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Eddies in the Marginal Ice Zones (MIZ) contribute to an enhanced melting of sea ice by forcing contact between the sea ice and the warmer water off the ice edge. The horizontal current field can be retrieved by tracking the movement of ice floes between sequential satellite images. However, the traditional method of current retrieval imposes stringent requirements on the time intervals between sequential images, and the performance of the traditional method diminishes rapidly with increasing time intervals. In this paper, an improved method of retrieving currents from multi-sensor remote sensing images is proposed, which can significantly improve the accuracy of current retrieval. The method first extracts feature from remote sensing images based on texture features and a support vector machine model and then extracts prior information about the ice-edge eddy from the first two remote sensing images. Under the guidance of prior information, horizontal currents of the eddy are retrieved by tracking the movement of sea ice from sequential remote sensing images. The proposed method was used to retrieve currents from Sentinel-1 and Sentinel-2 sequential images over the MIZ of the Fram Strait. Comparisons with the traditional method show encouraging results that the proposed method can significantly reduce the number of mismatches and improve the reliability of current vectors. [ABSTRACT FROM AUTHOR]

The interaction between waves and ice has a crucial impact on the seasonal change in the sea ice extent. However, our comprehension of this phenomenon is restricted by a lack of observations. In recent years, availability of the low-cost and accurate Inertial Motion Units has enabled the development of affordable wave research devices. Despite advancements in designing innovative open-source instruments optimized for deployment on ice floes, their customizability and survivability remain limited, especially in open waters. This study presents a novel design concept for an affordable and customizable wave buoy, aimed for wave measurements in marginal ice zones. The central focus of this wave buoy design is the application of 3D printing as rapid prototyping technology. By utilizing the high customizability offered by 3D printing, the previously developed solar-powered wave buoy was customized to install a battery pack. Preliminary results from field deployments in the Pacific and Arctic Oceans demonstrate that the performance of the instruments is promising. The accuracy of frequency wave spectra measurements is found to be comparable to that of considerably more expensive instruments. Finally, the study concludes with a general evaluation of using rapid prototyping technologies for buoy designs and proposes recommendations for future designs. [ABSTRACT FROM AUTHOR]

A drifting wave-ice buoy (Medusa-766) was deployed at the Lützow-Holm Bay (LHB) marginal ice zone in Antarctica during the 63rd Japanese Antarctic Research Expedition to study the wave influence on the unstable LHB fast ice. Medusa-766 survived the Antarctic winter as it was located deep in the ice cover with the shortest distance to the ice-free Southern Ocean over 1,000 km; at this time, there was evidence of 8-cm-height wave signal at the buoy position. Using the the ECMWF's reanalysis wave data, we show that the incoming waves were likely 4-m waves that were generated by an extratropical cyclone in the Southern Ocean. Wave-induced ice breakup potential for this event could extend hundreds of kilometres into the ice field. When Medusa-766 was in LHB in the summer months, it did not detect sizable wave energy despite the low sea ice concentration extent even during on-ice wave events. Understanding the wave attenuation characteristics is needed to elucidate the ocean wave effect to the unstable LHB fast ice. The success of Medusa-766 demonstrates the robustness of the general design and the high sensitivity of the sensor used, which is promising for future LHB wave–ice interaction research. [ABSTRACT FROM AUTHOR]

Expendable microSWIFT buoys have been developed and tested for measuring ocean surface waves. Wave spectra are calculated via onboard processing of GPS velocities sampled at 5 Hz, and wave spectra are delivered to a shore-side server via Iridium modem once per hour. The microSWIFTs support additional sensor payloads, in particular seawater conductivity and temperature. The buoys have a non-traditional, cylindrical shape that is required for deployment via the dropsonde tube of research aircraft. Multiple versions have been developed and tested, with design considerations that include: buoy hydrodynamics, sensor noise, algorithm tuning, processor power, and ease of deployment. Field testing in a range of conditions, including near sea ice and in a hurricane, has validated the design. [ABSTRACT FROM AUTHOR]

For the impact of land, sea ice, rainfall and strong wind, the wet tropospheric correction (WTC) of HY-2C (the third marine dynamic environmental monitoring satellite of China) altimeter cannot be obtained accurately by the calibration microwave radiometer (CMR) over the coastal ocean. The conducted evaluation of the HY-2C CMR WTC over the coastal ocean by the Global Navigation Satellite System (GNSS) and ECMWF Reanalysis v5 (ERA5) data showed that the CMR WTC is abnormal or invalid. To solve this issue, a new method to determine the WTC of the HY-2C altimeter based on objective analysis (OA) by combining the GNSS WTC, ERA5 WTC and valid CMR WTC is developed. The OA WTC and HY-2C CMR WTC are compared to the Integrated Global Radiosonde Archive (IGRA) WTC to validate the OA method. The results show that the RMSE of the WTC differences significantly decreases and the correlation coefficient increases. The number of available WTC data increases over the open ocean. The variances of sea level anomaly corresponding to the OA WTC in most areas are significantly smaller than those of the HY-2C CMR WTC. In general, the availability and accuracy of HY-2C altimeter data are largely enhanced with the OA WTC. [ABSTRACT FROM AUTHOR]

The article discusses the close relationship between the Yupik people of St. Lawrence Island, Alaska, and walruses. The Yupik have relied on walruses for food, materials, and cultural expression. However, the Pacific walrus population was severely depleted by whalers in the late 1800s, leading to starvation and hardship for the island's communities. Today, the changing environment and increased human activity pose a double threat to walruses and the Yupik way of life. Efforts are being made to protect walruses and sustain the knowledge and skills needed to continue relying on them. The article emphasizes the importance of respect for walruses and supporting the rights of Indigenous communities. [Extracted from the article]

