Unraveling the Dominant Influence of ENSO over IOD on Australian Springtime Climate Variability Using a Pacemaker Modeling Approach

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Abstract This study employs the Conformal Cubic Atmospheric Model (CCAM) to investigate the independent influences of El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) on Australian springtime rainfall and temperature variability. A pacemaker-style experiment was conducted using a variable-resolution configuration with ∼20-km resolution over Australia. Three 10-member ensemble simulations were performed: a control (CTRL) run, an ENSO-removed (noENSO) run with tropical Pacific ENSO-driven sea surface temperature (SST) variability removed via empirical orthogonal function (EOF) filtering, and an IOD-removed (noIOD) run with IOD-related SST variability removed. The results demonstrate that ENSO is the primary driver of Australian rainfall and temperature variability, dominating patterns traditionally linked to both ENSO and IOD. In the noENSO experiment, correlations between climate variables and ENSO/IOD indices are significantly reduced, while in noIOD, the correlations are only slightly weakened. This underscores the limited role of IOD in the CTRL simulation interannual variability. The circulation analysis reveals that removing ENSO-associated SST variability over the tropical Pacific also eliminates circulation responses beyond the tropical Pacific, confirming that ENSO primarily affects Australian climate variability via teleconnections. Additionally, the sensitivity of rainfall correlations to internal variability highlights the importance of ensemble experiments for robustly assessing ENSO/IOD impacts on regional climate dynamics. However, uncertainties remain regarding the interaction between eastern Pacific El Niño and positive IOD events, warranting further investigation. Significance Statement This study examines how El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) independently influence Australian springtime rainfall and temperature variability. While these climate modes often co-occur and produce similar impacts, we designed a modeling experiment to isolate their effects. By systematically removing each mode, we found that ENSO plays a dominant role in year-to-year variability, whereas IOD has a more limited impact. Our results also highlight the need for large-ensemble simulations to ensure statistically robust conclusions. This research improves seasonal-to-subseasonal weather forecasting and enhances climate change mitigation by refining the attribution of weather events to these modes, reducing uncertainties in future climate assessments.

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