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We analyse Indian summer monsoon (ISM) seasonal reforecasts by CFSv2 model,initiated from January (4-month lead time, L4) through May (0-month lead time,L0) initial conditions (ICs), to examine the cause for highest all-India ISMrainfall (ISMR) forecast skill with February (L3) ICs. The reported highest L3skill is based on correlation between observed and predicted interannualvariation (IAV) of ISMR. Other scores such as mean error, bias, RMSE, mean,standard deviation and coefficient of variation, indicate higher or comparableskill for April(L1)/May(L0) ICs. Though theory suggests that forecast skilldegrades with increase in lead-time, CFSv2 shows highest skill with L3 ICs, dueto predicting 1983 ISMR excess for which other ICs fail. But this correctprediction is caused by wrong forecast of La Nina or cooling of equatorialcentral Pacific (NINO3.4) during ISM season. In observation, normal sea surfacetemperatures (SSTs) prevailed over NINO3.4 and ISMR excess was due to variationof convection over equatorial Indian Ocean or EQUINOO, which CFSv2 failed tocapture with all ICs. Major results are reaffirmed by analysing an optimumnumber of 5 experimental reforecasts by current version of CFSv2 withlate-April/early-May ICs having short yet useful lead-time. These reforecastsshowed least seasonal biases and highest ISMR correlation skill if 1983 isexcluded. Model deficiencies such as over-sensitivity of ISMR to SST variationover NINO3.4 (ENSO) and unrealistic influence of ENSO on EQUINOO, contribute toerrors in ISMR forecasting. Whereas, in observation, ISMR is influenced by bothENSO and EQUINOO. Forecast skill for Boreal summer ENSO is found to bedeficient with lowest skill for L3/L4 ICs, hinting the possible influence oflong lead-time induced dynamical drift. The results warrant the need forminimisation of bias in SST boundary forcing to achieve improved ISMRforecasts.

Sensitivity of Indian summer monsoon rainfall forecast skill of CFSv2 model to initial conditions and the role of model biases