Northern Sea Route (NSR) has attracted much attention from the academia. However, the relevant issues, namely, the seasonal variation of sea ice coverage, the possibility and delay of ice besetting events (IBEs), formulation of ice-breaker escort fee and rescue fee, and the internalization of environmental costs, are considered in past models, but not in a systematic manner. To break through the limitations of previous studies, this paper investigates the economic and environmental feasibility of the NSR for container service, and clarifies the said issues using the latest survey data on a business case. Specifically, a novel evaluation model was developed to estimate the required freight rate (RFR) comprehensively, from the perspective of economic and environmental costs. Based on the modeling results, the optimal scheme was designed for all-season commercial liner transportation via the NSR. Besides, the RFR-based logit competitiveness model was adopted to evaluate how the NSR as an alternative to the Suez Canal Route (SCR) is affected by multiple factors: the delay induced by the IBEs, carbon tax rate, loading factor of eastbound containerships, and fuel price. The research results provide reference for decision-makers to formulate operations plans for ice class, navigation speed, and ship type. [ABSTRACT FROM AUTHOR]

For spatial linear models, the classical maximum-likelihood estimators of both regression coefficients and variance components can be biased when the covariates are measured with errors. This work introduces a theoretically backed-up estimation framework for the spatial linear errors-in-variables model in a functional approach. Compared with the structural models, the functional approach treats the unobserved true covariates as fixed unknown parameters without imposing additional structures, thus leading to more robust parameter inference. Our model parameters are estimated simultaneously based on a set of unbiased estimating equations. Under some regularity conditions, we prove the consistency of the proposed estimating-equation estimators and derive their asymptotic distribution. In addition, a consistent variance estimator is developed for the estimating-equation estimators. To handle large spatial datasets, we provide two approaches to obtain scalable estimations based on our proposed estimating equations, where the required computational time and storage are reduced to be linear with sample size for each estimating-function evaluation. Simulation studies under different settings show that our estimators are consistent and the scalable algorithms work well. Finally, the proposed method is applied to studying the relationship between Arctic sea ice and related geophysical variables. for this article are available online. [ABSTRACT FROM AUTHOR]

Understanding the future of Southern Ocean ecosystems requires approaches at micro to macro scales. The Southern Ocean has experienced both top-down and bottom-up perturbations driven by man. The removal of whales and finfish was a top-down disruption, removing enormous levels of biomass of consumers and driving competitive release for other predators. In contrast, climate change is altering physical conditions in complex, interrelated ways likely to change primary-producer community structure, with bottom-up consequences for the efficiency of energy transfer to top predators, and for the biological pump. The physiological effects of increasing ocean temperatures on animal species are likely to be less important than three key physical changes: loss of sea ice, changes to water-column stability, and patterns of water transport; these will affect krill abundances, primary producer community structure, and prey availability to land-breeding predators, respectively. The Southern Ocean is vast and critically influenced by global teleconnections, with different regions already exhibiting different patterns of physical and biological change. Given the strong physical forcing of these ecosystems, many of the initial consequences of climate change will operate through direct physiological effects on the primary producers and indirect effects on the larger organisms. This will disrupt species interactions and drive new ecological relationships. [ABSTRACT FROM AUTHOR]

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification. [ABSTRACT FROM AUTHOR]

The increased oil-spill possibilities in sea-ice areas pose a threat to the marine environment and the ice-area coverage. Contrary to being widely used in monitoring oil spills in open waters, quantitative analyses of oil-spill hyperspectral information in ice-covered sea areas are inadequate. Due to multiple propagating processes, small oil droplets tend to form and mix into ice, resulting in low background oil concentration. For a complex nonlinear mixture, accurately estimating the material abundances remains a challenging yet fundamental task, particularly with extensive detailed and low-energy information. Herein, an abundance estimation method with normalizations in a combined polynomial and sine model is proposed to resolve the over-fitting problems. An energy information–based wavelet package scheme effectively highlights the latent information of the concerned materials. The oil-contaminated sea-ice images were generated with spectral reflectance collected outdoors on sea ice in Bohai Bay, China, in the form of pure as well as mixed spectral signatures. Experimental analyses suggest that the proposed method exhibits superior unmixing performance, particularly in delivering more accurate abundance estimations of different oil concentration levels as low as 10 − 4 than classic unmixing methods, even in low signal-to-noise data, and can be used to respond to oil spills at high latitudes. [ABSTRACT FROM AUTHOR]

The identification and evaluation of the quality of water reserves will be needed after the landing of man on the Mars' surface. To solve this problem, we propose two decision-making systems that result from searching for and estimating the ice thickness or the aquifer depth on Mars. These decision-making systems are based on the use of both multi-channel microwave radiometry, spectral optical tools and the corresponding software. Decision-making systems allow the specification and spatial identification of potential water resources and the evaluation of the depth of the ice-water layer. Algorithms for the functional classification of water samples are proposed and the method for diagnosing water quality is described. The technique is based on the recognition of optical spectral images using a database of spectral patterns. The scheme for passive microwave measurements of ice thickness or groundwater is presented. More efficient radiometers are recommended as components of the microwave decision-making system. The 35-channel spectrophotometer and the 128-channel spectroellipsometer are presented as optical tools for the synthesis of spectral images of ice and water samples. An algorithm of optical inverse task solution is given. [ABSTRACT FROM AUTHOR]

Touzi's scattering vector model allows for a unified decomposition of both coherent and incoherent target scattering. Based on this model, a unique and roll-invariant model can be developed for target decomposition. Our study aims at the investigation of the Touzi decomposition for lake and sea ice monitoring. Thus, we investigate the sensitivity of the target parameters obtained from the Touzi incoherent decomposition to modeled ice growth. Our study focuses on thermodynamically-grown fast lake and sea ice during the early ice growth. A time-series quad pol synthetic aperture radar (SAR) imagery was acquired over a study site around the Resolute Bay area. Results indicate that for lake ice, the scattering type magnitude ( α s ) decreases within a thickness up to 20 cm. This means switching from volume scattering to dominant surface scattering. The same sensitivity is observed for young sea ice with thickness up to 30 cm, which corresponds to modeled ice bulk salinity of 8.5‰. Remarkably, we found a trend of increasing target orientation angle ( ψ) for thin sea ice up to 20 cm, which corresponds to a bulk salinity of 10.3‰. This trend is unique to the case of sea ice. [ABSTRACT FROM AUTHOR]

