Predictability of Sea Surface Temperature Anomaly of the Eastern Indian Ocean Over Past 142 Years

Abstract This study studies the predictability of the sea surface temperature anomaly (SSTA) for the eastern Indian Ocean (EIO) over the past 142 years from 1880–2021 by exploring its fastest error growth. This is implemented by applying the climate‐relevant singular vector (CSV) analysis onto a tropical intermediate coupled model. Emphasis places on identifying the optimal perturbation that can result in the fastest prediction error growth of the EIO SSTA. It is found that, with the 6‐month lead prediction, the spatial pattern of the optimal initial perturbations is characterized by a negative SSTA pole with the diverged surface wind in the southern Indian Ocean and a positive SSTA pole with the converged surface wind in the eastern Pacific. The growth rate of optimal initial perturbation, as measured by the leading singular value, exhibits significant interannual variation consistent with the variation of the SSTA in EIO. Specifically, when the SSTA in the EIO is colder, the prediction error grows more rapidly, and conversely, when the SSTA is warmer, the error grows more slowly. The results of the dynamical analysis show that a cold SSTA background strengthens Ekman feedback through both atmospheric and oceanic responses, leading to faster growth of prediction errors. This study enhances our understanding to the predictability in the Indian Ocean, in particular the role of EIO in the Indian Ocean dipole predictability.