Sea Ice Characteristics Research Articles

Прогноз толщины ледяного покрова в прибрежных областях Карского и Охотского морей

Based on the CICE viscoplastic sea ice model, the ice cover of the Gulf of Ob and the northwestern Okhotsk Sea was simulated for the winter seasons of 2021/2022, 2022/2023, and 2023/2024. The description of the seasonal variability of sea ice characteristics in these areas and examples of their numerical modeling are given. Using atmospheric forcing based on the WRF-ARW nonhydrostatic atmospheric model, the sea ice thickness in the northwestern Okhotsk Sea was predicted for April 2024. A comparison of the forecasts with the ice maps of the Hydrometcentre of Russia and sea ice thickness measurements at the Ayan and Bolshoy Shantar stations gave satisfactory results.

Exploring the Use of Fengyun-3 Meteorological Satellite for Monitoring Sea Ice to Provide Services for Polar Navigation

This paper explores the feasibility and effectiveness of using the Fengyun-3 meteorological satellite to monitor sea ice, providing services for ships navigating in polar regions. Firstly, it analyzes the impact of Arctic sea ice changes on ship navigation and the importance of sea ice monitoring in route planning. Next, it provides a detailed introduction to the data sources and processing methods of the Fengyun-3 satellite, including radiometric calibration, geometric correction, image registration, and cropping. Subsequently, it discusses the characteristics of sea ice in the visible spectrum and successfully extracts sea ice information using MERSI-II data with land, cloud, and seawater masking techniques. The study indicates that the comprehensive use of multi-spectral data and other observation methods can significantly enhance sea ice monitoring capabilities. In the future, integrating more advanced technologies is expected to achieve refined identification and short-term prediction of sea ice movement, thereby providing more scientific and efficient support for ships navigating in polar regions, enhancing navigation safety and efficiency, and offering a scientific basis for the development of Arctic shipping routes.

The radiative and geometric properties of melting first-year landfast sea ice in the Arctic

Abstract. In polar regions, sea ice is a crucial mediator of the interaction between Earth's atmosphere and oceans. Its formation and breakup is intimately connected with large-scale climatic processes, local weather patterns, and the use of sea ice in coastal Arctic regions by Indigenous people. In order to investigate the physical phenomena at the heart of this process, a set of targeted, intensive observations were made over spring sea ice melt and breakup in Kotzebue Sound, Alaska. These observations were planned and executed through a collaborative effort in which an Indigenous Elder advisory council from Kotzebue and scientists participated in co-production of hypotheses and observational research, including a stronger understanding of the physical properties of sea ice during spring melt. Here we present the results of observations performed using high-endurance, fixed-wing uncrewed aerial vehicles (UAVs) containing custom-built scientific payloads. Repeated flights over the measurement period captured the early stages of the transition from a white, snow-covered state to a broken-up, bare blue-green state. We found that the reflectance of sea ice features depends strongly on their size. Snow patches get darker as they get smaller, an effect owed to the geometric relationship between the bright interior and the darker, melting feature edges. Conversely, bare patches get darker as they get larger. For the largest ice features observed, bare blue-green ice patches were found to be ≈ 20 % less reflective than average across all observational cases, while large snowy white ice patches were found to be ≈ 20 % more reflective than that same average.

Exploring non-Gaussian sea ice characteristics via observing system simulation experiments

Exploring non-Gaussian sea ice characteristics via observing system simulation experiments