The study was carried out in seven stations to assess concentration levels of Vanadium (V) in sediments of the Prydz Bay, Indian sector of the Southern Ocean during the austral summer season. Sediment analysis showed that V concentration on the eastern side was extensively higher than on the western side of Prydz Bay. A significant observation was the high concentration of V concentration in fine sediment fraction than in total metal fraction. Vanadium concentration level on the western side varied from 19.4 to 45.5 mg/kg while it was 40.9–65.8 mg/kg on the eastern side of Prydz Bay. The prevailing petroleum-related activities coupled with fuel combustion processes at various research stations in the Antarctic subcontinent and the influence of sea ice cover interaction could be the reason for the high V concentration on the eastern side of Prydz Bay. A significant spatial variation in textural characteristics, elemental composition, and total organic carbon was observed with high concentration levels of total organic carbon (TOC), total nitrogen (TN), and total hydrogen (TH) in the eastern part of Prydz Bay. The statistical analysis revealed a strong positive correlation of V concentration with governing factors at p <.01 level. [ABSTRACT FROM AUTHOR]

Settlement of the Outer Planets of the Solar System will take humans far away from the Sun, its warmth, and the light on which humans depend. Most settlements will be small mining enclaves or research stations until well into the future. Lengthy travel times will satisfy the human needs for hope, patience, acceptance, self-study, and contemplation of God's role in the universe. The authors explore the potential roles of theology and religion in Deep Space, the Asteroids, and the icy moons of Jupiter and Saturn where life might be found in the liquid oceans under ice that is kilometers deep. [ABSTRACT FROM AUTHOR]

McMurdo Station, located in Antarctica, experiences extreme weather conditions due to its proximity to the Southern Ocean and the surrounding features such as the Ross Sea, the Ross Ice Shelf, and the Transantarctic Mountains. The station is known for its cold temperatures and powerful katabatic winds, which are gravity-driven winds that accelerate dense, cold air downslope. These winds, along with the complex topography, create localized weather patterns and storms, including intense snowstorms called "Herbies." The area also includes the McMurdo Dry Valleys, which are the largest ice-free land area in Antarctica and some of the driest places in the world. McMurdo Station serves as a hub for scientific research and is administered by the United States National Science Foundation. [Extracted from the article]

Keywords: Antarctica; global warming; Thwaites Glacier; sea level rise; climate crisis; ice melt EN Antarctica global warming Thwaites Glacier sea level rise climate crisis ice melt 121 128 8 05/12/20 20200501 NES 200501 On the Thwaites Glacier on Antarctica's western shelf, researchers are going all-out to learn what is happening deep under the surface of the ice, out of sight: How much is melting from below, where the ice comes into contact with warm ocean waters? B Davis b :My understanding is if the whole of the Thwaites Glacier were to collapse - and there's no indication that would occur - you would get somewhere between 50 centimeters (19.5 inches) and maybe 65-to-80 centimeters (25.5-to-31.5 inches) of sea level rise from this one glacier and the region of the ice sheet that it drains alone. B Davis b :The whole of the West Antarctic ice sheet, of which the Thwaites Glacier is a part, has the potential for two-to-three meters of sea level rise, if the whole of the ice sheet were to collapse. Antarctica, global warming, Thwaites Glacier, sea level rise, climate crisis, ice melt. [Extracted from the article]

In this interview, Durwood Zaelke, president of the Institute for Governance & Sustainable Development, outlines the climate change problem in the Arctic, tells of the increasing speed with which the ice is melting, and outlines some possible solutions, ranging from relatively benign approaches to methods that call for increasingly more intrusive tinkering with the planet. [ABSTRACT FROM AUTHOR]

If 1000 years were required to melt the ice cap, the sea level would rise about 4 feet every ten years ... 100 times the present rate of sea level rise'. The report of his scientific advisory committee published in the same year suggested that one result could be'the melting of the Antarctic ice cap which would raise sea level by 400 feet. I An Andean glacier is melting: See you in court i is an account of a Peruvian farmer suing a German power utility for contributing to melting an Andean glacier that might bury his home with a cataclysmic mudflow, as happened earlier, in 1941. [Extracted from the article]

The Arctic is often cast as a site where natural resources can be extracted with processes eased by retreating sea ice. This article argues that Arctic resource euphoria is misplaced – at least partly – due to the worsening climatic, ecological, and social crises that increase the costs and risks of extraction. Permafrost melt is making mineral and fossil fuel extraction riskier and more difficult, as extractive infrastructures – built with great sunk costs – are deteriorating. The climate catastrophe can already be observed, first-hand, in the Arctic. This can give insights into what kinds of dynamics can be expected globally. The deterioration of extractive infrastructures caused by the climate crisis is likely to dampen the forecasts for expanding extractivism. This article identifies interdisciplinary research gaps and needs, while offering new prognoses for the future in and through the Arctic at the interface of climate crises, global climate tipping points, and resource extraction. [ABSTRACT FROM AUTHOR]

Arctic sea ice change is one of the critical factors affecting the global climate environment; hence, it is crucial to obtain sea ice thickness with high accuracy while studying polar and global climate change. In the process of using altimetry data to estimate sea ice thickness, the mean sea surface height will bring greater uncertainty to the extraction of sea ice freeboard, which will affect the accuracy of sea ice thickness. Also, the influence of snow cover on radar signal penetration will bring greater uncertainty to sea ice thickness. In this paper, we present a processing chain for sea ice thickness estimation. First, we compare the effect of four different MSS models on the freeboard estimation. Then, considering the incomplete penetration of radar signals and the different speeds of radar signals penetrating the snow layer and the vacuum, the traditional sea ice thickness model is optimized to obtain the sea ice thickness. Compared with the Operation IceBridge (OIB) sea ice thickness, the accuracy of sea ice thickness obtained by the optimized model is better than 0.350 m, with a root mean square error of 0.260 m and a mean bias of 0.048 m. The comparison results show that the combination of the latest DTU18 MSS model and sea ice thickness optimization model effectively improve the accuracy of sea ice thickness. [ABSTRACT FROM AUTHOR]

