freeboard in the Arctic winter by synergizing active CryoSat-2 with passive microwave and infrared measurements. Two parameterizations for the snow-ice thickness ratio and bulk sea ice density were combined with the hydrostatic balance and radar wave speed correction equations. Consequently, solutions for the four target variables were obtained and applied to different CryoSat-2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h_f</i> , derived from empirical and waveform-fitting retracker algorithms. The retrieved thickness-related parameters based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h_f</i> from the lognormal waveform-fitting retracker algorithm showed good agreement with the airborne snow depth, total freeboard, and mooring ice draft measurements. The retrieved multiyear sea ice bulk density was significantly higher than the value of 882 kg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , which was used in the previous density parameterization, showing a higher agreement with values from in-situ measurements. The spatial and interannual variabilities of SIT increased when the results from this study were compared with those based on previous parameterizations.

Sea Ice Type and Thickness Identification Based on Vibration Sensor Networks and Machine Learning

The collision between sea ice and ship is a serious threat to navigation safety. However, the idea of using vibration data generated from ship-ice collisions to obtain physical information about sea ice, such as its type and thickness, is a novel method. This method overcomes the problem of traditional techniques (optical cameras and satellite remote sensing) being affected by visibility, and also provides data for assessing the ice load capacity of ships. In this paper, we propose a new method for sea ice type and thickness identification based on vibration sensor networks and machine learning. Firstly, the characteristic data set of sea ice type and thickness is constructed by analyzing the six nodes ice-ship vibration signals obtained from the vibration sensor network. We then used six classification algorithms to identify three common sea ice types (broken ice, flat ice, sea/open water) and assessed the accuracy of these algorithms at each node. Then, we use Gradient Boosting Decision Tree (GBDT) regression algorithm to predict sea ice thickness and evaluate the combination of node features. The results shows that the accuracy of single-node sea ice classification is 98.71%, and the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for sea ice thickness regression on a single node is 0.908. After feature fusion of two nodes, the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for sea ice thickness regression is 0.94, with a Root Mean Square Error (RMSE) of 0.847 cm, and the accuracy is better than traditional identification methods. The results show that the sea ice type and thickness can be identified with high accuracy by analyzing the vibration signals generated by ice-ship collision. The reliability and effectiveness of the proposed method in low visibility and other environments are verified by the sailing experiments in the Bohai Sea ice area accumulated at 1020 nautical miles.

Fiber Optic Ice Sensor for Measuring Ice Thickness, Type and the Freezing Fraction on Aircraft Wings

Ice accretion on an aircraft affects the aerodynamic performance of the wings by disrupting the airflow, increasing drag, and altering its flight characteristics, leading to a main or tail wing-stall and altimetry to aircraft loss. The current generation of ice-detection systems relies on environmental parameters to determine icing conditions, with the sensors usually located on the nose of the aircraft, giving no information on the ice accreted on the wings. This work focuses on modeling and developing a fiber-optic-array ice sensor, which illuminates and detects the reflected and scattered light directly from the ice surface and volume, and measures the accretion rate and type of ice on the wings. The ice morphology is influenced by the rate of freezing of the super-cooled droplets impacting the wings, and partially or totally trapping the dissolved gasses. This leads to the formation of rugged surfaces and ice shapes, which can be transparent or opaque, a process which is dependent on the local Freezing Fraction (FF) of the impinging super cooled water. The detection method relies on the optical characteristics of ice, affected by density and the size of micro-cracks and micro-bubbles formed during freezing. By using high Numerical Aperture (NA) fibers, it was possible to accurately measure the ice thickness, and to investigate a proof-of-concept experiment, correlating the optical diffusion to the FF of the ice on a wing.

