Sea Ice Characteristics Research Articles

COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE

Abstract. The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice.

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.

Prey selection of polar bears in Foxe Basin, NU, Canada: evidence of dietary flexibility in a specialized predator

AbstractEcological flexibility of a species reflects its ability to cope with environmental change. Although polar bears (Ursus maritimus) are experiencing changes in foraging opportunities due to sea ice loss, regional prey availability and environmental conditions will influence the rate and severity of these effects. We examined changes in polar bear diet and the influence of sea ice characteristics in Foxe Basin over an 18-year period. We combined previous fatty acid data from bears harvested from 1999 to 2003 (n = 82) with additional data from 2010 to 2018 (n = 397). Polar bear diets were diverse; however, ringed seal (Pusa hispida) was the primary prey throughout the sample period. Prey contribution varied temporally and spatially, and by intrinsic factors, while the frequency of prey in diets varied over time suggesting that diet estimates reflect the variability in available prey. Bowhead whale (Balaena mysticetus), although still a minor dietary component, has more than doubled in frequency of occurrence in diets in recent years in association with increased scavenging opportunities. Higher dietary levels of beluga whale (Delphinapterus leucas) and harbour seal (Phoca vitulina) were linked to later breakup date suggesting heavier ice conditions may promote access to both prey species. The flexible foraging strategies of bears in Foxe Basin may help mitigate their vulnerability to changes in prey distribution and habitat conditions. Our results provide insights into the importance of alternative and supplemental food sources for polar bears during phenological changes in ice conditions that will likely have consequences to Arctic community structure as warming continues.

HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation

On 22 December 2020, HISEA-1, the first C-band SAR small satellite for ocean remote sensing, was launched from the coastal Wenchang launch site. Though small in weight, the images it produced have a high spatial resolution of 1 m and a large observation width of 100 km. The first batch of images obtained within the first week after the launch confirmed the rich information in the data, including sea ice, wind, wave, rip currents, vortexes, ships, and oil film on the sea, as well as landmark buildings. Furthermore, geometric characteristics of sea ice, wind vector, ocean wave parameter, 3D features of buildings, and some air-sea interface phenomena in dark spots could also be detected after relevant processing. All these indicate that HISEA-1 could be a reliable, remarkable, and powerful instrument for observing oceans and lands.

Sizes and Shapes of Sea Ice Floes Broken by Waves–A Case Study From the East Antarctic Coast

The floe size distribution (FSD) is an important characteristics of sea ice, influencing several physical processes that take place in the oceanic and atmospheric boundary layers under/over sea ice, as well as within sea ice itself. Through complex feedback loops involving those processes, FSD might modify the short-term and seasonal evolution of the sea ice cover, and therefore significant effort is undertaken by the scientific community to better understand FSD-related effects and to include them in sea ice models. An important part of that effort is analyzing the FSD properties and variability in different ice and forcing conditions, based on airborne and satellite imagery. In this work we analyze a very high resolution (pixel size: 0.3 m) satellite image of sea ice from a location off the East Antarctic coast (65.6°S, 101.9°E), acquired on February 16, 2019. Contrary to most previous studies, the ice floes in the image have angular, polygonal shapes and a narrow size distribution. We show that the observed FSD can be represented as a weighted sum of two probability distributions, a Gaussian and a tapered power law, with the Gaussian part clearly dominating in the size range of floes that contribute over 90% to the total sea ice surface area. Based on an analysis of the weather, wave and ice conditions in the period preceding the day in question, we discuss the most probable scenarios that led to the breakup of landfast ice into floes visible in the image. Finally, theoretical arguments backed up by a series of numerical simulations of wave propagation in sea ice performed with a scattering model based on the Matched Eigenfunction Expansion Method are used to show that the observed dominating floe size in the three different regions of the image (18, 13 and 51 m, respectively) agree with those expected as a result of wave-induced breaking of landfast ice.

