Spatiotemporal characterization and prediction of the subsurface temperature front of the Kuroshio extension

Abstract

The Kuroshio Extension Front (KEF) is one of the most representative mesoscale dynamic processes in the Northwest Pacific, particularly the subsurface KEF that has a substantial influence on the marine environment. Based on the JCOPE2M reanalysis data and WOA23 climatological mean data, this study proposed the Ocean Front Fit Degree (OFFD) as an evaluation criterion for extracting the front lines. For the extraction of the KEF front line, an approach combining isotherms and meridians with the highest temperature gradient was employed. The absolute gradient method and wavelet variance were used for analyzing the spatiotemporal characteristics of the subsurface KEF strength and position. Considering the variations in the KEF characteristics at different time scales, a KEF prediction model based on VMD-LSTM was constructed. The results demonstrated that: (1) The front-line recognition and optimization method proposed in this study is applicable across all water depths and areas with interference and weak fronts, yielding accurate KEF prediction results; (2) The KEF strength and position can be decomposed into trend changes, periodic changes, and irregular wave changes. The factors that may contribute to the low-frequency variations in subsurface KEF include the meridional movement of the Aleutian Low and the Pacific Decadal Oscillation (PDO); (3) The combined VMD-LSTM model predicts the KEF strength with high accuracy, indicating excellent prediction performance and high potential application value compared to individual LSTM model.