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Landfast sea ice (LFSI) is sensitive to local climate change, making it an important component of the cryosphere system. In this study, the LFSI around the pan-Arctic domain was simulated from 1979 to 2021 using a well-validated snow and ice thermodynamic model (HIGHTSI) under the framework of the Fast Ice Prediction System (FIPS), forced by the ERA5 reanalysis. The simulation results agree well with the in-situ observations in the Canadian Arctic, with a mean error of −0.06 ± 0.29 m for ice thickness and −0.04 ± 0.12 m for snow depth. A decrease of −2.8 ± 0.4 cm/10a in thickness and −16.2 ± 1.5 km3/a in volume for the Arctic LFSI was modeled during this period. There was significant spatial variability among the different domains, with the fastest decline found in the Vilkitsky Strait. The modeled snow depth shows large interannual and spatial variations, which was confirmed by other modeling results. The spatiotemporal variations in both air temperature and precipitation are the driving factors for the multi-decadal variations in LFSI thickness. The decreasing air temperature during the 2010s aligned with a slower thickness decrease and a slight volume increase for LFSI, which agreed with the pan-Arctic sea ice pattern.

Based on the measurements conducted over the landfast sea ice in Prydz Bay, East Antarctica during the sea-ice growth season in 2016, various parameterization schemes in the high-resolution thermodynamic snow/ice model HIGHTSI are evaluated. The parameterization scheme of turbulent fluxes produces the largest errors compared with the parameterization schemes for other surface heat fluxes. However, the sea-ice thickness simulation is most sensitive to the differences in upward longwave radiation at the surface. In addition, the sea-ice thickness simulation during the growth season is highly sensitive to the oceanic heat flux, and a new oceanic heat flux parameterization scheme based on the bulk method is proposed. The new parameterization scheme is tested in a second year, and it significantly improves the model performance relative to the standard configuration when compared against observations. Finally, the seasonal variation in the heat budget and its influence on the sea-ice thickness variation are analyzed. The net shortwave radiation, sensible heat flux and conductive heat flux (the net longwave radiation and latent heat flux) are found to be the surface heat sources (heat sinks) during the growth season. The larger conductive heat flux and the smaller oceanic heat flux can intensify the growth of sea ice.

Liaodong Bay is one of the lowest latitude areas with seasonal sea ice cover in the Northern Hemisphere. Sea ice forecasting faces challenges in accuracy due to its low thickness. Therefore, a novel parameterization scheme for oceanic heat flux was developed to forecast sea ice thickness accurately. Application of the parameterization scheme for oceanic heat flux in the HIGHTSI model significantly improved the forecasting accuracy of sea ice thickness before the severe ice period. During this period, the RMSE of sea ice thickness measured on the JZ9–3 and the JZ20–2 oil platforms decreased by 0.53 cm and 2.90 cm compared to previous schemes, respectively. In order to improve the forecasting accuracy during the severe and melting ice periods, the observed and retrieved sea ice thickness was nudged into the model. The simulated results demonstrated a good agreement with monitored sea ice thickness distribution. During the entire season with sea ice cover, the R-squared values between simulated and retrieved sea ice thickness in the core area of Liaodong Bay reached 0.76. Furthermore, this study revealed a relatively strong correlation between air temperature and ice temperature on the following day. The proposed scheme provides a valuable approach to improve the forecasting accuracy for the areas with low thickness in the sea ice numerical models.

In this paper, snow and ice thickness products derived from an updated Fast Ice Prediction System Version 2.0 (FIPS V2.0) in Prydz Bay, East Antarctica, are introduced and compared with in-situ observations. FIPS V2.0 is comprised of a newly-developed snowdrift parameterization compared to the original FIPS V1.0. The simulation domain covers the entire fast ice region in Prydz Bay and is configured to 720 grid cells, with a spatial resolution of 0.125°. The ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasting (ECMWF) were used as the atmospheric forcing. The in-situ observations were obtained near Zhongshan Station by the wintering team, and the measurement frequency of the snow and ice thicknesses was around one week. Both the FIPS V2.0 products and in-situ observations introduced in this paper cover the time periods from 2012 to 2016. The primary assessments based on the in-situ observations show that FIPS V2.0 has mean biases of 0.01 ± 0.07 m and 0.23 ± 0.09 m for snow and ice thickness simulations, respectively. The results indicate that the updated FIPS V2.0 produces a reasonable snow thickness due to the newly-developed snowdrift parameterization, but it overestimates the ice thickness due to the cold bias in the air temperature forcing. These 2-D snow and ice thickness distributions provide important references for sea ice thermodynamic studies, remote sensing validations, and icebreaker navigation assessments in this region. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00066.

