Sea surface heat flux helps predicting thermocline in the South China Sea

Abstract

In this study, a deep learning model called Four Dimensional Residual Network (4D-ResNet) was proposed, which can capture both temporal and spatial information. Temperatures at various depths were predicted for the next 40 days using the last month's sea surface variables, and a spatio-temporal prediction of the thermocline was achieved. In addition to the satellite-observed sea surface parameters: sea surface temperature (SST), sea level anomaly (SLA), and sea surface wind (SSW), net heat flux (Q_net) was also included in the model input. Q_net can alter the density of the upper water, resulting in convection or improved stratification stability. The results indicate that the additional input of Q_net improves the model's accuracy, especially at the depth of the thermocline, where the RMSE reduced by up to 13.7%. The 4D-ResNet model has much lower estimation error compared to other models and successfully captures the seasonal characteristics of the thermocline.