Disentangling ENSO and IOD Teleconnections in Precipitation Variability

Tropical modes of variability, such as the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), exert a strong influence on the interannual variability of tropical precipitation, as well as on precipitation across large regions of the extra-tropics. However, widely used indices of ENSO and IOD variability exhibit substantial co-variability, making it challenging to robustly quantify the independent contribution of each mode to precipitation anomalies. A common approach to this issue involves statistically removing either ENSO or IOD variability from precipitation fields prior to calculating teleconnection patterns.

Here, we estimate the independent contributions of these dominant tropical modes by conducting a suite of coupled and uncoupled (atmospheric-only) modeling experiments, in which the sea surface temperature (SST) variability associated with either ENSO or IOD is suppressed. The partially coupled experiments include a fully dynamic ocean, but SSTs are restored to the model’s monthly mean climatology (CTRL-coupled) within specified regions. We apply two different restoring masks: the first spans the eastern Pacific (from 180°W to the American coast, 20°S–20°N) to suppress ENSO-related SST variability (noENSO-coupled experiment), and the second covers the Indian Ocean and western Pacific (from the African coast to the Maritime Continent, 20°S to the Asian coast) to suppress IOD-related SST variability (noIOD-coupled experiment).

Using Australia as a case study, we show that precipitation patterns attributed to ENSO, when computed by statistically removing the IOD influence, significantly underestimate the true impact of ENSO on precipitation variability. Conversely, we find that IOD teleconnections estimated by regressing the Dipole Mode Index (DMI) onto June–October mean precipitation anomalies tend to overestimate the role of the IOD. Motivated by these findings, we propose a conceptual framework that offers a more effective approach for disentangling the independent contributions of ENSO and IOD to precipitation variability.