Sea ice drift is a crucial parameter for sea ice flux, atmospheric and ocean circulation, and ship navigation. Pattern matching is widely used to retrieve sea ice drift from Synthetic Aperture Radar (SAR) data, but it often yields mismatched vectors and coarse spatial resolution. This study presents a framework to enhance the spatial resolution and accuracy of pattern-matching sea ice drift derived from SAR images. The framework employs the Accelerated-KAZE feature extraction method and Brute-Force feature matcher to extract feature-tracking sea ice drift vectors from SAR data, with mismatched vectors subsequently removed. The pattern-matching vectors are then refined by fusing with these feature-tracking vectors, using a Co-Kriging algorithm. Using the sea ice drift product from the Technical University of Denmark space as the pattern-matching vector field for refinement, the framework's effectiveness is evaluated by comparing the refined vectors with buoy displacements and pattern-matching vectors across five selected regions. Results show a reduction in velocity and direction root mean square error (RMSE) by 0.47 km/d (22%) and 4.97° (28%), respectively, and an enhanced spatial resolution from 10 km to 1 km. The findings demonstrate the framework's success in improving the accuracy and resolution of pattern-matching sea ice drift from SAR imagery. [ABSTRACT FROM AUTHOR]

Numerous studies have been conducted to enhance our understanding of how climate change impacts landfast ice and its break-up in spring or summer. Yet, predictions of break-up timing have proven elusive illusive, and dependent on multiple environmental drivers. In this study, we investigate whether/to what extent snow melt on land adjacent to the coast can serve as a precursor to landfast ice break-up. For the study, we used MODIS snowmelt timing products to explore the pattern of snowmelt across the study area. This was compared against landfast ice break-up dates generated mainly from CIS ice-chart, across the Hudson Bay and James Bay coast. Snowmelt timings recorded across the study area followed the same latitudinal gradient as the landfast ice break-up dates, with almost a 50-day difference between the south and the north snowmelt and ice break-up timings. The data shows that the timing of landfast ice break-up across the Hudson Bay and James Bay region showed significantly stronger correlations with the timing of terrestrial snowmelt, as compared to air temperature thresholds, with an average 17-day gap observed between snowmelt to landfast ice break-up. Based on the observations, we believe snowmelt can be a reliable precursor to landfast ice break-up. [ABSTRACT FROM AUTHOR]

Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals. [ABSTRACT FROM AUTHOR]

Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from water on a large volume of data covering 11 years of ice and surface water conditions. An overall accuracy of 90.1% was achieved on 989 scenes of 100% ice cover or ice-free conditions. An accuracy of 86.3% was achieved on the last year of data (134 scenes) which was kept out of the training process to test the model on an independent dataset. A better accuracy is obtained at lower incidence angles and the HH polarization provides the most information to classify ice from water. To achieve the best accuracy, the incidence angle and the noise equivalent sigma-nought had to be included as input to the model. A comparison done with the ASI passive microwave product shows similar errors in total sea ice concentration when using the Canadian Ice Service regional charts as reference. Nonetheless, errors from both datasets differ and the CNN outputs show greater potential to reduce masked areas, given the better spatial resolution, enabling data classification closer to land and identify features not captured by the ASI dataset. [ABSTRACT FROM AUTHOR]

Arctic sea ice is rapidly transitioning into a perennial first year ice pack and this is being observed with satellite remote sensing. Satellite image interpretation requires accurate knowledge of the physical conditions and how they give rise to the microwave scattering response that is present within a single image pixel. This study addresses this issue through a focused remote sensing study of thin first year sea ice. We present results from an experiment that fused datasets from surface-based C- and Ku-band polarimetric scatterometers, LiDAR, and drone-based optical and thermal infrared sensors. We grew frost-flower-covered thin first year sea ice in a mesocosm facility and measured the time-series evolution of C- and Ku-band scattering response as it evolved into snow-covered sea ice. Drone surveys, LiDAR scans, and physical sampling provided complementary characterization of the ice. Results quantify the sensitivity of C- and Ku-band to the presence of frost flowers, the addition of snow, and the meteorological conditions. Drone surveys enhanced the characterization by rapidly performing observations over a larger representative region. In essence, they are helping to close the gap between surface-based sensing and satellite imagery. Furthermore, this study complements and enhances our understanding of the snow-covered sea ice system. [ABSTRACT FROM AUTHOR]

In this study, sea ice edge derived from three passive microwave (PM) algorithms, ARTIST sea ice (ASI), enhanced NASA Team 2 (NT2), and Bootstrap (BT), are compared to those derived from the daily Canadian Ice Service charts over a primarily seasonal ice zone in the eastern Canadian Arctic for 2013–2021. To determine the ice edge error, we introduced an edge-length-based displacement measure called the edge displacement error (EDE), a dimensionless measurement obtained by dividing the weighted average Hausdorff distance by the ice edge length. We found that the ASI algorithm has the highest EDE on average, while the BT algorithm has the lowest one. In October (the beginning of the freeze-up period), the ice edge exhibits significant meandering, and the EDE is less sensitive to changes in the charted area. In the freeze-up period, the PM algorithms have the highest mean EDE value relative to other months due to the appearance of thin ice. A greater range of EDE values was observed in April than in other months. Throughout this region, the wind speed varies the most in April and May, whereas in April, the air temperature fluctuates more than in the other months. [ABSTRACT FROM AUTHOR]

It is critical to understand the mechanisms and impacts of global climate change in cryosphere and polar environment. Here we describe the design and progress of the remote sensing products we have developed to monitor these changes. We obtain datasets by combining multi-sensor remote sensing systems with field validation systems, and then develop a methodological system that combines multiple sources and calibration technologies to address the challenges of data heterogeneity in dynamically monitoring key parameters. This global change and response special project supported by the National Key Research and Development Program of China, named 'observation and inversion of critical parameters of changes in the cryosphere and polar environment', providing comprehensive records of essential elements of the cryosphere through long-term satellite records. The multi-team is completing the project and collecting data from multi-sensor to create remote sensing data products that include records of changes in the cryosphere, such as permafrost, glaciers, ice sheet mass balance, seasonal snow accumulation, sea ice, and albedo changes. Finally, the existing ground- and space-based observing system can be improved to provide dynamic monitoring of key parameters in the cryosphere and changes in the polar environment. [ABSTRACT FROM AUTHOR]

