Sea ice physical properties were determined at 21 first‐year ice (FYI) and 20 multiyear ice (MYI) stations in the Pacific sector of the Arctic during summer in 2008–2018. The bulk ice temperature was between −2.7 and −0.3 °C for FYI and between −1.7 and −0.2 °C for MYI. The bulk salinity was 0.4–3.2 practical salinity unit (psu) for FYI and 0.4–2.4 psu for MYI. A low‐salinity layer and an almost fresh layer were always present at the top of FYI and MYI, respectively. The bulk density was 600.3–900.1 kg/m³ for FYI and 686.1–903.3 kg/m³ for MYI. The upper layer density was less than the lower layer density due to higher gas content. Salinity and density were less than previously reported values of summer sea ice, and their parameterization formulae were updated. The brine and gas volume fractions were determined based on the measured ice temperature, salinity, and density. The average bulk brine and gas volume fractions were 11.3 ± 6.2% and 14.9 ± 7.9% for FYI and 9.5 ± 11.0% and 13.5 ± 12.7% for MYI. Typical brine and gas volume fraction profiles were identified and parameterized using regression analysis. The large brine and gas content of sea ice affects the sea ice melting. The energy used to melt a unit volume of summer Arctic sea ice was estimated as (2.26 ± 0.29) × 10⁵ kJ for FYI and (2.36 ± 0.24) × 10⁵ kJ for MYI. This study shows that the current physical state of the Arctic summer sea ice is different from earlier sea ice climatology, and the results will guide further research and modeling of Arctic sea ice.

A total of 25 flexural and 55 uniaxial compressive strength tests were conducted in laboratory using landfast sea ice samples collected in the Prydz Bay. Three-point bending tests were performed at ice temperatures of −12 to −3 ^∘C with force applied vertically to original ice surface, and compressive strength tests were performed at −3 ^∘C with a strain-rate level of 10⁻⁶–10⁻² s⁻¹ in the directions vertical and horizontal to ice surface. Judging from crystal structure, the ice samples were divided into congelation ice, snow ice, and a mixture of the two. The results of congelation ice showed that the flexural strength had a decreasing trend depending on porosity rather than brine volume, based on which a mathematical equation was established to estimate flexural strength. Both flexural strength and effective modulus of elasticity increased with increasing platelet spacing. The uniaxial compressive strength increased and decreased with strain rate below and above the critical regime, respectively, which is 8.0 × 10⁻⁴–1.5 × 10⁻³ s⁻¹ for vertically loaded samples and 2.0 × 10⁻³–3.0 × 10⁻³ s⁻¹ for horizontally loaded samples. A drop-off in compressive strength was shown with increasing sea ice porosity. Consequently, a model was developed to depict the combined effects of porosity and strain rate on compressive strength in both ductile and brittle regimes. The mechanical strength of mixed ice was lower than congelation ice, and that of snow ice was much weaker. To provide a safe guide for the transportation of goods on landfast sea ice in the Prydz Bay, the bearing capacity of the ice cover is estimated with the lower and upper envelopes of flexural strength and effective elastic modulus, respectively, which turned out to be a function of sea ice porosity.

It is challenging to obtain the ice phenology for a lake covered with a vast area of aquatic (shallow lake wetlands) using optical satellite data because possible clouds above the lake could contaminate the result. We developed a new method to tackle this challenge. Our target was Wuliangsu Lake, a large (330 km²) and shallow (1.6 m average depth) lake wetland in the Inner Mongolia Plateau. We used Landsat and Sentinel-2 imageries to extract the lake water boundary. The MOD09GQ/MYD09GQ dataset, having the highest spatial resolution among MODIS reflectivity products, was first selected to differentiate water and ice pixels. Then, we used the reflectivity state parameters containing cloud information in the dataset to filter out the cloud pixels. The ice phenology characteristics, such as freeze-up, break-up dates, and ice cover duration (ICD) between 2013 and 2022 were obtained. We further applied the air temperature correction technique to remove the outliers. The average of ICD in Wuliangsu Lake was about 127 ± 6 days. The freeze-up start and break-up end occurred on 17 November ± 5 days and 25 March ± 4 days, respectively. The remote sensing results agree well with the field observation, with a mean absolute error of 2 days. The algorithm can effectively remove the influence of aquatic plants and clouds on lake ice identification, thereby satisfying the needs of daily monitoring and ice phenology research in the lake wetlands.

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions—Laplace, Gauss, Gumbel, and Cauchy—were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5° indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions.

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (A_w, A_p, and A_i, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing A_p, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.

In order to satisfy the demand of key sea ice parameters, including melt pond depth H_p and underlying ice thickness H_i, in studies of Arctic sea ice change in summer, four algorithms of retrieving H_p and H_i were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of H_p. For the retrieval of H_i, Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of H_i for Lu18 agreed better with field measurements for thin ice (H_i < 1 m) than that for thick ice, but those results of H_p were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated H_p, while showing a good agreement with H_p < 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve H_p and H_i. Malimka18 also showed the ability to retrieve H_i, except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of H_p and H_i under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer.