Search Results (41)

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change. Full article

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. Full article

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands. Full article

The aim of the study is to determine the scale of differentiation and variability of ice phenomena on the lakes in the south-eastern part of the Baltic Sea catchment area. The analysis was performed based on data from the period 1961–2020 from 15 lakes located in Poland (10) and Belarus (5). The characteristics of ice phenomena were characterized, i.e., the length of their occurrence and ice cover, the thickness of ice cover and the number of breaks occurring in the ice cover in the given years were characterized. The analysis of the course of ice phenomena made it possible to distinguish three regions with an increasing length of ice phenomenon occurrence from west to east. The zones were the west of the Vistula, the east of it and the eastern part of the Belarusian Lake District. In the analyzed multi-year period, a shortening of the duration of ice phenomena and ice cover, a decrease in the maximum thickness of the ice and an increasing number of breaks in ice cover were observed. These data correlate with the upward trend in air temperature. Full article

With the goal of automatic sea ice mapping during the summer sea ice melt cycle, this study involved designing a fully automatic sea ice segmentation method based on a deep learning semantic segmentation network applicable to summer SAR images, which achieved high accuracy and the fully automatic extraction of sea ice segmentation during the summer ice melt cycle by optimizing the process, improving the pixel-level semantic segmentation network, and introducing high-resolution sea ice concentration features. Firstly, a convolution-based, high-resolution sea ice concentration calculation method is proposed and was applied to the deep learning task. Secondly, the proposed DF-UHRNet network was improved upon by designing high- and low-level fusion modules, introducing an attention mechanism, and reducing the number of convolution layers and other operations, and it can effectively fuse high- and low-scale semantic features and global contextual information based on reducing the overall number of network parameters, enabling it to achieve pixel-level classification. The results show that this method meets the needs associated with the automatic mapping and high-precision classification of thin ice, one-year ice, open water, and multi-year ice and effectively reduces the model size. Full article

Satellite altimeters have been used to monitor Arctic sea ice (ASI) thickness for several decades, but whether the different altimeter missions (such as radar and laser altimeters) are in agreement with each other and suitable for long-term research needs to be investigated. To analyze the spatiotemporal characteristics of ASI, continuous long-term first-year ice, and multi-year ice of ASI freeboard, thickness, and volume from 2002 to 2021 using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2, altimeter data are comprehensively constructed and assessed. The influences of sea surface temperature (SST) and sea surface wind field (SSW) on ASI are also discussed. The freeboard/thickness and extent/area of ASI all varied seasonally and reached their maximum and minimum in April and October, March and September, respectively. From 2002 to 2021, the freeboard, thickness, extent, and area of ASI all consistently showed downward trends, and sea ice volume decreased by 5437 km³/month. SST in the Arctic rose by 0.003 degrees C/month, and the sea ice changes lagged behind this temperature variation by one month between 2002 and 2021. The meridional winds blowing from the central Arctic region along the eastern coast of Greenland to the North Atlantic each month are consistent with changes in the freeboard and thickness of ASI. SST and SSW are two of the most critical factors driving sea ice changes. This study provides new data and technical support for monitoring ASI and exploring its response mechanisms to climate change. Full article

We reviewed various physical and biological manifestations of an unprecedented large-scale water temperature anomaly that emerged in the Northeast Pacific in late 2013. The anomaly dubbed “The Blob” persisted through 2014–2016, with some signs of its persistence through 2017–2018 and a possible reemergence in 2019. The tentative timeline of The Blob’s successive appearances around the Northeast Pacific is suggestive of its advection by currents around the Gulf of Alaska, along the Aleutians, into the Bering Sea, and eventually to the Bering Strait. During the initial phase of The Blob’s development in 2013–2014, advection along the Polar Front might have played a certain role. The extreme persistence and magnitude of The Blob resulted in numerous and sometimes dramatic ecosystem responses in the eastern Bering Sea. The multi-year duration of The Blob might have preconditioned the Bering Sea for the record low seasonal sea ice extent during the winter of 2017–2018 and the disappearance of the cold pool in 2016 and 2018 that profoundly affected zooplankton, invertebrates, fishes, seabirds, and marine mammals. A comparison of the time series of population responses across trophic levels suggests that The Blob lowered primary production during spring, increased production of small copepods and jellyfish, and reduced the efficiency of energy transfer to higher trophic levels. While the Bering Sea’s water temperature, seasonal sea ice, and cold pool seem to return to the long-term mean state in 2022, it remains to be seen if the Bering Sea ecosystem will completely recover. The two most likely alternative scenarios envision either irreversible changes or hysteresis recovery. Full article