Digital information on sea ice extent, thickness, volume, and distribution is crucial for understanding Earth's climate system. The Snow and Ice Mass Balance Apparatus (SIMBA) is used to determine snow and ice temperatures in Arctic, Antarctic, ice-covered seas, and boreal lakes. Snow depth and ice thickness are derived from SIMBA temperature regimes (SIMBA_ET and SIMBA_HT). In warm conditions, SIMBA_ET temperature-based ice thickness may have errors due to the isothermal vertical profile. SIMBA_HT provides a visible ice-bottom interface for manual quantification. We propose an unmanned approach, combining neural networks, wavelet analysis, and Kalman filtering (NWK), to mathematically establish NWK and retrieve ice bottoms from various SIMBA_HT datasets. In the Arctic, NWK-derived total thickness showed a bias range of −5.64 cm to 4.01 cm and a correlation coefficient of 95%−99%. For Baltic Sea ice, values ranged from 1.31 cm to 2.41 cm (88%−98% correlation), and for boreal lake ice, −0.7 cm to 2.6 cm (75%−83% correlation). During ice growth, thermal equilibrium, and melting, the bias varied from −3.93 cm to 2.37 cm, −1.92 cm to 0.04 cm, and −4.90 cm to 3.96 cm, with correlation coefficients of 76%−99%. These results demonstrate NWK's robustness in retrieving ice bottom evolution in different water environments. [ABSTRACT FROM AUTHOR]

Because ice surface temperature (IST) controls snow melt, sea ice growth and air–ocean heat exchange, it is a key parameter in analyses of the Arctic climate system. However, optical satellite-based IST data often have missing values due to the satellite orbit, clouds and polar night. In this study, we estimated IST using a deep neural network (DNN) algorithm based on meteorological, sea ice and geometric variables that are strongly correlated with IST. Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra IST data were used, and the input data were 2 m air temperature (Ta), 30-year averaged Ta (Ta climatology), total column water vapour (TCWV), solar zenith angle (SZA), local solar noon angle (LSN) and latitude. The data were classified into six cases according to sea ice age (SIA) and Ta to create an efficient DNN model that supplemented the IST data missing from the existing dataset. Model validation based on the MODIS IST data revealed a correlation coefficient of 0.94, root mean square error of 3.54 K and relative root mean square error of 1.35%, showing high accuracy. [ABSTRACT FROM AUTHOR]

Ice and snow in the Cold War, histories of extreme climatic environments: edited by J. Herzberg, C. Kehrt and F. Torma, 2018, Oxford, Berghahn, viii+322 pp., $135/£ 99 (hardback), ISBN: 978-1-78533-986-8 The book opens up these discussions and more, illustrating the numerous meanings, functions and use of the cold environment in a condensed and place-bound manner, showing why the Poles became an important area of study during the Cold War. These studies emphasised the concern and the special status the cold environment commanded during the Cold War. [Extracted from the article]

Sea ice detection has played an important role in climate protection and strategic deployment. Due to the autonomous learning characteristics of deep learning, it has been gradually applied to the classification of remote sensing sea ice images. At present, deep learning models are mostly used in sea ice classification of single source remote sensing data. Due to the limitations of single source data and the information loss of deep learning model in the process of feature extraction layer by layer, it is inevitable to encounter a bottleneck in sea ice detection requiring fine classification. To solve the above problems, this paper proposes a sea ice image classification method based on ResNet16-feature pyramid networks-spatial pyramid pooling-gated fusion network (ResFPG) and heterogeneous data fusion. In the feature extraction part, the method uses the improved ResNet16 to extract the multi-level feature information of sea ice in synthetic aperture radar data and optical data, reducing information loss in the feature extraction process, then mines and fuses the low-level spatial information and high-level semantic information through the improved feature pyramid networks (FPN), and then collects and fuses the output features of different scales through the spatial pyramid pooling (SPP) network. In the feature fusion part, a gated feature-level fusion strategy is designed to further improve the overall classification accuracy by adaptively adjusting the feature contribution of two heterogeneous sources of data through the gated fusion network (GFN). In order to verify the effectiveness of this method, we use two sets of heterogeneous sea ice remote sensing data located in Hudson Bay area for experiments. The experimental results show that compared with other image classification methods, the proposed method fully excavates and integrates the multi-scale and multi-level features in heterogeneous data, effectively distinguishes the feature contribution, and achieves better classification results (97.14% and 95.85%). [ABSTRACT FROM AUTHOR]

In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean. [ABSTRACT FROM AUTHOR]

Information about the state of the Arctic sea ice is becoming increasingly important. This paper describes an approach for automatic retrieval of daily thin sea ice thickness maps around Svalbard. The algorithm uses thermal satellite imagery from MODIS to estimate the surface temperature of the ice and further uses a thermal model of the ice surface to estimate the thickness of the sea ice. The approach is usable for thin sea ice, up to ca. 50 cm thick, during cold weather (freezing) conditions and without cloud cover present. The algorithm is compared with helicopter-borne electromagnetic ice thickness measurements. The comparison yields increasing root-mean-square deviation (RMSD) for thicker ice. The lowest RMSD found is 8.7 cm for ice thickness in the range 10 cm < hi ≤ 20 cm. The highest RMSD found is 25.2 cm for ice thickness in the range 30 cm < hi ≤ 40 cm. The bias shows no such trend, and the overall bias is found to be −5.5 cm. The results show that this is a promising approach, allowing monitoring of thin sea ice thickness at relatively higher spatial resolution. [ABSTRACT FROM AUTHOR]

