Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height - Xin, Linchao; Hu, Shijian; Wang, Fan; Xie, Wenhong; Hu, Dunxin; Dong, Changming (Frontiers in Marine Science, 2023)
The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional to global climate system. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport with sea surface height (SSH) of the Indo-Pacific Ocean. Here we use a deep learning approach with the Convolutional Neural Network (CNN) model to reproduce the ITF transport. The CNN model is trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results shows that the CNN model with SSH is able to reproduce about 90% of the total variance of ITF transport. The CNN model with CMIP6 is then transformed to the Simple Ocean Data Assimilation (SODA) dataset and we find that the transformed model reproduces about 80% of the total variance of ITF transport in SODA. A time series of ITF transport, verified by the Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, is then produced by the model using satellite observations from 1993 to 2021. We discover that the CNN model can make a valid prediction with a leading time of seven months, implying that the ITF transport can be predicted using the deep learning approach with SSH data.