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Freezing temperature parameterization significantly impacts the heat balance at sea‐ice bottom and, consequently, the simulated sea‐ice thickness. Here, the single‐column model ICEPACK was used to investigate the impact of the freezing temperature parameterization on the simulated sea‐ice thermodynamic growth during the MOSAiC expedition from October 2019 to September 2020. It is shown that large model errors exist with the standard parameterization and that different formulations for calculating the freezing temperature impact the simulated sea‐ice thickness significantly. Considering the winter mixed layer temperature, a modified parameterization of the freezing point temperature based on Mushy scheme was developed. The mean absolute error (ratio) of simulating sea‐ice thickness for all buoys reduces from 7.4 cm (4.9%) with the “Millero” scheme, which performs the best among the existing schemes in the ICEPACK model, to 4.2 cm (2.9%) with the new developed scheme. Plain Language Summary The heat transferred from the ocean to the sea‐ice influences the growth and melting of the sea‐ice. Freezing temperature is an essential parameter for calculating the heat transfer. Nevertheless, few studies have attempted to evaluate the impact of different freezing temperature parameterizations on the simulated sea‐ice thermodynamic growth. This study uses observed atmosphere and ocean data to force a single‐column model. Using different methods to calculate the freezing temperature significantly impacts the simulated sea‐ice thickness. After a series of testing and comparisons, we have developed a modified parameterization of freezing temperature that significantly reduces the simulation deviation from the observations. Key Points Different parameterizations of the freezing temperature significantly influence the simulated sea‐ice thickness A modified‐Mushy parameterization method is developed for the freezing temperature, significantly improving ice thickness simulation

The control of freezing temperatures throughout the artificial ground freezing (AGF) process is always difficult. An overly high temperature of the circulating refrigerant may lead to insufficient frozen soil strength, while an overly low temperature may cause unnecessary energy waste, and even excessive pore ice may damage the soil structure and reduce the frozen soil strength. What's more, overly freezing may damage buildings on the surface. Therefore, it is of great significance to study the optimum freezing temperature (OFT), which is very important for better and more energy-efficient employment of the AGF method. In this paper, we use uniaxial compression and direct shear tests to obtain dynamic mechanical parameters in the soil freezing process. After the analysis of varying mechanical parameters by the entropy weight TOPSIS principal component analysis method, the results show that the interval range of OFT for saturated and unsaturated sandy gravel is [− 10 °C, − 15 °C] and [− 15 °C, − 20 °C], respectively. The findings indicate that, in the AGF method, a lower temperature is not always preferable. According to the results, constructive measures to optimize the temperature field distribution in the AGF method are proposed. The research results will contribute to the assessment of the safety and efficiency of AGF projects.

The temperature for cloud glaciation importantly determines the initialization of precipitation and lifetime of clouds. The role of anthropogenic pollutants as ice nucleating particles (INPs) to determine the cloud glaciation remains uncertain. In this study, based on satellite radar and lidar observations, the clouds either in pure liquid or mixed‐phase with liquid top were statistically analyzed over China during 2006–2019, to obtain the transition freezing temperature (T*) of cloud top where mixed‐phase becomes more frequent than pure water, with further validation by the aircraft in situ measurements. Anthropogenic pollution was observed to raise T* up to −9°C, significantly increasing it by approximately 5°C per unit of aerosol optical depth. The results provide regional‐scale evidence that anthropogenic pollutants act as efficient INPs, increasing the freezing temperature of mixed‐phase clouds. Plain Language Summary Cloud phase transition (from liquid water to ice) is of great importance to Earth's energy and water cycle. Forming ice crystals in mixed‐phase clouds requires ice nucleating particles (INPs). However, whether the anthropogenic pollution can be efficient INPs remains unclear. In this study, based on satellite and aircraft observations in China, the temperature when clouds start to freeze is estimated by analyzing the frequency of liquid water and glaciated cloud tops, under which the occurrence of glaciated cloud tops started to overtake that of pure liquid water cloud tops. We found that the clouds over the anthropogenically polluted region over China presented higher freezing temperature than other cleaner regions. The results suggest anthropogenic pollution can efficiently control cloud glaciation in regional scale by serving as INPs. Key Points Transition freezing temperature is estimated by analyzing the frequency of mixed‐phase and liquid clouds over China using satellite data Anthropogenic pollution can cause freezing temperature of clouds up to −9°C Anthropogenic pollutants can be efficient ice nuclei and increase the freezing temperature of mixed‐phase clouds