This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in the northwest of the Antarctic Peninsula. Sentinel-1A Synthetic Aperture Radar (SAR) data were used to estimate fraction images needed for subpixel analysis of passive microwave data acquired by the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS) in the 19H, 19 V, 37H, and 37 V channels and polarizations. Sea ice concentrations estimated by the SLMM approach had a difference of 1.4% (13.6% standard deviation, 15.3% root mean square error) when compared to sea ice concentrations mapped using Sentinel-1A SAR data. The evolution in the total area covered by young and/or first-year ice with concentrations ≥ 15% showed a negative linear trend for the studied region from 1979 to 2018. [ABSTRACT FROM AUTHOR]

As global warming is causing a significant reduction in the extent and the thickness of Arctic sea ice, a narrative for future large-scale commercial shipping in the Arctic has started to evolve. However, while most efforts have aimed to assess the financial benefits along with potential savings in greenhouse gas emissions, few studies have explored risk patterns and safety issues associated with Arctic shipping and its current commercial alternatives in a geospatial manner. This study aims at addressing this gap in the literature, by assessing the risk factors associated with Arctic shipping, in particular, risks along the Northern Sea Route (NSR) connecting European and Asian ports. Its main component includes a global least-cost analysis, aiming to assess global and Arctic risk factors associated with commercial shipping, to derive the least risk paths from 10 major ports in Europe to 10 in Asia. To project the results into the future, and thus assess the future potential of the NSR as a viable and less risky alternative, a dataset on predicted sea ice thickness within 2010–2099, restricted the analysis based on the penetration capabilities of three selected vessel types. Ultimately, the study concludes that maritime transportation, bound for East-Asia, commencing from most northern European ports, could benefit from the NSR, not just in terms of distance savings, cost reduction and less CO2 production, but also as a safer alternative. Lastly, two recommendations drawn from our results aim to assist decision-makers in facilitating future Arctic transportation by addressing accessibility and safety issues. Highlights We explore risk patterns and safety issues associated with Arctic shipping and its current commercial alternatives in a geospatial manner. We assess the future potential of the northern sea route (NSR) as a viable and less risky alternative for European-Asian shipping. We provide a discussion on pros and cons of utilising the NSR in terms of distance savings resulting in cost reduction as well as less CO2 production, and as a safer alternative. [ABSTRACT FROM AUTHOR]

Fully polarimetric (FP) SAR systems offer parameters that describe and quantify the scattering mechanisms from the surface cover. These are usually derived from decomposition of matrices derived from the original scattering matrix from observations at each pixel. Power from scattering mechanisms have potential for retrieval of sea ice information, which cannot be derived using traditional backscatter (magnitude or phase) measured by single- or dual-polarization SAR systems. This study investigates the potential of selected FP parameters that represent the power of three scattering mechanisms, in addition to the total power, in identifying ice types and surface features for operational use. Parameters were obtained from a set of 62 RADARSAT-2 Quad-pol data over Resolute Passage, central Arctic, during the period September-December 2017. A scattering-based color-composite scheme was developed. Analysis of the examined color images was supported by information from regional ice charts and SAR image interpretations from the Canadian Ice Service. Case studies are presented to demonstrate the potential of the proposed color-composite tool. Open water, new ice, multi-year ice and a few surface features including rafted, ridged and smooth/rough surfaces can be identified better in the color images. Physical interpretation of the relative power from the given scattering mechanisms is explained for the relevant ice types and surfaces. [ABSTRACT FROM AUTHOR]

We study a Sturm–Liouville problem in the cross-section of the ocean waveguide covered by pack ice. We prove the basis properties of the eigenfunctions, the convergence of the corresponding Fourier type series, orthogonality relations for the eigenfunctions, and study the dispersion relations of the leading modes for two maximal eigenvalues analytically, numerically, and asymptotically. We prove the continuity and monotonicity of the eigenvalues with respect to the frequency and the speed of propagation, their differentiability with respect to the frequency, and the existence of the cut-off frequency. We prove analytically that these eigenvalues are strictly greater than the eigenvalues for the case of a waveguide with a free surface. Assuming that the speed of propagation varies within the given limits, we find the minimum and maximum of the wavenumbers of these leading modes. We develop a numerical algorithm based on the formalism for the layered media that allows, for a given continuous profile of the speed, to study dispersion relations for the leading eigenvalues. The results of the numerical experiments are in complete agreement with the analytical results. Finally, we outline our results that would be of interest in connection with the models of ice other than pack ice. [ABSTRACT FROM AUTHOR]

Thalassiosira species significantly contribute to phytoplankton communities, forming algal blooms in temperate and polar regions, and are broadly used as model organisms for nanotechnology and as important food sources in the aquaculture industry. Despite being widespread in the world's oceans, data on abundance and seasonal patterns of individual species of Thalassiosira are sparse. In the present study, we have identified Thalassiosira tenera from the northwestern Sea of Japan, using light and scanning electron microscopy (SEM), and molecular genetic analysis of 28S rDNA sequence. Based on field and cultured material, morphological variability of T. tenera was studied; a detailed diagnostic description of the species is provided, and its morphometry is compared with that of the morphologically similar T. exigua. Growth peculiarities of the species were investigated in laboratory culture; variations in frustule morphology occur at different growth stages. The degree of frustule silicification is assumed to depend on the stage of cell development. By SEM observations of field samples, the seasonal dynamics of the species was examined and data on its abundance are provided for the first time. Findings indicate that T. tenera is present year-round in the phytoplankton assemblage in the northwestern Sea of Japan. The species dominates the community from November to February, accounting for more than 60% of the total phytoplankton abundance. The highest numbers, 7,281 cells l−1, were recorded under the sea ice in January, and the lowest numbers, less than 100 cells l−1, occurred in April. In addition, the study gives information on the species ecology. [ABSTRACT FROM AUTHOR]

Climate change is affecting the marine environment in Nunatsiavut, leading to changing sea ice thickness and seasonal timing, and increasing water temperatures. This impacts the lives of Labrador Inuit, whose culture, economy, and history are deeply tied to marine spaces. Recently, research partnerships involving Inuit communities in Nunatsiavut have increased, creating space for Labrador Inuit in large scale marine research agendas. While including Labrador Inuit knowledge is critical for making research relevant to communities, there are challenges to engaging it alongside oceanographic scientific knowledge, as both stem from unique ontologies, at times having different values, scales, and languages of understanding. Boundary work offers a lens to analyze how boundary objects can foster connections between Labrador Inuit knowledge and oceanographic research. This research offers a conceptual exploration of this subject through analysing the co-production of maps representing Labrador Inuit knowledge of ocean features which, as data, were then applied in oceanographic research problems. Framing these maps as boundary objects demonstrates their utility in mobilizing Inuit knowledge into scientific approaches, acknowledging limitations with respect to knowledge that cannot be spatially rendered. [ABSTRACT FROM AUTHOR]

The late early Permian coincided with a short-lived cold episode in Western Australia, which occurred long after the demise of late Paleozoic ice sheets, and is manifested by conspicuous dropstones in corresponding marine strata. In the northern Perth Basin, this time interval is represented by the Carynginia Formation, which has long been regarded as a record of low-energy marginal-marine sedimentation. The low-diversity body-fossil content and impoverished trace-fossil assemblages in the formation were recognised as evidence of low salinities, low dissolved-oxygen levels and high suspended-sediment concentrations presumably related to freshwater influxes into a restricted basin. However, sedimentary facies indicative of river processes have yet to be documented to explain the sedimentological and paleontological character of the formation. This study examines an overlooked sandstone-rich interval of the Carynginia Formation in the Irwin Sub-basin, a marginal half graben faulted against crystalline basement of the Yilgarn Craton and forming part of a series of late Paleozoic intracratonic rift basins in Western Australia. Facies analysis indicates that the lower part of the interval is dominated by storm-generated deposits containing extraformational pebbles and boulders. These deposits are interpreted as recording lower-shoreface sedimentation under the influence of seasonal sea ice, and constitute a shallower, sandstone-rich equivalent of mudstone-prone strata that otherwise characterise the Carynginia Formation. The upper part of the interval consists of river-borne deposits representing friction-dominated deposition of a river-mouth bar, the first direct record of deltaic sedimentation in the Carynginia Formation. The stratal organisation of mouth-bar bedsets is likely the result of successive sediment-laden pulses related to seasonal floods in a shallow marine basin, and the autogenic evolution of the mouth-bar complex in response to fluctuating river discharge and compensational stacking. Formative runoff events were arguably responsible for reductions in salinity and the introduction of turbid buoyant plumes into the shallow sea that were adverse to faunal development. A fluvial influence may explain the sedimentary character and fossil content of coeval mudstone-prone formations in Western Australian rift basins, especially in the vicinity of cratonic margins. The lower Permian (late Artinskian–Kungurian) mudstone-prone Carynginia Formation in the northern Perth Basin has long been regarded as the record of low-energy, marginal-marine conditions in a cold-water seaway. The low-diversity fossil content in the lower part of the formation indicates stressed physico-chemical conditions influenced by freshwater, sediment-laden influxes. The described mouth-bar deposits are the first direct evidence of deltaic sedimentation in the Carynginia Formation, demonstrating that river outflows likely controlled water chemistry and impacted faunal development. [ABSTRACT FROM AUTHOR]

Due to the existence of seasonal or perennial sea ice cover, the determination of the Arctic sea surface is more difficult than that of mid-low latitudinal oceans. Focusing on the sea surface height in the ice-covered region, this paper constructs a new Arctic mean sea surface (MSS) model, named SUST22, by combining the measurements from ICESat and Cryosat-2 missions. The lead detection methods of ICESat and Cryosat-2 are first studied and modified to acquire sea surface measurements with better accuracy. The results have shown that the standard deviation of Cryosat-2-derived Arctic sea surface height is about 3–4 cm in 10-km resolution grids, while the value of ICESat is 5–6 cm. Then the MSS construction procedure is discussed and the SUST22 MSS model is constructed. The new model is compared with the other four Arctic MSS models. The best agreement is found between SUST22 and DTU21 with an average difference of −4.0 ± 5.2 cm. These models are also validated by ICESat-2 samples. The average difference between ICESat-2 and SUST22 is 15.8 ± 7.4 cm, which shows that the new model SUST22 presents better consistency with the ICESat-2 than any of the other models. [ABSTRACT FROM AUTHOR]

Sea ice surface temperature (IST) is an important indicator of environmental changes in the Arctic Ocean. In this study, the relative performance of four mainstream IST records, i.e. airborne IST, infrared radiometer measured IST (IR IST), longwave radiation derived IST (LWR IST), and snow and ice mass balance array buoy derived IST (Buoy IST), were evaluated against the MODIS IST product. Bias, standard deviation (STD), and root mean square error (RMSE) were used to evaluate the data quality. Results revealed that airborne IST had the best accuracy, which was 0.21 K colder than MODIS IST, with STD of 1.46 K and RMSE of 1.47 K. Ground-based ISTs were biased with each other but all warmer than the MODIS IST. The IR IST had the best overall accuracy (bias = 0.55 K; STD = 1.52 K; RMSE = 1.61 K), while the LWR IST was the noisiest measurement with the largest outlier data percent. Besides, co-located IR and LWR ISTs were more consistent than any type of evaluated IST against MODIS IST (correlation coefficient = 0.99). Airborne and IR ISTs are thus the premier choice for monitoring the rapidly changing Arctic sea ice, together with satellite observations. [ABSTRACT FROM AUTHOR]

The rapid decline of Arctic sea ice has been reminding us the significant impacts caused by global warming. However, the other side of the coin is that this opens a window to utilize the Arctic sea routes in the summer seasons, bringing remarkable economic benefits for ocean transportation between Asia and Europe. However, commercial vessels with low ice classes must tackle substantial environmental challenges in the Arctic sea routes, particularly those caused by variable sea ice, even in the melting seasons. Therefore, the science-based support for shipping safety in the Arctic sea routes is being given more prominence. Emerging satellite remote sensing technology plays a critical role in environmental monitoring in the Arctic. This paper reviews state-of-the-art satellite observations on monitoring sea ice and potential applications on supporting shipping activities in the Arctic Ocean. Moreover, we introduced a recently developed system based on satellite observations to support the safe transportation of Chinese cargo vessels in the Arctic northern sea route, demonstrating the efforts by both the science and business communities to promote the development of the polar silk road. [ABSTRACT FROM AUTHOR]

