Analysis of under-ice ambient noise characteristics of Gakkel Ridge in the Arctic.

This paper presents an analysis of the under-ice acoustic data and environmental parameters measured over a three-month period from August 31 to November 28, 2021, within the area of the Gakkel Ridge in the Arctic. After "spikes" caused by micro-level events are removed, the distribution of the retained under-ice noise related to macro-level events can be described satisfactorily by a Gaussian distribution, as verified by Q-Q plots and kurtosis/skewness analysis. We use sliding window analysis to deal with the features of under-ice ambient noise and model the data by Gaussian interpolation. This shows that the ambient noise level over the low-frequency range (10-100 Hz) is comparatively flat at about 60 dB; with the frequency increases from 100 to 2560 Hz, the ANL decreased to about 40 dB. We then introduce canonical correlation analysis (CCA) to analyze the potential relation between environmental forcing and the under-ice noise level. The results of CCA indicate that the seawater parameters (including temperature, salinity, and sound velocity) close to the ice-water interface have the greatest influence on the under-ice noise level among all environmental parameters recorded in the air, sea-ice, and seawater. Additionally, the under-ice noise level forced by the environment does not exhibit any particular frequency dependence.

A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica.

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station's sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

Space Physical Sensor Protection and Control System Based on Neural Network Prediction: Application in Princess Elizabeth Area of Antarctica.

In the inland areas of Antarctica, the establishment of an unmanned automatic observation support system is an urgent problem and challenge. This article introduces the development and application of an unmanned control system suitable for inland Antarctica. The system is called RIOD (Remote Control, Image Acquisition, Operation Maintenance, and Document Management System) for short. At the beginning of this research project, a mathematical model of heat conduction in the surface observation chamber was established, and the control strategy was determined through mathematical relationships and field experiments. Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. Moreover, the future temperature is substituted into the mathematical model of thermal conductivity to obtain the input value of the next input power, to formulate the operation strategy for the system. This method maintains the regular operation of the sensor while reducing energy consumption. The RIOD system has been deployed in the Tai-Shan camp in China's Antarctic inland inspection route. The application results 4.5 months after deployment show that the RIOD system can maintain stable operation at lower temperatures. This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas.

Design and Performance Analysis of a Multilayer Sea Ice Temperature Sensor Used in Polar Region.

Temperature profiles of sea ice have been recorded more than a few decades. However, few high-precision temperature sensors can complete the observation of temperature profile of sea ice, especially in extreme environments. At present, the most widely used sea ice observation instruments can reach an accuracy of sea ice temperature measurement of 0.1 °C. In this study, a multilayer sea ice temperature sensor is developed with temperature measurement accuracy from -0.0047 °C to 0.0059 °C. The sensor system composition, structure of the thermistor string, and work mode are analyzed. The performance of the sensor system is evaluated from -50 °C to 30 °C. The temperature dependence of the constant current source, the amplification circuit, and the analog-to-digital converter (ADC) circuit are comprehensive tested and quantified. A temperature correction algorithm is designed to correct any deviation in the sensor system. A sea-ice thickness discrimination algorithm is proposed in charge of determining the thickness of sea ice automatically. The sensor system was field tested in Wuliangsuhai, Yellow River on 31 January 2018 and the second reservoir of Fen River, Yellow River on 30 January 2018. The integral practicality of this sensor system is identified and examined. The multilayer sea ice temperature sensor will provide good temperature results of sea ice and maintain stable performance in the low ambient temperature.

Discrimination Algorithm and Procedure of Snow Depth and Sea Ice Thickness Determination Using Measurements of the Vertical Ice Temperature Profile by the Ice-tethered Buoys.

Snow depth and sea ice thickness in the Polar Regions are significant indicators of climate change and have been measured over several decades by ice-tethered buoys. However, sea ice temperature profiles measured by ice-tethered buoys are rarely used to infer snow depth and sea ice thickness owing to the lack of automatic discrimination algorithms, restricting the use of the data for sea ice thermodynamics studies. In this study, snow depth and sea ice thickness were retrieved through the measurements of sea ice temperature profiles using discrimination algorithms of the change point and the maximum likelihood detection methods. The data measured by 50 ice-tethered buoys were used to evaluate the accuracy of the results determined by the algorithm. Influences on the seasonal sea ice thermodynamic state, vertical interval of temperature sensors on the buoys, and initial ice thickness on the estimation errors were also evaluated. The performance of the discrimination algorithm for the data from the Arctic and Antarctic regions was also compared. There were no identifiable differences between the estimation errors from the Arctic and Antarctica. Increases in both the interval of the temperature sensors and the initial ice thickness enlarged the error for the estimation of ice thickness. A procedure developed in this study strengthens the potential application of measurements from the ice-tethered buoys only with the measurements of the vertical temperature profile of the layer of snow-covered ice, but not the measurements of ice basal and surface positions using acoustic sounding.

Ex Situ Culturing Experiments Revealed Psychrophilic Hydrogentrophic Methanogenesis Being the Potential Dominant Methane-Producing Pathway in Subglacial Sediment in Larsemann Hills, Antarctic.

It was recognized only recently that subglacial ecosystems support considerable methanogenic activity, thus significantly contributing the global methane production. However, only limited knowledge is available on the physiological characteristics of this kind of methanogenic community because of the technical constraints associated with sampling and cultivation under corresponding environmental conditions. To elucidate methanogenesis beneath the glacial margin in East Antarctic Ice Sheet, we took an integrated approach that included cultivation of microbes associated with the sediment samples in the lab and analysis of mcrA gene therein. After 7 months of incubation, the highest rate of methanogenesis [398 (pmol/day)/gram] was observed at 1°C on a supply of H2. The rates of methanogenesis were lower on acetate or unamended substrate than on H2. The rates on these two substrates increased when the temperature was raised. Methanomicrobiales predominated before and after prolonged incubation, regardless whether H2, acetate, or unamended substrate were the energy source. Therefore, it was inferred that psychrophilic hydrogenotrophic methanogenesis was the primary methane-producing pathway in the subglacial ecosystem we sampled. These findings highlight the effects of temperature and substrate on potential methanogenesis in the subglacial sediment of this area, and may help us for a better estimation on the Antarctica methane production in a changing climate.