The Indian Summer Monsoon Rainfall (ISMR) plays a pivotal role in shaping India's agriculture, water resources, and overall economic stability. However, its year-to-year variability, predominantly influenced by the El Niño–Southern Oscillation (ENSO), presents major forecasting challenges. Traditional sea surface temperature (SST)-based indices like the Oceanic Niño Index (ONI) have long been used to monitor ENSO. Yet, their reliability is increasingly compromised by the global warming trend, which distorts the timing and intensity of ENSO signals.
To address this limitation, the Relative Oceanic Niño Index (RONI) has been proposed as an alternative that adjusts for the background warming signal. This study explores the potential of RONI in enhancing ENSO monitoring and forecasting accuracy, particularly in the context of its impact on the monsoon system. Using NOAA’s ERSSTv5 dataset, the onset and demise of ENSO phases were analyzed through both ONI and RONI. Results indicate that RONI more effectively captures the influence of ENSO on atmospheric circulation and rainfall patterns, aligning more closely with observed monsoonal behavior.
Furthermore, forecasts from the Monsoon Mission Coupled Forecast System (MMCFS) show improved correlation with observed conditions when using RONI, especially in the latter half of the year. These findings suggest that RONI is a more robust tool for seasonal climate predictions, offering a deeper understanding of the ENSO-ISMR linkage and enhancing the potential for more accurate monsoon forecasting and proactive risk management in India.
The relative Niño 3.4 index was computed following the approach described by van Oldenborgh et al. (2021) and L’Heureux et al. (2024). First, the conventional Niño 3.4 index was calculated by averaging sea surface temperature (SST) anomalies over the Niño 3.4 region (5°S–5°N, 190°E–240°E). Simultaneously, the tropical mean SST anomalies were calculated by averaging over the 30°S–30°N belt. Anomalies for both indices were obtained by subtracting the corresponding monthly climatology for the 1991–2020 period.
The relative Niño 3.4 index (RONI) was then defined as the difference between the Niño 3.4 SST anomaly and the tropical mean SST anomaly. However, this subtraction reduces the variance of the resulting time series. Since operational agencies rely on the magnitude of anomalies to determine ENSO status, maintaining variance is essential. Therefore, the RONI time series was scaled to match the variance of the original Niño 3.4 index (ONI).
This scaling ensures the resulting RONI is comparable in variability to the ONI, preserving its utility for threshold-based ENSO monitoring. In both forecast and observational datasets, this scaling method is more computationally efficient and easier to implement than the regression-based approach used by van Oldenborgh et al. (2021).
Relative ONI= (ONI - TropAve) x σ ONI / σ (ONI−TropAve)
Here, σ ONI represents the standard deviation of the ONI, while σ (ONI−TropAve) denotes the standard deviation of the ONI after subtracting the tropical mean SST anomaly. The scaling factor σ ONI/σ (ONI−TropAve) is calculated separately for each calendar month.