Review of research on ice loading of offshore structures in level ice fields

The physical and mechanical properties of sea ice and the failure modes of ice sheets significantly affect the ice loading of offshore structures. This paper first introduces the physical and mechanical characteristics of sea ice, then describes the physical processes of the interaction of ice sheets with offshore structures. Finally, the progress of research on the ice loading of offshore structures is briefly analyzed, including the three main approaches of formula estimation, experimental testing and numerical simulation. By discussing the applicability of formula estimation, the research focus and difficulties of experimental testing, and the development trends of numerical simulation, we find that the model testing of ice loading is an effective method. This paper covers the research trends and problems faced by ice loading on offshore structures, and it can provide certain guiding references the related research of scholars in the field.

Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal Features Learning

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks.

The Best of Both Worlds: Connecting Remote Sensing and Arctic Communities for Safe Sea Ice Travel


 Northern communities are increasingly interested in technology that provides information about the sea ice environment for travel purposes. Synthetic aperture radar (SAR) remote sensing is widely used to observe sea ice independently of sunlight and cloud cover, however, access to SAR in northern communities has been limited. This study 1) defines the sea ice features that influence travel for two communities in the Western Canadian Arctic, 2) identifies the utility of SAR for enhancing mobility and safety while traversing environments with these features, and 3) describes methods for sharing SAR-based maps. Three field seasons (spring and fall 2017 and spring 2018) were used to engage residents in locally guided research, where applied outputs were evaluated by community members. We found that SAR image data inform and improve sea ice safety, trafficability, and education. Information from technology is desired to complement Inuit knowledge-based understanding of sea ice features, including surface roughness, thin sea ice, early and late season conditions, slush and water on sea ice, sea ice encountered by boats, and ice discontinuities. Floe edge information was not a priority. Sea ice surface roughness was identified as the main condition where benefits to trafficability from SAR-based mapping were regarded as substantial. Classified roughness maps are designed using thresholds representing domains of sea ice surface roughness (smooth ice/maniqtuk hiku, moderately rough ice/maniilrulik hiku, rough ice/maniittuq hiku; dialect is Inuinnaqtun). These maps show excellent agreement with local observations. Overall, SAR-based maps tailored for on-ice use are beneficial for and desired by northern community residents, and we recommend that high-resolution products be routinely made available in communities.

Determination of the indirect physical and mechanical characteristics of sea ice – ulmate strength in bending

Determination of the indirect physical and mechanical characteristics of sea ice – ulmate strength in bending

Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

In this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples and a relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using a limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semisupervised reference methods.

Physical and optical characteristics of sea ice in the Pacific Arctic Sector during the summer of 2018

The reduction in Arctic sea ice in summer has been reported to have a significant impact on the global climate. In this study, Arctic sea ice/snow at the end of the melting season in 2018 was investigated during CHINARE-2018, in terms of its temperature, salinity, density and textural structure, the snow density, water content and albedo, as well as morphology and albedo of the refreezing melt pond. The interior melting of sea ice caused a strong stratification of temperature, salinity and density. The temperature of sea ice ranged from −0.8°C to 0°C, and exhibited linear cooling with depth. The average salinity and density of sea ice were approximately 1.3 psu and 825 kg/m3, respectively, and increased slightly with depth. The first-year sea ice was dominated by columnar grained ice. Snow cover over all the investigated floes was in the melt phase, and the average water content and density were 0.74% and 241 kg/m3, respectively. The thickness of the thin ice lid ranged from 2.2 cm to 7.0 cm, and the depth of the pond ranged from 1.8 cm to 26.8 cm. The integrated albedo of the refreezing melt pond was in the range of 0.28–0.57. Because of the thin ice lid, the albedo of the melt pond improved to twice as high as that of the mature melt pond. These results provide a reference for the current state of Arctic sea ice and the mechanism of its reduction.

The Marine Hindcast and Forecast System for Diagnosis and Prediction of Hydrometeorological Characteristics of the Caspian Sea and Forecast Verification Based on Field Measurements

The possibility of using the Marine Hindcast and Forecast System (MHFS) developed at Zubov State Oceanographic Institute for short-term forecasting of hydrometeorological and ice characteristics of the Caspian Sea is demonstrated. The presented implementation of the system is based on the regional nonhydrostatic atmospheric model WRF (Weather Research and Forecast Model) and model of marine circulation INMOM (Institute of Numerical Mathematics Ocean Model). The system performance is assessed for the period from January 1 to March 31, 2017 by comparing the forecasts of hydrometeorological characteristics with observational data from hydrometeorological stations. The quality of reproducing sea ice characteristics based on ESIMO objective data is also evaluated.

