Rethinking Indian monsoon rainfall prediction in the context of recent global warming.

Area of Science:

• Climate Science
• Meteorology
• Tropical Climate Prediction

Background:

• Indian summer monsoon rainfall (ISMR) prediction is crucial for tropical climate forecasting.
• Operational ISMR forecasts (1989-2012) have shown limited skill despite historical progress.

Purpose of the Study:

• To identify the reasons behind the recent decline in ISMR forecast skill.
• To develop improved models for accurate ISMR prediction by incorporating new predictability sources.

Main Methods:

• Utilized both dynamical and physical-empirical climate models.
• Analyzed the impact of emerging predictors like central-Pacific El Nino-Southern Oscillation (CP-ENSO), deepening Asian Low, and strengthening Pacific Highs.
• Developed and validated a physical-empirical model incorporating these new predictors.

Main Results:

• Recent forecast failures are linked to models' inability to capture CP-ENSO, deepening Asian Low, and strengthening Pacific Highs.
• The improved physical-empirical model achieved an independent forecast skill of 0.51 (1989-2012) and a 92-year retrospective skill of 0.64 (1921-2012).
• Dynamical models showed deficiencies in capturing CP-ENSO and anomalous Asian Low, contributing to low skill.

Conclusions:

• The study highlights a significant gap between ISMR prediction skill and actual predictability.
• Incorporating newly identified climate predictors is essential for enhancing ISMR forecast accuracy.
• Future ISMR prediction models must account for evolving climate dynamics, including CP-ENSO and associated atmospheric patterns.