Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was 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 showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data

Vertical distribution of intraseasonal variation signals in the Kuroshio Current source area during 2018–2020

In the Kuroshio Current (KC) source area, intraseasonal variation (ISV) plays a significant role in dynamic oceanic processes. This study used data collected from three moorings (122.7°E, 123°E, 123.3°E) along 18°N from January 2018 to May 2020 to investigate the ISVs of meridional velocities. Notably, our findings reveal that the ISV above 200 m has a period of approximately 56 days and its intensity exhibits a gradual increase toward the west. For the 500–800 m depth interval, the ISV period is 73 days at 122.7°E/18°N and 60 days at 123.3°E/18°N. This discrepancy indicates that the ISVs have different vertical structures and frequencies at 122.7°E and 123.3°E along 18°N. In particular, at 122.7°E/18°N, the distinctiveness of two different periods of ISVs in surface and subsurface layers was more pronounced in 2018 than in 2019. The analyses of eddy kinetic energy distribution and eddy tracking indicate a connection between ISV in stratification and locally generated mesoscale eddies in the KC source area. Specifically, the stronger eddy activity in 2018, in contrast with that in 2019, correlates with a more pronounced ISV. Energy analysis demonstrates a distinct positivity in the baroclinic conversion rate (BC) in the surface layer (upper 200 m) of the KC source region, surpassing the absolute value of the barotropic conversion rate (BT). This finding indicates a notable shift of energy from eddy available potential energy to eddy kinetic energy, strengthening the high-frequency ISV signals in this area. In the subsurface layer, a strong negative BT is observed west of 122.8°E, with its absolute value exceeding the BC. This finding indicates that the energy is converted from eddy kinetic energy into mean kinetic energy, resulting in the appearance of the Luzon Undercurrent (LUC) at mooring station 122.7°E/18°N, characterized by a low frequency of ISV. Contrastingly, a positive BT plays a dominant role at 123.3°E/18°N, leading to the disappearance of the LUC amid an apparent presence of high-frequency ISV