The mechanical response and failure behavior of high-pressure frozen ice are essential to the technological progress in drilling thick polar ice sheets, but current research primarily focuses on non-pressure-frozen ice. In this paper, ice specimens with a cylindrical geometry were fabricated at −20 °C, applying freezing pressures across a range of 10 to 40 MPa with a 10 MPa interval. Their mechanical properties were investigated through triaxial compression tests under axial loading conditions and were compared with the results obtained at −10 °C. The results indicate that, with increasing freezing pressure, the samples transitioned from a failure state of interlaced cracking to a highly transparent state. The failure behavior observed in the specimens was characterized as ductile, as evidenced by the deviatoric stress–axial strain relationships. Moreover, the peak deviatoric stress exhibited a non-monotonic dependence on freezing pressure, with an initial rise from 9.59 MPa at 10 MPa to a peak of 14.37 MPa at 30 MPa and a subsequent decline to 10.12 MPa at 40 MPa. All specimens reached a relatively stable residual state at 5% axial strain, with residual deviatoric stresses ranging from 4.13 to 5.71 MPa. A reduction in freezing temperature from −10 °C to −20 °C can effectively enhance both the peak deviatoric stress and the residual stress of high-pressure frozen ice under triaxial shear conditions. All peak tangent modulus values, ranging from 1.61 to 2.93 GPa with an average of 2.2 GPa, were observed within 0.7% axial strain and exhibited mild fluctuations with increasing freezing pressure. These findings provide a more robust mechanical foundation for drilling research and operations in extremely thick polar ice caps.

This study derived contact angles for fifteen types of pollens, nine types of fungi, ten types of bacteria, one type of diatom, and twelve types of mineral dust for use in the parameterization of immersion freezing based on the classical nucleation theory (CNT). Our approach is to interpret freezing temperature measurement results with the stochastic nucleation concept. In this way, the abundant freezing temperature data available in the literature can be converted to contact angles that needed in the CNT parameterization for a variety of INPs. The derived contact angles compared well with values independently obtained in earlier studies based on a pure-CNT approach using laboratory nucleation rate data. The uncertainties in contact angle calculation associated with the definition of onset nucleation rate, the activation energy, and the ice-nuclei size are estimated to be about ± 1–2°, ± 1–5°, and ± 1–2°, respectively, among different ice-nucleating particles.

This work studies and evaluates the mechanical properties of frozen soil, FS, from Yulong mine in Tibet, under different freezing temperatures ranging from −12 °C to −1 °C, using experimental tests. In addition, the effects of temperature and time on the freezing depth of FS were investigated. Based on the cantilever beam theory, the mechanical model of FS breaking under blasting stress wave was established, and the theoretical formula for the fracture length (FL) of FS was deduced. Moreover, the major influences on FL were analyzed. The results showed that the frozen depth curves at different temperatures were approximately linear in single or double segments, and the lower the temperature, the greater the early freezing rate. Additionally, the uniaxial compression strength, elastic modulus, and tensile strength of FS were found to decrease with the increase in temperature. The findings highlight that the shear strength, cohesion, and internal friction angle decreased first and then increased with the increase in temperature. It was shown that the thickness, friction coefficient, weight density, and tensile strength of FS are positively correlated with the FL, while there is an inverse proportionality with respect to the pressure of the detonation wave.

Freezing temperature is an important physical index of saline soil in permafrost and seasonal frozen area, and it is difficult to be predicted with a formula when saline soil contains multiple salts. In this study, we used a backpropagation neural network (BPNN) and a radial basis function neural network (RBFNN) to predict the freezing temperature of saline soil from the Qinghai–Tibet Plateau and Lanzhou. Several variables (ion content, soluble salt content, and water content) were adopted based on previous studies and experimental conditions. After the above two neural network models were established, the parameters were input into the two models to obtain the predicted values of the freezing temperature. Then, the measured and predicted values were compared to evaluate the accuracy of the two neural network models. Additionally, three statistical indicators were used to quantify the reliability of the two neural networks. Our results showed that BPNN had a stronger ability to predict freezing temperatures. Moreover, the established BPNN model was applied to analyze the sensitivity of the freezing temperature to the content of different ions under two different water content conditions. Finally, it was concluded that the influence of main ions on the freezing temperature in descending order was Cl− > K+ ≈ Na+ > SO42− > CO32− > Ca2+ under the condition of 10% water content, and K+ >Cl− > SO42− > Na+ > CO32− > Ca2+ when the water content was 30%. This study offers a new prediction method for the freezing temperature of multicomponent saline soil and can be used as a reference to investigate the factors affecting freezing temperatures.