Retrieval of sea ice thickness using FY-3E/GNOS-II data

Sea ice, a significant component in polar regions, plays a crucial role in climate change through its varying conditions. In Global Navigation Satellite System-Reflectometry (GNSS-R) studies, the observed surface reflectivity Γ serves as a tool to examine the physical characteristics of sea ice covers. This facilitates the large-scale estimation of first-year ice thickness using a two-layer sea ice-seawater medium model. However, it is important to note that when Sea Ice Thickness (SIT) becomes thicker, the accuracy of SIT retrieval via this two-layer model begins to decline. In this paper, we present a novel application of a spaceborne GNSS-R technique to retrieve SIT based on a three-layer model using the data from Fengyun-3E (FY-3E). Soil Moisture Ocean Salinity (SMOS) data are treated as the reference. The performance of the proposed three-layer model is evaluated against a previously established two-layer model for SIT retrieval. The analysis used the sea ice data from 2022 and 2023 with SITs less than 1.1 m. By comparing the retrieved SITs against reference values, the three-layer model achieved a Root Mean Square Error (RMSE) of 0.149 m and Correlation Coefficient (r) of 0.830, while the two-layer model reported the RMSE of 0.162 m and r value of 0.789. A scheme incorporating both models yielded superior results than either individual model, with the RMSE of 0.137 m and r reaching up to 0.852. This study is the first application of FY-3E for GNSS-R SIT retrieval, combining the advantages of a two-layer model and a three-layer model and extending the precision of GNSS-R retrieval for SIT to within 1.1 m. This provides a good reference for the future studies on GNSS-R SIT retrieval.

Characteristics of late summer Arctic brash sea ice and its melting effect on the surface-water biogeochemistry of the Chukchi Shelf and Canada Basin

To understand the impact of the melting of late summer Arctic brash ice on the surface waters of the Chukchi Sea, we collected sea-ice samples during 2021. Floating sea ice was collected by a wire mesh pallet cage from the side of the R/V Mirai. We measured physical and biogeochemical parameters such as salinity, oxygen stable isotopic ratios, turbidity, and concentrations of chlorophyll-a and nutrients. The samples of brash ice were multiyear ice based on satellite back-trajectory analysis. Comparison of nutrient concentrations in brash ice with those of seawater samples from the temperature minimum layer similar to the water in the sea ice originated suggested that the characteristics of the brash ice were greatly affected by biogeochemical processes such as remineralization. The extremely high turbidity and concentrations of chlorophyll-a observed in the brown/green ice samples reflected the impact of sediment as well as the influence of biological activities. The N:P ratios were less than 1 because of the high phosphate concentrations, even though the ammonium concentrations were high. We hypothesized that this low N:P ratio reflected the combined effects of the accumulation of nutrients due to remineralization in the biofilm and differences of remineralization rate and adsorption features of nitrogen and phosphorus. Based on the high nitrate and ammonium concentrations in the sea-ice samples, we postulated a marked impact of sea-ice meltwater on the nitrogen cycle in the nitrate-depleted surface waters of the Chukchi Sea during late summer. We estimated that meltwater nitrogen could support 0.3%–2.6% of primary production in the northern Chukchi Sea. Our results suggest that high-turbidity ice will play an important role as a source of nutrients to the ocean during melting of sea ice, and understanding its distribution, amount, and geochemical characteristics is vital.

Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture

Shipborne sea ice detection aboard icebreakers plays a paramount role in polar navigation. The continuous evolution of deep learning semantic segmentation networks has promoted the advancement of sea ice detection tasks. At this stage, there are relatively few studies on shipboard sea ice detection, and the accuracy of polar sea ice detection will be reduced due to problems such as blurred sea fog and indistinct boundaries. In this study, a shipboard sea ice detection dataset is constructed, and a sea ice detection method that combines multi-branch attention feature alignment and multi-scale feature extraction is proposed. The heterogeneous receptive field enhancement atrous spatial convolution pooling pyramid module is designed, and the feature alignment module based on the attention mechanism is constructed, which strengthens the model’s extraction of sea ice features and elevates representation performance. Experimental results underscore the heightened precision of our approach in sea ice detection, to some extent alleviating the issue of missed detections in new ice. It constitutes a positive contribution towards advancing shipborne sea ice detection in polar environments.

Investigation of the Recent Ice Characteristics in the Bohai Sea in the Winters of 2005–2022 Using Multi-Source Data

The safety of winter activities in the Bohai Sea requires more detailed information on ice characteristics and a more refined ice zone division. In the present study, 1/12°-resolution sea ice characteristic data were obtained based on the NEMO-LIM2 ice–ocean coupling model that assimilated MODIS satellite sea ice observations from the years of 2005 to 2022 to acquire new sea ice hindcasting data. On this basis, the ice period, ice thickness, ice concentration, ice temperature, ice salinity, and design ice thickness for different return periods in the 1/4°-resolution refined zoning were analyzed, which were then compared with the sea ice characteristics in the previous 21-ice-zone standard. The distribution of ice temperature and ice salinity was closely related to the distribution of ice thickness. The results of ice period, ice thickness, and ice concentration, as well as design ice thickness for different return periods, and the comparison with the previous 21-ice-zone standards, showed that the ice condition on the west coast of the Bohai Sea has significantly reduced.

Distribution Characteristics and Influencing Factors of Sea Ice Leads in the Weddell Sea, Antarctica

The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from MODIS thermal images and then generated a daily SIL map for 2015 and 2022 by utilizing the iterative threshold method on the optimised MOD35 cloud-masked IST. The results showed that SIL variations in the Weddell Sea presented remarkable seasonal characteristics. The trend of the SIL area exhibited an initial rise followed by a decline from January to December, characterised by lower values in spring and summer and higher values in fall and winter. SILs in the Weddell Sea were predominantly concentrated between 70~78°S and 60~30°W. The coastal spatial distribution density of the SILs exceeded that of offshore regions, peaking near the Antarctic Peninsula and then near Queen Maud Land. The SIL variation was mainly influenced by dynamical factors, and there were strong positive correlations between the wind field, ocean currents, and sea-ice motion.

Modelled frontal ablation and velocities at Kronebreen, Svalbard, are sensitive to the choice of submarine melt rate scenario

Abstract Both submarine melt and calving are important for the overall mass balance of marine-terminating glaciers, but uncertainty is rife with regards to the magnitude of the processes. Modelling allows for these processes to be investigated without the need to visit inaccessible ice marginal zones. This study looks at the impact of different submarine melt and sea-ice back pressure scenarios on modelled calving activity and dynamics at Kronebreen, Svalbard, by running separate summer and winter simulations with various submarine melt parameterisations and sea-ice characteristics. It is found that submarine melt is an important driver of seasonal variation in modelled glacier dynamics and calving activity, with the choice of sliding law also exerting a significant influence on results.

Optimization of the k-nearest-neighbors model for summer Arctic Sea ice prediction

The Arctic sea ice plays a significant role in climate-related processes and has a considerable effect on humans, however accurately predicting the Arctic sea ice concentration is still challenging. Recently, with the rise and development of artificial intelligence, big data technology, machine learning has been widely used in the field of sea ice prediction. In this study, we utilized a sea ice concentration dataset obtained from satellite remote sensing and applied the k-nearest-neighbors (Ice-kNN) machine learning model to forecast the summer Arctic sea ice concentration and extent on 122 days prediction. Based on the physical characteristics of summer sea ice, different algorithms are employed to optimize the prediction model. A drift-ice correction algorithm is designed to address the unrealistic drift ice around the sea ice edge, and a distance function combined with the spatial pattern is proposed to enhance similarity detection. Deseasonalized and detrended sea ice datasets and an expanded training library are also utilized to improve model performance. Furthermore, sensitivity analysis reveals a positive impact of net surface heat flux on sea ice prediction. The modified Ice-kNN model outperforms climatological and anomaly persistence predictions, demonstrating its applicability to predicting summer Arctic sea ice. The September sea ice extent hindcasts of the modified Ice-kNN model are compared to a variety of models submitted to the Sea Ice Prediction Network, underscoring its potential to improve predictive skill for Arctic sea ice.

Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.

Diverse marine benthic communities and reduced anthropogenic contaminants near Scott Base (Hut Point Peninsula, Ross Island, Antarctica)

AbstractAntarctic marine ecosystems are largely thought to be among the planet’s least impacted, yet habitats adjacent to research stations can be heavily polluted. Despite long-term monitoring and remediation being high priorities for international environmental protection, the ecological responses to contaminants and stressors remains poorly characterised, limiting our ability to manage and reduce human impacts. This study compares epifaunal community composition at two sites close to Scott Base with a reference site further away. We couple these data with environmental characteristics, including current data, sediment properties, and contaminant concentrations within the sediment and in the tissues of two epifaunal species, both from this survey and those previously reported. Previously high concentrations of polychlorinated biphenyls and polyaromatic hydrocarbons are now undetectable and concentrations of heavy metals were mostly similar or reduced compared to previous data from 2002. High within-site variance suggests localised contamination footprints from being situated within a deposition zone and/or from the erosion of anthropogenic debris. Despite the persistence of some metals (arsenic, copper and lead) at one site, our study revealed high biodiversity at all three sites (22–28 taxa per 0.25 m2). Benthic community structure was influenced by a combination of factors, including sea ice characteristics, sediment type and habitat complexity. Overall, our study clearly highlights the influence of human activities on the benthos in adjacent marine habitats. The established monitoring protocols coupling diver and remote sampling will enable regular monitoring, filling a critical need for time-series data in order to detect long-term trends and interactions with climate drivers.