The Southern Annular Mode (SAM) influences phytoplankton communities in the seasonal ice zone of the Southern Ocean

Abstract. Ozone depletion and climate change are causing the Southern Annular Mode (SAM) to become increasingly positive, driving stronger winds southward in the Southern Ocean (SO), with likely effects on phytoplankton habitat due to possible changes in ocean mixing, nutrient upwelling, and sea ice characteristics. This study examined the effect of the SAM and 12 other environmental variables on the abundance of siliceous and calcareous phytoplankton in the seasonal ice zone (SIZ) of the SO. A total of 52 surface-water samples were collected during repeat resupply voyages between Hobart, Australia, and Dumont d'Urville, Antarctica, centred around longitude 142∘ E, over 11 consecutive austral spring–summer seasons (2002–2012), and spanning 131 d in the spring–summer from 20 October to 28 February. A total of 22 taxa groups, comprised of individual species, groups of species, genera, or higher taxonomic groups, were analysed using CAP analysis (constrained analysis of principal coordinates), cluster analysis, and correlation. Overall, satellite-derived estimates of total chlorophyll and measured depletion of macronutrients both indicated a more positive SAM was associated with greater productivity in the SIZ. The greatest effect of the SAM on phytoplankton communities was the average value of the SAM across 57 d in the previous austral autumn centred around 11 March, which explained 13.3 % of the variance in community composition in the following spring–summer. This autumn SAM index was significantly correlated pair-wise (p<0.05) with the relative abundance of 12 of the 22 taxa groups resolved. A more positive SAM favoured increases in the relative abundance of large Chaetoceros spp. that predominated later in the spring–summer and reductions in small diatom taxa and siliceous and calcareous flagellates that predominated earlier in the spring–summer. Individual species belonging to the abundant Fragilariopsis genera responded differently to the SAM, indicating the importance of species-level observation in detecting SAM-induced changes in phytoplankton communities. The day through the spring–summer on which a sample was collected explained a significant and larger proportion (15.4 %) of the variance in the phytoplankton community composition than the SAM, yet this covariate was a proxy for such environmental factors as ice cover and sea surface temperature, factors that are regarded as drivers of the extreme seasonal variability in phytoplankton communities in Antarctic waters. The impacts of SAM on phytoplankton, which are the pasture of the SO and principal energy source for Antarctic life, would have ramifications for both carbon export and food availability for higher trophic levels in the SIZ of the SO.

Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery

Melt ponds on sea ice play an important role in the seasonal evolution of the summer ice cover. In this study we present two machine learning algorithms, one (multi-layer neural network) for the retrieval of melt pond binary classification and another (multinomial logistic regression) for melt pond fraction using moderate resolution visible satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). To minimize the impact of the anisotropic reflectance characteristics of sea ice and melt ponds, normalized MODIS band reflectance differences from top-of-the-atmosphere (TOA) measured reflectances were used. The training samples for the machine learning were based on MODIS reflectances extracted for sea ice, melt ponds and open water classifications based on high resolution (~2 m) WorldView (WV) data. The accuracy assessment for melt pond binary classification and fraction is further evaluated against WV imagery, showing mean overall accuracy (85.5%), average mean difference (0.09), and mean RMSE (0.18). In addition to cross-validation with WV, retrieved melt pond data are validated against melt pond fractions from satellite and ship-based observations, showing average mean differences (MD), root-mean-square-error (RMSE), and correlation coefficients (R) of 0.05, 0.12, and 0.41, respectively. We further investigate a case study of the spectral characteristics of melt ponds and ice during refreezing, and demonstrate an approach to mask out refrozen pixels by using yearly maps of melt onset and freeze-up data together with ice surface temperatures (IST). Finally, an example of monthly mean pan-Arctic melt pond binary classification and fraction are shown for July 2001, 2004, 2007, 2010, 2013, 2016, and 2019. Bulk processing of the entire 20 years of MODIS data will provide the science community with a much needed pan-Arctic melt pond data set.