Arctic climate change has already resulted in amplified and accelerated regional warming, or the Arctic amplification. Satellite observations have captured this climate phenomenon in its development and in sufficient spatial details. As such, these observations have been—and still are—indispensable for monitoring of the amplification in this remote and inhospitable region, which is sparsely covered with ground observations. This study synthesizes the key contributions of satellite observations into an understanding and characterization of the amplification. The study reveals that the satellites were able to capture a number of important environmental transitions in the region that both precede and follow the emergence of the apparent amplification. Among those transitions, we find a rapid decline in the multiyear sea ice and subsequent changes in the surface radiation balance. Satellites have witnessed the impact of the amplification on phytoplankton and vegetation productivity as well as on human activity and infrastructure. Satellite missions of the European Space Agency (ESA) are increasingly contributing to amplification monitoring and assessment. The ESA Climate Change Initiative has become an essential provider of long-term climatic-quality remote-sensing data products for essential climate variables. Still, such synthesis has found that additional efforts are needed to improve cross-sensor calibrations and retrieval algorithms and to reduce uncertainties. As the amplification is set to continue into the 21st century, a new generation of satellite instruments with improved revisiting time and spectral and spatial resolutions are in high demand in both research and stakeholders’ communities. Full article

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD. Full article

Landfast ice has undergone a dramatic decline in recent decades, imposing potential effects on ice travel for coastal populations, habitats for marine biota, and ice use for industries. The mapping of landfast ice deformation and the investigation of corresponding causes of changes are urgent tasks that can provide substantial data to support the maintenance of the stability of the Arctic ecosystem and the development of human activities on ice. This work aims to investigate the time-series deformation characteristics of landfast ice at multi-year scales and the corresponding influence factors. For the landfast ice deformation monitoring technique, we first combined the small baseline subset approach with ascending and descending Sentinel-1 images to obtain the line-of-sight deformations for two flight directions, and then we derived the 2D deformation fields comprising the vertical and horizontal directions for the corresponding periods by introducing a transform model. The vertical deformation results were mostly within the interval [−65, 23] cm, while the horizontal displacement was largely within the range of [−26, 78] cm. Moreover, the magnitude of deformation observed in 2019 was evidently greater than those in 2020 and 2021. In accordance with the available data, we speculate that the westerly wind and eastward-flowing ocean currents are the dominant reasons for the variation in the horizontal direction in Cambridge Bay, while the factors causing spatial differences in the vertical direction are the sea-level tilt and ice growth. For the interannual variation, the leading cause is the difference in sea-level tilt. These results can assist in predicting the future deformation of landfast ice and provide a reference for on-ice activities. Full article

A study of the climatic characteristics and annual runoff of the Volga and Severnaya Dvina rivers demonstrates that, on the East European Plain (EEP), Early Twentieth Century Warming (ETCW) manifested in a multiyear drought between 1934 and 1940; this drought has no analogues in this region in terms of intensity and duration according to Palmer’s classification, and caused extreme hydrological events. The circulation conditions during this event were characterized by an extensive anticyclone over Eastern Europe, combined with a cyclonic anomaly in the circumpolar region. An analysis of the spatial features of sea surface temperature (SST) anomalies indicate that the surface air temperature (SAT) anomalies in July on the EEP during ETCW were related not only to the North Atlantic (NA) warming and positive AMO phase, but also to a certain spatial pattern of SST anomalies characteristic of the 1920–1950 period. The difference between the SST anomalies of the opposite sign in the different NA zones, used as the indicator of the obtained spatial pattern, shows the quite close relations between the July SAT anomalies on the EEP and the atmospheric circulation patterns responsible for them. The positive phase of the Atlantic Multidecadal Oscillation (AMO) and the expansion of the subtropical high-pressure belt to the north and to the east can be considered as global-scale drivers of this phenomenon. The AMO also impacts the sea ice cover in the Barents–Kara Sea region, which, in turn, could have led to specific atmospheric circulation patterns and contributed to droughts on the EEP in the 1930s. Full article

