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Oceanic mesoscale eddies are prevalent throughout the global ocean, playing a critical role in material and energy transport while significantly influencing climate change. Accurate characterization of their three-dimensional structures and movement is essential for a quantitative analysis of their transport processes. Traditional eddy detection algorithms have lower successful detection rate and with more limitations, so they fall short in the complex and dynamic ocean environment. The rising trend of applying artificial intelligence (AI) algorithms, due to their efficiency, precision, and automation, addresses this challenge. This study employs the 3D-U-Res-Net algorithm to identify the three-dimensional structures of mesoscale eddies in the Southern Ocean using GLORYS12V1 data from 2011 to 2020. A vector geometry-based eddy detection algorithm (VG) initially identified 1587292 eddy snapshots in the Southern Ocean (2011–2019), which were used for training the 3D-U-Res-Net algorithm. Data from 2020 served as the ground truth and validation set. The successful detection rate of 3D-U-Res-Net algorithm is 100%, which means that it identified all 135734 eddy snapshots from the VG dataset in 2020. For eddy tracking, the VG algorithm counted 18168 eddy tracks, whereas the 3D-U-Res-Net counted 18559, reflecting a 2.15% bias. To reduce uncertainty, eddies with lifespans shorter than two weeks were excluded. The average lifespans and traveling distances for eddies detected by the 3D-U-Res-Net (VG) algorithm were 29.35 (29.61) days and 77.78 (37.60) km, respectively, with the 3D-U-Res-Net identifying eddies with longer traveling distances. The mean radius of eddies detected by the VG algorithm was 43.16 km, while the 3D-U-Res-Net detected eddies with a mean radius of 43.74 km, a 0.58 km increase. We categorized eddies into four three-dimensional structures: bowl-shaped, cone-shaped, lens-shaped, and cylindrical. The VG algorithm identified these structures in proportions of 32%, 31%, 25%, and 12%, respectively, whereas the 3D-U-Res-Net algorithm found 19.48%, 19.58%, 0.04%, and 60.9%, respectively. The 3D-U-Res-Net identified more cylindrical eddies and was approximately ten times faster than the VG algorithm. Overall, this algorithm has good performance and higher efficiency. It is an attempt of using AI for oceanic research, and more works can be carried out in the future.

The East Greenland Polar Front (EGPF) is an important front with strong salinity and temperature gradients in the Nordic Seas. It is formed by the interaction between Arctic-origin and Atlantic-origin water. The variations of EGPF are closely linked with sea ice melting and heat content transport associated with North Atlantic water recirculation. For a three-dimensional (3D) daily analysis, we use the global ocean eddy resolution reanalysis product (GLORYS12V1) from 1993 to 2018 to calculate the salinity and temperature horizontal gradient in the upper ocean and obtain the spatiotemporal distribution and intensity characteristics of EGPF. After assessment, the thresholds of the salinity and temperature fronts are set to 0.04 psu/km and 0.09°C/km, respectively. Compared with satellite observations of sea ice concentration, a significant spatial relationship is observed between the main position of EGPF and the ice edge before the sea ice shrinks to the continental shelf sea area. Affected by the freshening of the Arctic-origin water due to the melting of the sea ice, the intensity and area of EGPF show significant seasonal variations. Against the background of global warming, the sea ice area presents an obvious decreasing trend in the Greenland Sea. The melting of sea ice increased annually every summer from 1998 to 2018. The heat content transport of the Atlantic-origin water has also increased in recent years. The 3D characteristics (intensity and volume) of EGPF as salinity and temperature fronts exhibit increasing trends.