Causal Interactions between Southern Ocean Polynyas and High-Latitude Atmosphere–Ocean Variability

AbstractWeddell Sea open-ocean polynyas have been observed to occasionally release heat from the deep ocean to the atmosphere, indicating that their sporadic appearances may be an important feature of high-latitude atmosphere–ocean variability. Yet, observations of the phenomenon are sparse and many standard-resolution models represent these features poorly, if at all. We use a fully coupled, synoptic-scale preindustrial control simulation of the Energy Exascale Earth System Model (E3SMv0-HR) to effectively simulate open-ocean polynyas and investigate their role in the climate system. Our approach employs statistical tests of Granger causality to diagnose local and remote drivers of, and responses to, polynya heat loss on interannual to decadal time scales. First, we find that polynya heat loss Granger causes a persistent increase in surface air temperature over the Weddell Sea, strengthening the local cyclonic wind circulation. Along with responding to polynyas, atmospheric conditions also facilitate their development. When the Southern Ocean experiences a rapid poleward shift in the circumpolar westerlies following a prolonged negative phase of the southern annular mode (SAM), Weddell Sea salinity increases, promoting density destratification and convection in the water column. Finally, we find that the reduction of surface heat fluxes during periods of full ice cover is not fully compensated by ocean heat transport into the high latitudes. This imbalance leads to a buildup of ocean heat content that supplies polynya heat loss. These results disentangle the complex, coupled climate processes that both enable the polynya’s existence and respond to it, providing insights to improve the representation of these highly episodic sea ice features in climate models.

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification

Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.

Antarctic petrels 'on the ice rocks': wintering strategy of an Antarctic seabird.

There is a paucity of information on the foraging ecology, especially individual use of sea-ice features and icebergs, over the non-breeding season in many seabird species. Using geolocators and stable isotopes, we defined the movements, distribution and diet of adult Antarctic petrels Thalassoica antarctica from the largest known breeding colony, the inland Svarthamaren, Antarctica. More specifically, we examined how sea-ice concentration and free-drifting icebergs affect the distribution of Antarctic petrels. After breeding, birds moved north to the marginal ice zone (MIZ) in the Weddell sector of the Southern Ocean, following its northward extension during freeze-up in April, and they wintered there in April–August. There, the birds stayed predominantly out of the water (60–80% of the time) suggesting they use icebergs as platforms to stand on and/or to rest. Feather δ15N values encompassed one full trophic level, indicating that birds fed on various proportions of crustaceans and fish/squid, most likely Antarctic krill Euphausia superba and the myctophid fish Electrona antarctica and/or the squid Psychroteuthis glacialis. Birds showed strong affinity for the open waters of the northern boundary of the MIZ, an important iceberg transit area, which offers roosting opportunities and rich prey fields. The strong association of Antarctic petrels with sea-ice cycle and icebergs suggests the species can serve, year-round, as a sentinel of environmental changes for this remote region.

Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections

Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.

Analysis of Intraseasonal Oscillation Characteristics of Arctic Summer Sea Ice

AbstractThis study investigates the intraseasonal variations of the Arctic sea ice concentration (SIC) based on daily data band‐passed for 20–80 days. Results show that the areas with strong intraseasonal signal of SIC move northward and then southward before and after the Arctic summer (i.e., from August to October), respectively, and the signal reaches its maximum intensity in summer. Power spectrum analyses indicate that the SIC over the region bounded by 0–180°E and 70–80°N (60–180°W and 70–80°N) in summer exhibits remarkable intraseasonal variation with a dominant period of 40–60 days (two typical periods of 40–60 and 70–80 days). These two kinds of oscillations move eastward along the Arctic marginal sea. In addition, the intensity of SIC intraseasonal oscillation has an apparent interdecadal change that is closely related to the decrease of Arctic sea ice extent.