Impact of improved ocean initial condition on the seasonal prediction of Indian summer monsoon
Cite this dataset
Pokhrel, Samir et al. (2021). Impact of improved ocean initial condition on the seasonal prediction of Indian summer monsoon [Dataset]. Dryad. https://doi.org/10.5061/dryad.sqv9s4n57
In this study, an effort has been made to show the impact of improved ocean initial condition in the coupled forecast system (CFSv2) on the seasonal prediction skill of Indian summer monsoon rainfall (ISMR). CFSv2 is used as an operational dynamical model for the seasonal prediction of ISMR. The new improved ocean initial condition is based on Global Ocean Data Assimilation System (GODAS) analysis and is produced by assimilating vertical profiles of observed temperature and salinity from all the sources (XBTs, buoys and Argo profiling floats) over the global ocean using 3Dvar assimilation scheme and MOM4p1 ocean model. This new analysis is improved compared to the NCEP GODAS which uses earlier generation MOM4p0d and assimilates observed temperature and synthetic salinity. Twin sets of identical model experiments differing in initial conditions (IC) with the former (later) using NCEP IC (new IC; NIC) are performed. The NIC experiment shows consistent enhancement of ENSO skill compared to NCEP IC. This advancement leads to the improvement of ISMR skill. We found that the significant improvement of surface and sub-surface temperature, thermocline depth, and heat content over the global ocean particularly in the Nino3 region in the NIC compared to NCEP IC contributed to the improved ISMR skills. This enhanced ISMR skill score might be the result of reduced model drift in the NIC even on 4 month lead and capturing the ISMR – ENSO teleconnection with great fidelity.