The Indonesian Throughflow (ITF) connects the Pacific Ocean and the Indian Ocean. It plays an important role in the global ocean circulation system. The interannual variability of ITF transport is largely modulated by climate modes, such as Central-Pacific (CP) and Eastern-Pacific (EP) El Niño and Indian Ocean Dipole (IOD). However, the relative importance of these climate modes importing on the ITF is not well clarified.
Dominant roles of the climate modes on ITF in specific periods are quantified by combining a machine learning algorithm of the random forest (RF) model with a variety of reanalysis datasets.
The results reveal that during the period from 1993 to 2019, the average ITF transport derived from high-resolution reanalysis datasets is -14.97 Sv with an intensification trend of -0.06 Sv year-1, which mainly occurred in the upper layer. Four periods, which are 1993–2000, 2002–2008, 2009–2012 and 2013–2019, are identified as Niño 3.4, Dipole Mode Index (DMI), no significant dominant index, and DMI dominated, respectively.
The corresponding sea surface height differences between the Northwest Tropical Pacific Ocean (NWP) and Southeast Indian Ocean (SEI) in these three periods when exist dominant index are -0.50 cm, 0.99 cm and -3.22 cm, respectively, which are responsible for the dominance of the climate modes. The study provides a new insight to quantify the response of ITF transport to climate drivers.