Improved Indian Ocean dipole seasonal prediction in the new generation of CMA prediction system

Abstract

Abstract Seasonal prediction of the Indian Ocean dipole (IOD) is important, considering its impact on the climate of surrounding regions. Here we compare the prediction of the IOD in two generations of prediction system developed by the China Meteorology Administration (CMA), i.e., the second-generation climate model prediction system (CPSv2) and CPSv3. The results show that CPSv3 has better ability to predict the variability and spatial pattern of the IOD than CPSv2, especially when the lead time is long. CPSv3 maintains a certain level of credibility when predicting IOD events with 6-month lead time. The improved data assimilation in CPSv3 has reduced the predictability error of eastern Indian Ocean sea surface temperature (SST) and contributed to improvements in IOD prediction. Enhanced simulation of the El Niño-Southern Oscillation (ENSO)–IOD relationship promotes better prediction skill of ENSO-related IOD events in CPSv3. Our results suggest that upgrading data assimilation and the simulation of the ENSO–IOD relationship are critical for improving the prediction of the IOD in coupled climate models.

Keywords

• Indian Ocean dipole
• Seasonal prediction
• Climate extremes
• Climate models