In August 2020, during a dramatical summer retreat of sea ice in the Nansen Basin, a study of phytoplankton was conducted on the transect from two northern stations in the marginal ice zone (MIZ) (north of 83° N m and east of 38° E) through the open water to the southern station located in the Franz Victoria Trench. The presence of melted polar surface waters (mPSW), polar surface waters (PSW), and Atlantic waters (AW) were characteristic of the MIZ. There are only two water masses in open water, namely PSW and AW, at the southernmost station; the contribution of AW was minimal. In the MIZ, first-year and multiyear ice species and Atlantic species were noted; Atlantic species and first-year ice species were in open water, and only ice flora was at the southernmost station. The maximum phytoplankton biomass (30 g · m⁻³) was recorded at the northernmost station of the MIZ, and 99% of the phytoplankton consisted of a large diatom Porosira glacialis. Intensive growth of this species occurred on the subsurface halocline separating mPSW from PSW. A thermocline was formed in open water south of the MIZ towards the Franz Victoria Trench. A strong stratification decreases vertical nutrient fluxes, so phytoplankton biomass decreases significantly. Phytoplankton formed the maximum biomass in the thermocline. When moving south, biomass decreased and its minimum values were observed at the southernmost station where the influence of AW is minimal or completely absent. A transition from the silicon-limited state of phytoplankton (MIZ area) to nitrogen-limited (open water) was noted. Full article

The surface effective emissivities of Arctic sea ice are calculated using Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements. These emissivities are analyzed for stable winter conditions during the months of January–May and November and December of 2020 for several main sea ice types defined with the sea ice maps of the Arctic and Antarctic Research Institute (AARI). The sea ice emissivities are derived from the AMSR2 data using the radiation transfer model for a non-scattering atmosphere and ERA5 reanalysis data. The emissivities are analyzed only for areas of totally consolidated sea ice of definite types. Probability distribution functions are built for the emissivities and their functions for such sea ice types as nilas, young ice, thin first-year (FY) ice, medium FY ice, thick FY ice and multi-year ice. The emissivity variations with frequency are estimated for each of the considered sea ice type for all seven months. The variations are calculated both for the emissivities and for their gradients at the AMSR2 channel frequencies. Obtained emissivities turned out to be generally lower than reported previously in scientific studies, whereas the emissivity variability values proved to be much larger than was known before. For all FY ice types, at all the frequencies, an increase in the emissivity at the beginning of winter and its decrease by the end of May are observed. The emissivity gradients demonstrate noticeable decreases with sea ice age, and their values may be used in sea ice classification algorithms based on the AMSR2 data. Full article

Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, MYI and total sea ice areal flux through six Arctic gateways over the winters (October–April) of 2002–2021 were estimated using daily sea ice motion and MYI/total sea ice concentration data. Inconsistencies caused by different data sources were considered for the estimate of MYI flux. Results showed that, there is a slight declining trend in the Arctic MYI areal flux over the past two decades, which is attributable to the decrease in MYI concentration. Overall speaking, MYI flux through Fram Strait accounts for ~87% of the Arctic MYI outflow, with an average of ~325.92 × 10³ km² for the winters of 2002–2021. The monthly MYI areal flux through Fram Strait is characterized with a peak in March (~55.56 × 10³ km²) and a trough in April (~40.97 × 10³ km²), with a major contribution from MYI concentration. The connections between sea ice outflow and large-scale atmospheric circulations such as Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Dipole Anomaly (DA) were investigated. High correlation coefficients (CCs) were found in winter months such as January and February. While AO and NAO (especially NAO) exhibited generally weak correlations with the MYI/total sea ice flux, DA presented strong correlations with the areal flux, especially for MYI (CC up to 0.90 in January). However, the atmospheric circulation patterns are sometimes not fully characterized by the specific indices, which could have different effects on sea ice flux and its correlation with the atmospheric indices. Full article

Revealing the changes in the Fram Strait (FS) multiyear ice (MYI) export is crucial due to their climate relevance in the context of the loss rate of MYI being faster than that of the total ice in the Arctic. Here, we estimated winter (October–April) MYI area export through the FS over the last 2 decades by using updated MYI concentration data retrieved from active and passive microwave satellite observations. We divided the period into two regimes relative to the ice index: D1 (2002/03–2010/11) and D2 (2012/13–2019/20). The observed variations of winter MYI exports D2 were compared with those of the previous decade D1. The results show that the MYI area exports display strong interannual variability. A significant decrease in MYI export for the periods between D1 and D2 is noted. On average, the wintertime MYI area exports declined sharply by 22% from 3.82 × 10⁵ km² in D1 to 3.00 × 10⁵ km² in D2. In addition, the percentage of MYI in the total sea ice outflow through the FS (PCM) also decreased distinctly from 72% in D1 to 59% in D2. Statistics show that weekly sea ice drift across the strait can explain 76% of the MYI area export variability. Furthermore, the dominant atmospheric drivers contributing to the decline in MYI area export during D2 were examined. In the last decade (D2), the strengthened low pressure in the North Atlantic sector, combined with an eastward shift in the axis of dipole anomaly (DA), resulted in reduced MYI advection from the Beaufort Sea and Siberian Coast toward the FS. Moreover, weakened cyclonic activity south of the FS also contributed to the reduction in MYI export during D2. Full article