ISSN 2096-5508

e-ISSN 2523-3521

CN 37-1518/P

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    Abstract：Mesoscale eddies significantly affect sound propagation, yet their complex internal sound speed profiles suffer from substantial reconstruction errors, and only few studies specifically addressed this reconstruction. To bridge this gap, we used multi-source satellite data and Argo profiles to identify eddies and build a temperature-salinity-sound speed dataset. Sea surface parameters (temperature, height anomalies, salinity, density) and Argo density serve as inputs for a random forest (RF) algorithm to learn the surface-to-underwater sound speed mapping. Concurrently, a unified eddy dynamic model reconstructs the internal density field. By combining these environmental parameters with the reconstructed density generates eddy sound speed profiles, an integrated PIRF-DEN model that merges machine learning and physical modeling was established. Evaluations demonstrate the superiority of the model. By incorporating density input, the reconstruction accuracy was significantly improved, the mean absolute error (MAE) and root mean square error (RMSE) were reduced to 0.83 and 1.39 m/s, respectively, which is 87.3% and 83.7% less than that of the sEOF­r method. Integration of the eddy model effectively characterized the vertical density structure, whose constraint lowered the overfitting risk and enhanced the accuracy and stability over sEOF­r, sEOF-RF, and RF models. Propagation loss calculations using the reconstructed sound speed showed high correlation (coefficient: 0.77) with measured data, further confirming its reliability.  

    Hongchen LI, Ming LI, Penghao WANG, Kefeng MAO, Yuhang ZHU, Yuhang LIU

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  • Physics

    Abstract：The satellite synthetic aperture radar (SAR) sensor is one of the most critical tools for monitoring Arctic sea ice. Classifying sea ice types based on SAR images has been a research hotspot. Most existing deep-learning-based sea ice classification models rely on the polarimetric information of SAR images while ignoring the gray-level co-occurrence matrix (GLCM) feature. This study develops a three-branch U-Net model for classifying sea ice in SAR images. By integrating polarimetric information, GLCM features, and auxiliary data, the model can classify open water (OW), young ice (YIC), first-year ice (FYI), and old ice (OIC). The model is trained and tested on the well-known AI4Arctic sea ice challenge dataset. Experiments on 57 testing SAR images demonstrate that the proposed model achieves an overall classification accuracy of 91.45% and an Intersection over Union (IoU) of 0.846 4 for the four-type classification. Ablation experiments were conducted to evaluate the sensitivity of various GLCM features to sea ice classification. The effectiveness of the three-branch input for fusing polarimetric information, GLCM feature, and auxiliary data is validated. Results indicate that incorporating HV_mean significantly enhances classification performance, with an accuracy increase of approximately 0.7% and an improvement in IoU of 0.9%. The three-branch input structure is more effective than the single-branch structure in fusing three types of inputs, resulting in an accuracy increase of 4.7% and an improvement in IoU of 7%. Therefore, the proposed three-branch U-Net model demonstrates stable and reliable capabilities for classifying OW, YIC, FYI, and OIC in SAR images, providing a new approach for Arctic sea ice monitoring.  

    Lichen GAO

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  • Physics

    Abstract：The westward expansion of the Beaufort Gyre (BG) is essential for the transport and redistribution of heat, freshwater, and nutrients in the western Arctic Ocean. During 2003–2014, the core and edge of BG expended westward significantly, as indicated by satellite dataset and reanalysis data. An idealized model with spatial and temporal variation of the anticyclonic surface stress also demonstrates the BG westward expansion. Based on the potential vorticity (PV) budget analysis of the idealized model results, the westward advection of thickness perturbation near the continental slope transports the negative PV anomalies generated by variations in surface stress, which contributes to the westward expansion of the BG. Idealized model case studies are conducted to investigate how displacement of surface stress field location, enlargement of surface stress field, and asymmetry enhancement of surface stress field affect the westward expansion and asymmetry of the BG via advective processes. The enlargement of surface stress led to the most significant enhancement in advection of thickness perturbation and a pronounced westward expansion of BG, with both the core and edge of BG westward expansion over 300 km. The asymmetry enhancement of surface stress significantly contributes to the pronounced zonal asymmetry of BG. Advection of thickness perturbation exerts a more pronounced influence on the western edge of BG, while advection of vorticity perturbation has a greater impact on the core of BG. The beta effect is essential for the westward expansion of the BG, a more pronounced beta effect leads to a more rapid westward expansion.  

    Shuhao TAO, Ling DU

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  • Physics

    Abstract：The Amundsen Sea Low (ASL) is a connection between the tropical variabilities and climate changes in the Antarctic. The combination of the negative trend of the Interdecadal Pacific Oscillation (IPO) and the positive trend of the Atlantic Multidecadal Oscillation (AMO) results in the strengthening of the ASL as well as the dipole Sea Ice Concentration (SIC) trend of the Ross Sea (RS) and Amundsen and Bellingshausen Sea (ABS) since the satellite era. In this study, we demonstrate that models in the Coupled Model Intercomparison Project phase 6 (CMIP6) largely underestimate this congruent ASL trend that is related to the IPO and AMO. The unrealistic relationship between the IPO and AMO among coupled models, which potentially cancels out their effects on the ASL each other, is one potential factor for the underestimated ASL variability. Another factor is the large inconsistent AMO-related teleconnection patterns among models. Further analysis is carried out to explore the origin of the large intermodel spread, and the mean SST biases over the tropical Atlantic, tropical Pacific and southeast Pacific are found to be the dominant sources for the underestimated ASL change. These results emphasize the collaborative effect of the IPO and AMO on the Southern Ocean region. Thus, reducing related mean state SST bias may lead to improved coupled simulations of tropical variabilities as well as the climate of the Antarctic.  

    Donglin CAI, Xiao-Tong ZHENG

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