Role of Improved Ocean Initial State in the Seasonal Prediction of Indian Summer Monsoon: A Case Study

Abstract

This case study has made an effort to show the impact of improved ocean initial conditions (ICs) in a coupled forecast system (CFSv2) simulation on the seasonal prediction of Indian summer monsoon rainfall (ISMR). CFSv2 is used as an operational dynamical model for the seasonal prediction of ISMR. Here, we show an improved ISMR skill by initializing the ocean component of CFSv2 using new improved ocean ICs based on Global Ocean Data Assimilation System (GODAS) analysis. This new analysis is better than the NCEP GODAS, which uses the earlier-generation ocean model MOM4p0d and assimilates observed temperature and synthetic salinity using the 3DVar assimilation scheme. However, the new, improved GODAS analysis uses the MOM4p1 ocean model and assimilates observed salinity instead of synthetic salinity. We performed twin sets of nearly identical model experiments differing only in their ICs, with one set using NCEP ICs and the other using the new ICs (NIC). The NIC experiment consistently shows better El Niño–Southern Oscillation prediction skill than the NCEP IC experiment. This advancement leads to improvement in the ISMR skill. We found that the substantial improvements in both oceanic and atmospheric variables in a coupled feedback system contributed to the improved ISMR skills. The enhanced ISMR skill score of the NIC experiment might be the result of improved teleconnections, better depiction of large-scale monsoon circulations, and reduced model drift.