An improved least mean square/fourth direct adaptive equalizer (LMS/F-DAE) is proposed in this paper for underwater acoustic communication in the Arctic. It is able to process complex-valued baseband signals and has better equalization performance than LMS. Considering the sparsity feature of equalizer tap coefficients, an adaptive norm (AN) is incorporated into the cost function which is utilized as a sparse regularization. The norm constraint changes adaptively according to the amplitude of each coefficient. For small-scale coefficients, the sparse constraint exists to accelerate the convergence speed. For large-scale coefficients, it disappears to ensure smaller equalization error. The performance of the proposed AN-LMS/F-DAE is verified by the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that compared with the standard LMS/F-DAE, AN-LMS/F-DAE can promote the sparse level of the equalizer and achieve better performance.

A method of measuring the tritium in seawater based on electrolytic enrichment and ultra-low background liquid scintillation counting techniques was established. The different factors influencing the detection limit were studied, including the counting time, the electrolytic volume of the seawater samples, the selection of background water, scintillation solution and their ratio. After optimizing the parameters and electrolyzing 350 mL volume of samples, the detection limit of the method was as low as 0.10 Bq/L. In order to test the optimization of system for this method, of the 84 seawater samples collected from the Arctic Ocean we measured, 92% were above the detection limit (the activity of this samples ranged from 0.10 Bq/L to 1.44 Bq/L with an average of (0.30±0.24) Bq/L). In future research, if we need to accurately measure the tritium activity in samples, the volume of the electrolytic samples will be increased to further reduce the minimum detectable activity.

During the 3rd Chinese National Arctic Research Expedition cruise in the summer of 2008, nutrients (${\rm{NO}}_3^ - $, ${\rm{NO}}_2^ - $, ${\rm{SiO}}_3^ {2-} $, and ${\rm{PO}}_4^ {3-} $) and dissolved oxygen were measured in the western Arctic Ocean, to derive the vertical distribution of nutrient tracers and its relationship to water structure and biogeochemical processes. The nutrient data show that surface waters had the lowest ${\rm{NO}}_3^ - $/${\rm{PO}}_4^ {3-} $ (mean of 0.5) and ${\rm{SiO}}_3^ {2-} $/${\rm{PO}}_3^ {-} $ (mean of 2.8) values in the water column, suggesting an excess of phosphate. Winter Bering Shelf water (wBSW) had high Si* (16.7 μmol/L; Si*=[Si(OH)₄]–[${\rm{NO}}_3^ - $]) with negative N* (−11.7 μmol/L; N*=[${\rm{PO}}_4^ {3-} $]−16[${\rm{PO}}_4^ {3-} $]+3.5 μmol/L) in the water column, indicating nitrate deficiency. The warm Atlantic layer had positive N* (0.8 μmol/L) and negative Si* (−5.4 μmol/L) compared with Pacific source water. The vertical distribution of nutrients indicates that wBSW can be characterized by N* minimum and Si* maximum. In contrast, minima of Si* and ${\rm{SiO}}_3^ {2-} $/${\rm{PO}}_4^ {3-} $ below 200 m indicate the distribution of Atlantic warm water.

The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information. However, traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone. A successive corrections analysis using variational optimization method, called spatial multi-scale recursive filter (SMRF), has been designed in this paper to extract multi-scale information resolved by sea ice observations. It is a combination of successive correction methods (SCM) and minimization algorithms, in which various observational scales, from longer to shorter wavelengths, can be extracted successively. As a variational objective analysis scheme, it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time, and also, the specification of parameters is more convenient. Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals. Further, it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration (SIC) experiment with the real observations from Special Sensor Microwave/Imager SIC (SSMI).

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from −3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.

