High ENSO-based 18-month lead Potential Predictability of Indian Summer Monsoon Rainfall

TL;DR

This study demonstrates that 18-month lead forecasts of Indian summer monsoon rainfall have high potential skill due to ENSO-related slow-varying predictors, emphasizing the importance of nonlinear models for accurate long-term prediction.

Contribution

The paper introduces a new predictor discovery method highlighting D20's role in long-lead ISMR prediction and underscores the significance of nonlinearity in potential predictability.

Findings

High potential skill (r=0.86) for 18-month lead ISMR forecasts.

D20 depth is minimally affected by atmospheric noise.

Nonlinear predictor discovery enhances long-term prediction accuracy.

Abstract

Scientific basis for long-lead seasonal prediction of Indian summer monsoon rainfall (ISMR) critical for water resource and crop strategy planning is lacking. Using a new predictor discovery method, here we show that the depth of 20 degree isotherm (D20) is least influenced by atmospheric noise and that the 18-month lead forecasts of ISMR have high potential skill (r = 0.86). The high potential predictability is due to smaller initial errors associated with the 18-month lead initial conditions and their slow growth associated with the El Nino and Southern Oscillation (ENSO). The potential skill arises not only from the correlation between ISMR and large-scale slowly varying D20 but also contributed significantly by that with the interannual small-scale D20 anomalies indicating a seminal role of the nonlinearity on the potential predictability. It is, therefore, imperative that a nonlinear predictor discovery as well as nonlinear prediction model is essential for realizing this potential predictability.