Research on the Directional Characteristics of the Reflectance of Oil-Contaminated Sea Ice

Remote sensing has been widely used for oil spill monitoring in open waters. However, research on remote sensing monitoring of oil spills in ice-infested sea waters (IISWs) is still scarce. The spectral characteristics of oil-contaminated sea ice (OCSI) and clean sea ice (CSI) and their differences are an important basis for oil spill detection using visible/near-infrared (VNIR) remote sensing. Such features and differences can change with the observation geometry, affecting the identification accuracy. In this study, we carried out multi-angle reflection observation experiments of oil-contaminated sea ice (OCSI) and proposed a kernel-driven bidirectional reflectance distribution function (BRDF) model, Walthall–Ross thick-Litransit-Lisparse-r-RPV (WaRoLstRPV), which takes into account the strong forward-scattering characteristics of sea ice. We also analyzed the preferred observation geometry for oil spill monitoring in IISWs. In the validation using actual measured data, the proposed WaRoLstRPV performed well, with RMSEs of 0.0031 and 0.0026 for CSI and OCSI, respectively, outperforming the commonly used kernel-driven BRDF models, Ross thick-Li sparse (R-LiSpr), QU-Roujean (Qu-R), QU-Lisparse R-r-RPV (Qu-LiSpr-RrRPV), and Walthall (Wa). The observation geometry with a zenith angle around 50° and relative azimuth ranging from 250° to 290° is preferred for oil spill detection in IISWs.

Challenges in the Description of Sea Ice for a Kilometer-Scale Weather Forecasting System

Abstract Simulation of atmosphere–ocean–ice interactions in coupled Earth modeling systems with kilometer-scale resolution is a new challenge in operational numerical weather prediction. This study presents an assessment of sensitivity experiments performed with different sea ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea ice detection of the higher-resolution retrievals based on synthetic aperture radar instruments. We perform sensitivity experiments with sea ice concentration fields of 1) the global ECMWF-IFS forecast system, 2) a newly developed multisensor product processed through a coupled sea ice–ocean forecasting system, and 3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100) km scales in the northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and toward the Fram Strait. These differences have a direct impact on the modeled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2-m air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multisensor product; however, south of Svalbard this product has a negative impact. The different sea ice products are resulting in changes of the surface turbulent heat flux of up to 400 W m−2, which in turn results in T2M variations of up to 5°C. Over a 2-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea ice. Significance Statement Weather forecasting in polar regions requires an accurate description of sea ice properties due to the very important atmosphere–ocean–ice interactions. With the increasing resolution of weather forecasting systems, there is also a need to advance the resolution of the sea ice characteristics in the models. This is, however, not straightforward due to various issues in the sea ice satellite products. This study explores new products and approaches to integrate high-resolution sea ice in a weather prediction system. We find that the model is sensitive to the choice of the sea ice product and that it is still challenging to provide an accurate sea ice field on a kilometer-scale resolution.

Improving numerical accuracy for the viscous-plastic formulation of sea ice

Accurate modeling of sea ice dynamics is critical for predicting environmental variables and is important in applications such as navigating ice breaker ships, and as such there have been numerous investigations on modeling sea ice dynamics. The 1979 viscous-plastic (VP) sea ice model introduced by W.D. Hibler remains the most widely accepted. Due to its highly nonlinear features, the Hibler model is intrinsically challenging for computational solvers. This study therefore focuses on improving the numerical accuracy of the VP sea ice model. Since the poor convergence observed in existing numerical simulations stems from the nonlinear nature of the VP formulation, this investigation proposes using the celebrated weighted essentially non-oscillatory (WENO) scheme – as opposed to the frequently employed centered difference (CD) scheme – for the spatial derivatives in the VP sea ice model. We then proceed to numerically demonstrate that WENO yields higher-order convergence for smooth solutions, and that furthermore it is able to resolve the discontinuities in the sharp features of sea ice covers – something that is not possible using CD methods. Finally, our proposed framework introduces a potential function method that utilizes the phase field method that naturally incorporates the physical restrictions of ice thickness and ice concentration in transport equations, resulting in modified transport equations which include additional forcing terms. Our method does not require post-processing, thereby avoiding the possible introduction of discontinuities and corresponding negative impact on the solution behavior. Numerical experiments are provided to demonstrate the efficacy of our new methodology.