The climate of the Qinghai–Tibet Plateau is distinct. Given the large temperature difference between day and night, drought in perennial years, low rainfall and large evaporation volume, the frozen soil in some areas of the Qinghai–Tibet Plateau will occur in soil salt. The presence of salt in frozen soil salt changes the water thermal characteristics of the frozen soil, which will affect the changes in its activity layer. In this paper, the Beiluhe area of the Qinghai–Tibet Plateau was selected as the research object, and the numerical calculation model of water, heat and salt of salinised frozen soil was established. Considering the influence of salt crystallisation and salt on the freezing temperature of the active layer, the effects of different salt concentrations, water contents and salt type on the temperature of frozen soil and the thickness of the active layer were compared and analysed. Therefore, the salt of soil degenerates frozen soil under the action of sodium chloride and sodium sulphate, and the presence of sodium chloride and sodium sulphate is not conducive to the stability of frozen soil for many years. During soil salinisation, the content of sodium chloride in frozen soil increases; the temperature of permafrost initially decreases and then increases; the initial freezing time of the active layer is postponed in the freezing and cooling stages, the time when the water in the active layer with a salt concentration of 0.2–0.8% was delayed by 21, 32, 54 and 65 days; the temperature of the active layer decreases, which is the opposite in the thawing and heating stages, and the thickness of the active layer increases with the increase in salt concentration. During soil salinisation, the content of sodium sulphate in frozen soil increases; the freezing temperature of the active layer initially decreases and then increases and finally decreases, which is contrary to the temperature of the active layer in the warm season. The thickness of the active layer initially increases (with a maximum increase in 0.82 m) and then decreases and finally increases with the increase in salt concentration. The content of sodium sulphate in frozen soil has little effect on the initial freezing time of the active layer. High water content is conducive to the stability of permafrost. When the content of sodium chloride in frozen soil is constant, the water content increases; the temperature change of frozen soil is smaller; the temperature of the active layer in the warm season is lower; the thickness of the active layer is smaller, and the frozen soil tends to be more stable. When the content of sodium sulphate is constant, the increase in water content generally reduces the warm-season temperature of the active layer and the thickness of the active layer (−6 m the temperature of 30% and 40% water content in −6 m is 0.17 °C and 0.24 °C lower than that of 20% water content). However, analysis of the thickness of the active layer of the frozen soil containing sodium sulphate must combine the influence of water content and freezing temperature.

This research investigates the effects of freezing temperature and salinity on the adhesion shear strength of amphibious aircraft tires under static icing. It found that the lower the freezing temperature, the greater the ice adhesion shear strength, and the higher the salinity of the water sample, the lower the ice adhesion shear strength. This is related to the thickness of the brine layer at the ice-tire substrate interface; the temperature decreased, the brine layer became thinner, and, accordingly, the adhesion shear strength increased. This paper analyzes the problem of brine precipitation during seawater freezing and its influencing mechanism on the ice adhesion shear strength. In addition, it also found that the lower the temperature, the greater the growth rate of the ice adhesion shear strength. When the salinity in different ranges changed, its influence on the adhesion shear strength was different. When the salinity of the water sample is close to 0%, a small change in the salinity will cause a large change in the ice adhesion shear strength. When the salinity is large, the change of salinity has a weaker influence on the ice adhesion shear strength. This research provides a strong reference for the design and study of amphibious aircraft tires.

Achieving detailed neuronal structural information in large‐volume brain tissue has been a longstanding challenge in human brain imaging. A key obstacle arises from the trade‐off between staining efficiency and tissue autolysis. Traditional Golgi staining, typically conducted at room temperature or 37 °C to optimize staining efficiency, leads to rapid autolysis of brain tissue, resulting in the loss of fine structural details. Here, a near‐freezing temperature (NFT) staining strategy in post‐mortem frozen (PMF) human brain samples are presented, using a mercury chloride‐based method under ice‐water bath conditions. In contrast to the 37 °C Golgi staining, this NFT‐based method significantly reduces tissue autolysis, preserving fine neuronal structures. Notably, neuronal counts in the same field of view increased by 5.5‐fold, and dendritic spine density increases by 22‐fold. Using this approach, uniform staining of millimeter‐thick is achieved, centimeter‐scale human brain slices and integrated it with synchrotron‐based X‐ray microscopy to perform micrometer resolution 3D reconstructions of the cerebellum and frontal lobe. This novel technique offers a powerful tool for the fine‐structural imaging of large‐volume brain tissue, providing new insights into the intricate organization of neural networks. A near‐freezing temperature staining strategy, combined with a mercury chloride‐based method, effectively preserves delicate neuronal structures in post‐mortem frozen human brain tissues by minimizing autolysis. This approach enables uniform labeling across centimeter‐scale slices and, when integrated with synchrotron‐based X‐ray microscopy, achieves micrometer‐resolution 3D reconstructions, paving the way for large‐scale human brain mapping.