Sea ice conditions and navigability along four typical routes of the Northeast Passage (NEP) are analysed using remote-sensing data from 1979 to 2019. The influence of air temperature (T _air) and surface wind on the sea ice concentration (SIC) and the navigability of routes is determined. It is found that the annually averaged SICs of the different routes have decreased over the past 41 years. The fastest rate of decrease occurred in the Kara Sea (∼−1% per year), while the slowest rates of decrease occurred in the Laptev/East Siberian Sea (∼−0.42% per year). The number of navigable days for the Kara Sea has become ∼1–2 months longer than the Laptev/East Siberian Sea route as a result. The effect of T _air on SIC, quantified by ΔSIC/ΔT _air in the routes through the eastern Kara Sea and Laptev/East Siberian Sea in 2010s was ∼−0.04/°C, two to three times that seen during the 1980s. Air temperature is becoming a significant driving force of melting ice in these routes. Surface winds are also a crucial factor for the navigability of the Vilkitsky Strait and Long Strait, as they drive ice drift, and affect the navigability of the Kara Strait by introducing warm air.

The recent rapid changes in Arctic sea ice have occurred not only in ice thickness and extent, but also in the microstructure of ice. To understand the role of microstructure on partitioning of incident solar shortwave radiation within the ice and upper ocean, this study investigated the sensitivity of the optical properties of summer sea ice on ice microstructures such as the volume fraction, size, and vertical distribution of gas bubbles, brine pockets, and particulate matter (PM). The results show that gas bubbles are the predominant scatterers within sea ice. Their effects on the scattering coefficient and ice albedo are 5 and 20 times stronger respectively than the effect of brine pockets. Albedo and transmittance of ice decrease with higher concentration and larger size of PM particles. A 4-cm top layer of ice with high PM concentration (50 g/m³) results in a 10% increase in radiation absorption. The role of ice microstructure in the partitioning of radiation transfer is more important for seasonal than for multiyear ice, and more important for ponded than for snow-covered ice. Varying ice microstructure can obviously alter solar radiation transfer in the ice-ocean system, even with a constant ice thickness. Our results suggest that numerical models should take the variable microstructure of sea ice into account to improve model accuracy and to understand the interaction between internal variations in Arctic sea ice and the ocean, especially in summer.

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.

Variations in Arctic sea ice are apparent not only in its extent and thickness but also in its internal properties under global warming. The microstructure of summer Arctic sea ice changes due to varying external forces, ice age, and extended melting seasons, which affect its optical properties. Sea ice cores sampled in the Pacific sector of the Arctic obtained by the Chinese National Arctic Research Expedition (CHINARE) during the summers of 2008 to 2016 were used to estimate the variations in the microstructures and inherent optical properties (IOPs) of ice and determine the radiation budget of sea ice based on a radiative transfer model. The variations in the volume fraction of gas bubbles (V_a) of the ice top layer were not significant, and the V_a of the ice interior layer was significant. Compared with 2008, the mean V_a of interior ice in 2016 decreased by 9.1 %. Meanwhile, the volume fraction of brine pockets increased clearly during 2008–2016. The changing microstructure resulted in the scattering coefficient of the interior ice decreasing by 38.4 % from 2008 to 2016, while no clear variations can be seen in the scattering coefficient of the ice top layer. These estimated ice IOPs fell within the range of other observations. Furthermore, we found that variations in interior ice were significantly related to the interannual changes in ice ages. At the Arctic basin scale, the changing IOPs of interior ice greatly changed the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness is assumed, especially for thin ice in marginal zones, implying the presence of different sea ice bottom melt processes. These findings revealed the important role of the changing microstructure and IOPs of ice in affecting the radiation transfer of Arctic sea ice.

Lake ice phenology plays a critical role in determining the hydrological and biogeochemical dynamics of catchments and regional climates. Lakes with complex shorelines and abundant aquatic vegetation are challenging for retrieving lake ice phenology via remote sensing data, primarily because of mixed pixels containing plants, land, and ice. To address this challenge, a new double-threshold moving t-test (DMTT) algorithm, which uses Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager–Special Sensor Microwave Imager/Sounder (SSM/I–SSMIS) sensor-derived brightness temperature data at a 3.125 km resolution and long-term ERA5 data, was applied to capture the ice phenology of Lake Ulansu from 1979 to 2023. Compared with the previous moving t-test algorithm, the new DMTT algorithm employs air temperature time series to assist in determining abrupt change points and uses two distinct thresholds to calculate the freeze-up start (FUS) and break-up end (BUE) dates. This method effectively improved the detection of ice information for mixed pixels. Furthermore, we extended Lake Ulansu's ice phenology back to 1941 via a random forest (RF) model. The reconstructed ice phenology from 1941 to 2023 indicated that Lake Ulansu had average FUS and BUE dates of 15 ± 5 November and 25 ± 6 March, respectively, with an average ice cover duration (ICD) of 130 ± 8 d. Over the last 4 decades, the ICD has shortened by an average of 22 d. Air temperature was the primary impact factor, accounting for 56.5 % and 67.3 % of the variations in the FUS and BUE dates, respectively. We reconstructed, for the first time, the longest ice phenology over a large shallow lake with complex surface cover. We argue that DMTT can be effectively applied to retrieve ice phenology for other similar lakes, which has not been fully explored worldwide.

By employing high-resolution imaging and image processing techniques, a quantitative analysis was conducted on the changes in volume fraction and size of microstructures, such as brine inclusions and air bubbles, within natural saline ice at different temperatures. This study revealed the distinct stratified distribution characteristics of ice microstructure parameters along the depth direction and elucidated the differential response mechanisms of various ice layers to temperature changes. The results indicate that the sizes of brine inclusions and air bubbles decrease progressively from the surface layer to the bottom layer, with the size distribution of microstructures being most concentrated in the bottom layer. Changes in the size of microstructures in the surface ice layer are primarily dominated by solar radiation, showing strong correlations (brine inclusions: r = 0.96, p < 0.01; air bubbles: r = 0.95, p < 0.02). In contrast, the size changes of microstructures in the middle ice layer show a more significant response to ice temperature, with strong linear relationships between the sizes of brine inclusions/air bubbles and ice temperature (brine inclusions: r = 0.70, p < 0.04; air bubbles: r = 0.69, p < 0.05). The temperature of the bottom ice layer, influenced by the stable lake water temperature, remains relatively constant, and no significant correlation was observed between its microstructure size changes and ice temperature. Derived from field experiments, this study provides quantified, layer-specific mechanisms of how saline ice microstructure responds to temperature. These mechanisms offer crucial observational constraints for refining the parameterizations of ice thermodynamics and albedo feedback in cryosphere and climate system models.