Biogeochemical characteristics of brash sea ice and icebergs during summer and autumn in the Indian sector of the Southern Ocean

We analyzed biogeochemical components of brash ice, originating from sea ice and icebergs, collected in the Indian sector of the Southern Ocean during the summer and autumn of 2018–2020. Ice samples, collected from seawater by net or cage methods, were melted in the dark under cool conditions to measure physical and biogeochemical components such as salinity, stable oxygen isotopes, nutrients, and chlorophyll-a. We compared brash ice parameters with those of seawater samples from the temperature minimum layer, corresponding to the water in which the sea ice originated, to examine the effects of processes such as brine drainage, snow-ice formation, and biological activities on the biogeochemical components in sea ice. Samples from icebergs (ice formed on land) had salinity of zero and low concentrations of all other components, suggesting that the atmospheric deposition of nutrients is minimal in this clean environment. However, sea ice samples had a wide range of values for each parameter. Our results show that meteoric water makes a smaller contribution to sea ice than it typically does to multi-year landfast ice, and there is no correlation between this meteoric water contribution and nutrient concentrations, which suggests that the contribution of snow-ice formation to nutrients within sea ice is subordinate to the role of biological processes. Nutrient and chlorophyll-a concentrations in our brash ice samples are of similar magnitude to those in sea ice samples collected in the same area by coring of thick pack ice. Our data represent end-member values that may be useful to estimate the respective contributions of snow, sea ice, and seawater to surface water samples.

An inventory and evaluation of unmanned aerial systems for offshore surveys of marine mammals

A literature review, internet searches and communications with personnel working with unmanned aerial systems (UAS) were used to identify the capabilities of UAS throughout the world. We assessed their ability to replace manned aerial surveys for marine mammals, sea turtles and seabirds and to monitor, in real time, sea ice and other physical features that might influence marine mammal distribution. The vast majority of the systems identified were either too expensive or their capabilities did not meet minimum standards necessary to perform the tasks required of them in real time. Eight systems were identified that might be able to perform some of the desired tasks. Several other systems had similar capabilities but had not been tested or would require upgrades. Installation of high-definition (HD) video and better stabilisation systems would improve UAS performance. It is recommended that development of HD video with real-time data transmission and improved stabilisation systems for UAS be pursued and that side-by-side comparisons of a few of the best systems be conducted.

Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data

Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data.

Why Does Arctic Sea Ice Respond More Evidently than Antarctic Sea Ice to Climate Change?

The current climate change episode has impacted sea ice in the 2 polar regions differently. In the Arctic, remarkable sea ice extent and thickness declines have been observed with a stunning depletion rate of old ice. No similar changes have been observed in the Antarctic. In this paper, the question posed in the title is addressed by reviewing findings retrieved from previous publications. The paper starts by identifying key geographic and climatic features and sea ice characteristics in the 2 polar regions and summarizing relevant recent records. It then proceeds by investigating interactions between sea ice and environmental factors, including atmospheric, oceanic, and dynamic aspects in each region, as well as the increasing number of icebergs in Antarctica. It is concluded that peculiarities of each polar region render the response to climate change differently. Researchers should not apply scenarios regarding the impacts of climate change on Arctic sea ice (i.e., retreat) to Antarctic sea ice. Instead of asking why Antarctic sea ice has not responded to climate change in the same way as Arctic ice, a more reasonable question could be why Arctic ice changes are yielding an annual cycle that resembles that of Antarctic ice. Under current global warming conditions, old ice entrapment within the Arctic basin is relaxed. This could result in Arctic sea ice becoming predominantly seasonal during winter and almost completely melted during summer, which is the current state of Antarctic sea ice.