Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2

The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information‐based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead‐4 to lead‐2 and then decreases at lead‐1 followed by again increase at lead‐0. While the actual ISMR prediction skill is highest at lead‐3 forecast (second highest at lead‐1), the potential predictability is highest at lead‐2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño‐Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required. Key Points: Potential predictability of ISMR simulated by CFSv2 is estimated In general, potential predictability increases with decrease in lead forecast time Actual ISMR prediction skill is highest (second highest) with February (April) initial conditions