The potential of Arctic routes (ARs) has attracted global attention, and exploiting the Arctic has become an important strategy for many countries. However, there are still some challenges for ships sailing in Arctic ice zones, including harsh marine environments and the insufficient service capacity of sea ice information service systems. To better understand the route changes in the Arctic and extract real-time ship navigation routes, we developed an online interactive route planning system (RouteView) for ships sailing in the Arctic based on big Earth data. RouteView includes two main features: (1) an online calculation interface is provided for optimal routes along the Arctic Northeast Passage (NEP) 60 days into the future by utilizing reinforcement learning (RL) based on sea ice and meteorological data, and (2) an online ice-water classification is established based on synthetic aperture radar (SAR) data and deep learning to help users extract the sea ice distribution in real time. This work can potentially enhance the safety of shipping navigation along the NEP and improve information extraction methods for ARs. [ABSTRACT FROM AUTHOR]

According to the boundary conditions between two adjacent layers, a method for calculating the acoustic reflection coefficient of the layered ice-snow model is proposed, which is called the boundary condition method (BCM). Based on the actual ice parameters chosen from Schwarz and Weeks data, reflection coefficients under different conditions are analyzed. Comparing the BCM with the impedance transfer method (ITM), the results verified the validity of the BCM. After calculating and analyzing the multilayer ice-snow, it can be found that snow layer has more influence on the low-frequency sound waves. Snow layers have varying effects on the sound reflection coefficient depending on ice thickness: the thicker the ice layer is, the less influence the snow layer has on the sound reflection coefficient. The ice reflection coefficient model is combined with the Bellhop sound field simulation model, and the sound field simulation results are compared with the ninth Chinese polar scientific expedition results. [ABSTRACT FROM AUTHOR]

As the sea ice extent steadily decreases, the Arctic region is simultaneously experiencing extensive growth in commercial shipping activities, in areas which previously were considered inaccessible for most ships during large periods of the year, increasing the probability of accidents or incidents occurring. The International Code for Ships Operating in Polar Waters (The Polar Code) states that resources shall be provided to support survival following abandoning a ship; desalting apparatus is proposed for the provision of the recommended amount of freshwater. However, previous studies have shown that the expected performance criteria for survival equipment are significantly reduced in cold climate conditions. In this paper, we present and discuss the results of testing SOLAS approved desalting apparatus at low temperatures in a controlled and enclosed environment, studying the equipment's performance capabilities. [ABSTRACT FROM AUTHOR]

To investigate the seismic performance of a wind turbine that is influenced by both the ice load and the seismic load, the research proposes a numerical approach for simulating the seismic behavior of a wind turbine on a monopile foundation. First, the fluid–solid coupled equation for the water–ice–wind turbine is simplified by assigning reasonable boundary conditions and solving the motion equation, and the seismic motion equation of the wind turbine is developed. Then, on this basis, we propose a simplified 3D numerical model that can simulate the interactions among the wind turbine, water and sea ice. By conducting shaking table tests, the results demonstrate that the established numerical model is effective. Finally, we investigate the effect of the boundary range and ice thickness on the seismic performance of a turbine under near-field and far-field seismic actions. Research results illustrate that ice changes the distribution form of the hydrodynamic pressure. Moreover, the thickness of the ice greatly influences the seismic behavior, while the influence of the ice boundary range is only within a certain range. Additionally, the ice load decreases the energy-dissipating capacity of the wind turbine, so the earthquake resilience of the wind turbine is significantly decreased. [ABSTRACT FROM AUTHOR]

The refractive index of sea ice can be estimated using the Debye relaxation model. However, the practical use of this model is difficult because it requires a large amount of information as inputs. To resolve this issue, two different approaches have been developed to retrieve the Arctic basin-scale refractive index from satellite passive microwave measurements using the bulk radiative transfer concept. One is the simplified three-layer sea ice radiative transfer (3SRT) approach and the other is the Brewster's angle approximation (BAA) approach. Although these methods are different, both find polarized emissivities for the microwave emitting layer of sea ice to estimate the refractive index using a relationship between polarized emissivities and the refractive index. A typographical erroneous version of the equation for this relationship has been used in numerous studies without noting the errors. This study provides the correct equations for the relationship. The refractive index obtained using the 3SRT method is within a reasonable range of 1.2–1.8. However, the refractive index obtained using the BAA approach shows too high values ranging from 1.2 to 3.2. The sensitivity of the approaches to variation in the emissivity is analysed. The BAA method is sensitive to the surface emissivity because of the assumptions considered. The refractive index of sea ice obtained from the 3SRT approach is compared with that obtained from the Debye relaxation model. The results show that a plausible refractive index over Arctic sea ice can be estimated from satellite passive microwave measurements using the 3SRT method. [ABSTRACT FROM AUTHOR]

Arctic sea ice and its snow cover are important components of the cryosphere and the climate system. A series of in situ snow measurements were conducted during the seventh Chinese Arctic expedition in summer 2016 in the western Arctic Ocean. In this study, we made an analysis of snow features on Arctic sea ice based on in situ observations and the satellite-derived parameter of snow grain size from MODIS spectral reflectance data. Results indicate that snow depth on Arctic sea ice varied between 19 and 241 mm, with a mean value of 100 mm. The mean density of the snow was 340.4 kg/m3 during the expedition, which was higher than that reported in previous literature. The measurements revealed that a depth hoar layer was widely developed in the snow, accounting for 30%∼50% of the total snow depth. The equivalent snow grain size was small on the surface and large at the bottom in snow pits. The average relative error between MODIS-derived snow grain size and in situ measured surface snow grain size is 14.6%, indicating that remote sensing is a promising method to obtain large-scale information of snow grain size on Arctic sea ice. [ABSTRACT FROM AUTHOR]