Assessing travel time-based accessibility to outdoor ice skating fields for children in Helsinki during the COVID-19 pandemic

Informal sport is central to Finnish children’s leisure and physical activity time. This paper aims to build a better understanding of the travel time-based accessibility to informal sports facilities, specifically to ice skating fields, for children and adolescents (aged 7–19) in the city of Helsinki. We focused on the winter of 2020–2021 because COVID-19 restrictions on indoor activities resulted in ice skating fields being among the few public facilities that could remain open. Additionally, the weather was favourable for maintaining outdoor ice skating fields. We analysed if there would be a difference in children’s independent travel times by public transport or walking to ice skating fields due to the COVID-19 pandemic related recommendations by Helsinki Region Transport to avoid public transport. Children in Finland usually travel to and from school independently. Hence we focused on the transition from public transport to walking and omitted car usage, which would require an adult. We also looked at the potential differences in travel time to ice skating fields by analysing different types of fields separately. This difference would be of significance if climate change resulted in warmer winters in Finland. Helsinki has two types of ice skating fields: naturally frozen and mechanically frozen, of which only the mechanically frozen fields would be used during a warmer winter that is above zero degrees Celsius. We took a geographic information systems (GIS) analysis approach using travel time and population catchments. The study’s main findings show that during a milder winter and by walking, the accessibility for children is greatly reduced to 55.2%; that is, children face an increased travel time when naturally frozen ice skating fields are not in use. However, almost 100% of the child population can access both types of fields within a travel time of 30 minutes by public transport.

Impacts of sea ice melting procedures on measurements of microbial community structure

Microorganisms play critical roles in sea ice biogeochemical processes. However, microbes living within sea ice can be challenging to sample for scientific study. Because most techniques for microbial analysis are optimized for liquid samples, sea ice samples are typically melted first, often applying a buffering method to mitigate osmotic lysis. Here, we tested commonly used melting procedures on three different ice horizons of springtime, first year, land-fast Arctic sea ice to investigate potential methodological impacts on resulting measurements of cell abundance, photophysiology, and microbial community structure as determined by 16S and 18S rRNA gene amplicon sequencing. Specifically, we compared two buffering methods using NaCl solutions (“seawater,” melting the ice in an equal volume of 35-ppt solution, and “isohaline,” melting with a small volume of 250-ppt solution calculated to yield meltwater at estimated in situ brine salinity) to direct ice melting (no buffer addition) on both mechanically “shaved” and “non-shaved” samples. Shaving the ice shortened the melting process, with no significant impacts on the resulting measurements. The seawater buffer was best at minimizing cell lysis for this ice type, retaining the highest number of cells and chlorophyll a concentration. Comparative measurements of bacterial (16S) community structure highlighted ecologically relevant subsets of the community that were significantly more abundant in the buffered samples. The results for eukaryotic (18S) community structure were less conclusive. Taken together, our results suggest that an equivalent-volume seawater-salinity buffered melt is best at minimizing cell loss due to osmotic stress for springtime Arctic sea ice, but that either buffer will reduce bias in community composition when compared to direct melting. Overall, these findings indicate potential methodological biases that should be considered before developing a sea ice melting protocol for microbiological studies and afterwards, when interpreting biogeochemical or ecological meaning of the results.

