Assessing the Impact of Cold Tongue Bias on IOD Predictability Using a Model-Analog Method

In this study, we apply the Model‑based Analog Forecast (MAF) approach to perform Indian Ocean Dipole (IOD) hindcasts using CMIP6 pre‑industrial simulations. The MAF method constructs forecast ensembles by identifying states in existing model simulations that best match an observed initial anomaly and then tracing their subsequent evolution, without requiring additional model integrations. By optimizing key parameters in the MAF framework, we demonstrate that the MAF‑based IOD hindcasts exhibit skill comparable to that of assimilation‑initialized hindcasts. Utilizing this approach, we investigate the diversity in IOD prediction skill across different climate models, with a focus on the impact of cold tongue bias on forecast performance. Our analysis reveals substantial inter‑model spread in IOD prediction skill within CMIP6 models, with useful predictability extending up to 1–4 months depending on the model. Furthermore, we identify a clear link between cold tongue bias and IOD prediction skill: models with a stronger cold tongue bias show weaker El Niño–Southern Oscillation (ENSO) teleconnections into the tropical Indian Ocean, which consequently reduces their IOD forecast capability. These results offer valuable insights into the sources of IOD prediction diversity and underscore potential pathways for improving IOD forecasting.