Convolutional neural networks (CNNs) have achieved great progress in the classification of surface objects with hyperspectral data, but due to the limitations of convolutional operations, CNNs cannot effectively interact with contextual information. Transformer succeeds in solving this problem, and thus has been widely used to classify hyperspectral surface objects in recent years. However, the huge computational load of Transformer poses a challenge in hyperspectral semantic segmentation tasks. In addition, the use of single Transformer discards the local correlation, making it ineffective for remote sensing tasks with small datasets. Therefore, we propose a new Transformer layered architecture that combines Transformer with CNN, adopts a feature dimensionality reduction module and a Transformer-style CNN module to extract shallow features and construct texture constraints, and employs the original Transformer Encoder to extract deep features. Furthermore, we also designed a simple Decoder to process shallow spatial detail information and deep semantic features separately. Experimental results based on three publicly available hyperspectral datasets show that our proposed method has significant advantages compared with other traditional CNN, Transformer-type models.

A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

The surface energy budget over the Antarctic sea ice from 8 April 2016 through 26 November 2016 are presented. From April to October, Sensible heat flux (SH) and subsurface conductive heat flux (G) were the heat source of surface while latent heat flux (LE) and net radiation flux (Rn) were the heat sink of surface. Our results showed larger downward SH (due to the warmer air in our site) and upward LE (due to the drier air and higher wind speed in our site) compared with SHEBA data. However, the values of SH in N-ICE2015 campaign, which located at a zone with stronger winds and more advection of heat in the Arctic, were comparable to our results under clear skies. The values of aerodynamic roughness length (z0m) and scalar roughness length for temperature (z0h), being 1.9 × 10−3 m and 3.7 × 10−5 m, were suggested in this study. It is found that snow melting might increase z0m. Our results also indicate that the value of log(z0h/z0m) was related to the stability of stratification. In addition, several representative parameterization schemes for z0h have been tested and a couple of schemes were found to make a better performance.

Surface radiative fluxes over landfast sea ice off Zhongshan station have been measured in austral spring for five springs between 2010 and 2015. Downward and upward solar radiation vary diurnally with maximum amplitudes of 473 and 290 W m−2, respectively. The maximum and minimum long-wave radiation values of the mean diurnal cycle are 218 and 210 W m−2 for downward radiation, 277 and 259 W m−2 for upward radiation and 125 and −52 W m−2 for net radiation. The albedo has a U-shaped mean diurnal cycle with a minimum of 0.64 at noon. Sea ice thickness is in the growth phase for most spring days, but can be disturbed by synoptic processes. The surface temperature largely determines the occurrence of ice melting. Surface downward and upward long-wave radiation show synoptic oscillations with a 5–8 day period and intraseasonal variability with a 12–45 day period. The amplitudes of the diurnal, synoptic and intraseasonal variability show some differences during the five austral springs considered here. The intraseasonal and synoptic variability of downward and upward long-wave radiation are associated with the variability of cloud cover and surface temperature induced by the atmospheric circulation.

In this study, sea ice thickness (SIT) and sea ice extent (SIE) in the Bohai Sea from 2000 to 2016 were investigated. A surface heat balance equation was applied to calculate SIT using ice surface temperatures estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data with input from air temperature and wind speed from reanalyzing weather data. No trend was found in SIT during 2000–2016. The mean SIT and SIE during this period were 5.58±0.86 cm and 23×103±8×103 km2, respectively. The largest SIT and SIE periods were observed during the second half of January and the first half of February, respectively. The Spearman correlation coefficient between mean ice thickness and average air temperature from 21 automatic weather stations around the Bohai Sea was –0.94 (P < .005), and the coefficient between median ice extent and negative accumulated temperature was –0.503 (P < .001). The rate of increase in air temperature around the Bohai Sea is 0.271°C per decade in winter for 1979–2016 (P < .05), which is much lower than that in northern polar area (0.648°C per decade). This rate has not resulted in a decreasing trend in SIT and SIE for the past 16 years in the Bohai Sea.

An ice mass balance buoy (IMB) monitors the evolution of snow and ice cover on seas, ice caps and lakes through the measurement of various variables. The crucial measurement of snow and ice thickness has been achieved using acoustic sounders in early devices but a more recently developed IMB called the Snow and Ice Mass Balance Array (SIMBA) measures vertical temperature profiles through the air-snow-ice-water column using a thermistor string. The determination of snow depth and ice thickness from SIMBA temperature profiles is presently a manual process. We present an automated algorithm to perform this task. The algorithm is based on heat flux continuation, limit ratio between thermal heat conductivity of snow and ice, and minimum resolution (±0.0625°C) of the temperature sensors. The algorithm results are compared with manual analyses, in situ borehole measurements and numerical model simulation. The bias and root mean square error between algorithm and other methods ranged from 1 to 9 cm for ice thickness counting 2%–7% of the mean observed values. The algorithm works well in cold condition but becomes less reliable in warmer conditions where the vertical temperature gradient is reduced.

Summertime internal melting of Antarctic sea ice is common due to the penetration of solar radiation below the snow and ice surface. We focus on the role of internal melting and heat conduction in generating gap layers within the ice. These often occur approximately 0.1 m below the ice surface. In a small-scale survey over land-fast sea ice in Prydz Bay, East Antarctica, we observed, for the first time, gap layers 0.6–1.0 m below the surface for both first-year ice and multi-year ice. A 1D snow/ice thermodynamic model successfully simulated snow and ice mass balance and the evolution of the gap layers. Their spatial distribution was largely controlled by snow thickness and ice thickness. A C-shaped ice temperature profile with the lowest values in the middle of the ice layer resulted in heat flux convergence causing downward progression of the internal melt layer. Multidecadal (1979–2019) seasonal simulations showed decreasing air temperature favored a postposed internal melting onset, reduced total internal melt, and delayed potential ice breakup, which indicated a higher chance for local coastal ice to be shifted from first-year ice to multi-year ice.

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

The seasonal cycle of fast ice thickness in Prydz Bay, East Antarctica, was observed between March and December 2012. In March, we observed a 0.16 m thickness gain of 0.22 m-thick first-year ice (FYI), while 1.16 m-thick second-year ice (SYI) nearby simultaneously ablated by 0.59 m. A 1-D thermodynamic sea-ice model was applied to identify the factors that led to the simultaneous growth of FYI and melt of SYI. The different evolutions were explained by the difference in the conductive heat flux between the FYI and SYI. As the FYI was thin, there was a large temperature gradient between the ice base and the colder ice surface. This generated an upward conductive heat flux, which was larger than the heat flux from the ocean to the ice base, yielding basal growth of ice. In the case of the thicker SYI the temperature gradient and, hence, the conductive heat flux were smaller, and not sufficient to balance the oceanic heat flux at the ice base, yielding basal ablation. Penetration of solar radiation affected the conductive heat flux in both cases, and the model results were sensitive to the initial ice temperature profile and the uncertainty of the oceanic heat flux.

In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010–2020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends.

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January−June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976−2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 104 km2/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 104 km2/yr (5.2% per decade). The annual maximum extent reached 2.3 × 106 km2 in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 106 km2, which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone.

Tidal cracks are linear features that appear parallel to coastlines in fast ice regions due to the actions of periodic and non-periodic sea level oscillations. They can influence energy and heat exchange between the ocean, ice, and atmosphere, as well as human activities. In this paper, the LINE module of Geomatics 2015 software was used to automatically extract tidal cracks in fast ice regions near the Chinese Zhongshan Station in East Antarctica from Landsat-8 Operational Land Imager (OLI) data with resolutions of 15 m (panchromatic band 8) and 30 m (multispectral bands 1–7). The detected tidal cracks were determined based on matching between the output from the LINE module and manually-interpreted tidal cracks in OLI images. The ratio of the length of detected tidal cracks to the total length of interpreted cracks was used to evaluate the automated detection method. Results show that the vertical direction gradient is a better input to the LINE module than the top-of-atmosphere (TOA) reflectance input for estimating the presence of cracks, regardless of the examined resolution. Data with a resolution of 15 m also gives better results in crack detection than data with a resolution of 30 m. The statistics also show that, in the results from the 15-m-resolution data, the ratios in Band 8 performed best with values of the above-mentioned ratio of 50.92 and 31.38 percent using the vertical gradient and the TOA reflectance methods, respectively. On the other hand, in the results from the 30-m-resolution data, the ratios in Band 5 performed best with ratios of 47.43 and 17.8 percent using the same methods, respectively. This implies that Band 8 was better for tidal crack detection than the multispectral fusion data (Bands 1–7), and Band 5 with a resolution of 30 m was best among the multispectral data. The semi-automatic mapping of tidal cracks will improve the safety of vehicles travel in fast ice regimes.