Two Forms of Ice Identified in Mars-like Clay Using Neutron Spectroscopy

The capacity of clay minerals to store large amounts of water is utilized in a number of industrial and environmental applications on Earth, for example, as components in geosynthetic clay liners in landfills or ingredients in water-based drilling fluids, and could prove important on Mars to identify future human landing sites where water could be harvested. The subzero behavior of water interacting within the interlayer space of clay minerals is of particular interest in most applications but remains poorly understood. To better understand the hydrothermal mechanism by which water ice bonds and separates from clay interlayers, we have utilized neutron spectroscopy, spectral analysis, and phonon band assignment. The inelastic neutron scattering from sodium montmorillonite, hydrated at 24, 73, and 166% water content, as well as an oven-dried sample, were measured to assess the vibrational density of states. The water contents studied provide a range of pore dimensions within clay gels that have varying degrees of confinement. The type of ice formed from water held in larger intra- and interparticle pores differs substantially from that confined within the interlayer (pseudo-two-layer hydrate), and the differences vary with hydration level. Spectral subtraction over an energy transfer range 50 < E < 550 cm–1 (8 < E < 70 meV) produces clearly two different forms of ice: hexagonal and cubic in the two wetter samples. A form of interfacial ice, presumably of a lower density, is observed in the vibrational density of states spectrum of the sample hydrated to a pseudo-two-layer hydrate (ie 24% gravimetric water content (GWC), 10 H2O/Na+). No hexagonal or cubic ice is observed in this sample. The four vibrational modes within the translation band of hexagonal ice are apparent within the sample hydrated to 166% gravimetric water content, in which pores greater than 20 nm are largely water-filled. By considering hydrogen bonding of the water to the clay surface, our data indicate an increase in the strength of the H-bond due to a shorter distance to the hydroxyl. We attribute this decrease to the pores in the clay generating a localized negative pressure or “suction” effect, thus attracting the water.

An initial study of interference coloration for quantifying the texture and fabric of ice

The manual application of universal (Rigsby) stage techniques is commonly used to determine the fabric of thin sections of ice viewed with crossed-polarized light. This process can require hours of focus in cold conditions to identify the c-axis of each grain in a thin section. Automated ice texture and fabric methods of several forms exist but are rarely implemented beyond the field of glaciology. The present study introduces a method based on the theory of interference coloration for automated ice texture and quarter fabric analysis by using in-plane conventional photography of an ice thin section as input. The method is compatible with universal stages and polariscopes, and is not restricted by the planar-face dimensions of the thin section, allowing for thin section analysis of any size when sufficient digital camera resolution is available. Light source color temperature and chromatic adaptation are considered in the interference coloration theory, and ice fabrics are simulated for reference in identifying ice types. Sample thin section texture and quarter fabric analyses from freshwater lake and laboratory-grown ice are presented to demonstrate the applications of the method. The method is compared with the Rigsby stage technique, which yielded mean (standard deviation of) azimuth and inclination errors of 2.9 (1.0) and 11.5 (8.0) degrees, respectively, thereby demonstrating accuracy sufficient for quantifying quarter fabrics when considering a mean standard deviation in inclination of 5.4 degrees with the Rigsby stage technique.

Remote sensing sea ice classification based on DenseNet and heterogeneous data fusion

Most remote sensing sea ice classification methods use single-source remote sensing data, such as synthetic aperture radar (SAR) data and optical remote sensing data. SAR data contain rich sea ice texture information, but the data are relatively single, making it difficult to distinguish detailed sea ice categories. Optical data include abundant spatial-spectral information, but they are often affected by clouds, fog, and severe weather. Hence, given the limitations of single-source data, the remote sensing sea ice classification accuracy cannot be further improved. A sea ice classification method based on deep learning and multisource remote sensing data fusion is proposed utilizing an improved densely connected convolutional neural network (DenseNet) to mine and fuse the multilevel features of sea ice. According to the characteristics of SAR data and optical data, a dual-branch network structure based on an improved DenseNet is employed for feature extraction, and a squeeze-and-excitation attention mechanism is introduced to weight the fusion features to further enhance the feature weights that can effectively distinguish different types of sea ice. The fully connected network is used to perform the deep fusion of features and classify sea ice. To verify the effectiveness of the proposed method, two sets of sea ice data are utilized for sea ice classification. The experimental results show that the proposed method fully excavates and fuses the multilevel characteristics of heterogeneous data using the improved dual-branch network structure, leverages the complementary characteristics of SAR data and optical data, significantly increases the sea ice classification accuracy, and effectively improves the influence of cloud cover on the sea ice classification accuracy. Compared with the typical single-source data classification method and other heterogeneous data fusion classification methods, the proposed method achieves superior overall classification accuracy (98.49% and 98.58%).