Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction

Abstract

Using the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1, we explored the precursory disturbances that are most likely to develop into a positive Indian Ocean Dipole (IOD). The dominant spatial patterns of these precursors are defined as the optimal precursors (OPRs) of positive IOD as they are more inclined to cause a positive IOD than other superimposed initial perturbations in the experiments. Specifically, there are two types of OPRs with opposite patterns; the surface component of OPR‐1 (OPR‐2) is an indistinctive west‐east dipole pattern, with a small area of negative (positive) perturbations to the coast of Sumatra and Java. Correspondingly, there is a significant west‐east dipole pattern in the subsurface component of the OPRs, with the largest values located in the eastern equatorial Indian Ocean. The dominant mode of the time‐dependent evolutions of the precursors features rapid development of positive IOD. Furthermore, the OPRs are similar to the optimally growing initial errors (OGEs) associated with IOD predictions that have been presented in previous studies. The shortwave radiation, latent heat flux, and westward Rossby waves play an important role in the time‐dependent evolution of OGEs. Moreover, the large values of the OPRs are located in the same areas as the sensitive areas of targeted observations identified by the OGEs. This infers that intensive observations over these areas would not only reduce initial errors, improve the accuracy of initial fields and decrease the prediction errors but would also detect the precursory signals in advance, which substantially improves the forecast skill of IOD.

Predictability of the Super IOD Event in 2019 and Its Link With El Niño Modoki

“…While these precursors explain some of the IOD events, predicting IOD event is still challenging. Above all, how to overcome the so‐called “winter predictability barrier” is still a problem (Feng et al, 2017; Mu et al, 2017; Wajsowicz, 2005). However, the 2019 event was successfully predicted using the SINTEX‐F system over the winter predictability barrier; it is of interest to study why the barrier was overcome.…”

Increasingly important role of numerical modeling in oceanic observation design strategy: A review

“…Some studies have already revealed that optimally designed observations can result in better prediction performance than conventional observations (Alvarez and Mourre, 2012;Li et al, 2014). By using the CNOP approach, our group has also optimized the observation designs to improve the predictions of high-impact ocean-atmospheric extreme events (Mu et al, 2007;Yu et al, 2012;Wang et al, 2013;Mu et al, 2017;Liu et al, 2018;Zhang et al, 2019). In these studies, the optimal observation locations are different for predicting and mapping the same phenomenon.…”

A Region‐Optional Targeted Observation Method and Its Application in the Sea Surface Temperature Prediction Associated With the Indian Ocean Dipole

“…It has been found that the IOD has significant impacts on global weather and climate (Blau & Ha, 2020; Chan et al., 2008; Ratna et al., 2021; Saji & Yamagata, 2003). Targeted observation analysis for SST associated with the IOD has been reported in the literature, but only using the first approach described above; for example, the perfect model predictability experiments (Feng et al., 2017), and the conditional nonlinear optimal perturbation (Mu et al., 2017). Therefore, it is of interest to conduct similar analyses of targeted observations using a different method, shedding light on the robustness of previously reported results.…”