Under-ice ambient noise in the Arctic Ocean is studied using the data recorded by autonomous hydrophones at the long-term ice station during the 9th Chinese National Arctic Research Expedition. Time-frequency analysis of two 7-s-long ice-induced noise samples shows that both ice collision and ice breaking noise have many outliers in the time-domain (impulsive characteristic) and abundant frequency components in the frequency-domain. Ice collision noise lasts for several seconds while the duration of ice breaking noise is much shorter (i.e., less than tens of milliseconds). Gaussian distribution and symmetric alpha stable (sαs) distribution are used in this paper to fit the impulsive under-ice noise. The sαs distribution can achieve better performance as it can track the heavy tails of impulsive noise while Gaussian distribution fails. This paper also analyzes the meteorological variables during the under-ice noise observation experiment and deduces that the impulsive ambient noise was caused by the combined force of high wind speed and increasing atmosphere temperature on the ice canopy. The Pearson correlation coefficients between long-term power spectral density variations of under-ice ambient noise and meteorological variables are also studied in this paper.

The extremely low temperature, high humidity and limited power supply pose considerable challenges when using spectrometers within the Arctic sea ice. The feasibility of using a miniature low-power near-infrared spectrometer module to measure solar radiation in Arctic sea ice environments was investigated in this study. Temperature and integration time dependences of the spectrometer module were examined over the entire target operating range of –50°C to 30°C, well below the specified operating range of this spectrometer. Using these observations, a dark output prediction model was developed to represent dark output as a function of temperature and integration time. Temperature-induced biases in the saturation output and linear operating range of the spectrometer were also determined. Temperature and integration time dependences of the signal output were evaluated. Two signal output correction models were developed and compared, to convert the signal output at any temperature within the operating temperature range and integration time to that measured at the reference temperature and integration time. The overall performance of the spectrometer was evaluated by integrating it into a refined fiber optic spectrometry system and measuring solar irradiance distribution in the ice cover with thickness of 1.85 m in the Arctic during the 9th Chinese National Arctic Research Expedition. The general shape of the measured solar irradiance above the snow surface agreed well with that measured by other commercial oceanographic spectroradiometers. The measured optical properties of the sea ice were generally comparable to those of similar ice measured using other instruments. This approach provides a general framework for assessing the feasibility of using spectrometers for applications in cold environments.

The reduction in Arctic sea ice in summer has been reported to have a significant impact on the global climate. In this study, Arctic sea ice/snow at the end of the melting season in 2018 was investigated during CHINARE-2018, in terms of its temperature, salinity, density and textural structure, the snow density, water content and albedo, as well as morphology and albedo of the refreezing melt pond. The interior melting of sea ice caused a strong stratification of temperature, salinity and density. The temperature of sea ice ranged from –0.8°C to 0°C, and exhibited linear cooling with depth. The average salinity and density of sea ice were approximately 1.3 psu and 825 kg/m³, respectively, and increased slightly with depth. The first-year sea ice was dominated by columnar grained ice. Snow cover over all the investigated floes was in the melt phase, and the average water content and density were 0.74% and 241 kg/m³, respectively. The thickness of the thin ice lid ranged from 2.2 cm to 7.0 cm, and the depth of the pond ranged from 1.8 cm to 26.8 cm. The integrated albedo of the refreezing melt pond was in the range of 0.28–0.57. Because of the thin ice lid, the albedo of the melt pond improved to twice as high as that of the mature melt pond. These results provide a reference for the current state of Arctic sea ice and the mechanism of its reduction.

The spatial structure of the Arctic sea ice concentration (SIC) variability and the connection to atmospheric as well as radiative forcing during winter and summer for the 1979–2017 period are investigated. The interannual variability with different spatial characteristics of SIC in summer and winter is extracted using the empirical orthogonal function (EOF) analysis. The present study confirms that the atmospheric circulation has a strong influence on the SIC through both dynamic and thermodynamic processes, as the heat flux anomalies in summer are radiatively forced while those in winter contain both radiative and “circulation-induced” components. Thus, atmospheric fluctuations have an explicit and extensive influence to the SIC through complex mechanisms during both seasons. Moreover, analysis of a variety of atmospheric variables indicates that the primary mechanism about specific regional SIC patterns in Arctic marginal seas are different with special characteristics.