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

TL;DR

This study evaluates the Indian summer monsoon rainfall forecast skill of the CFSv2 model, highlighting how initial conditions and model biases affect prediction accuracy, especially regarding ENSO and regional convection influences.

Contribution

It identifies the causes of forecast skill variations in CFSv2, emphasizing the impact of model biases and initial conditions on monsoon rainfall predictions.

Findings

Highest forecast skill at February initial conditions due to specific 1983 event

Model biases in SST and ENSO influence lead to errors in ISMR prediction

Forecast skill for ENSO is limited at longer lead times, indicating dynamical drift

Abstract

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 ISM rainfall (ISMR) forecast skill with February (L3) ICs. The reported highest L3 skill is based on correlation between observed and predicted interannual variation (IAV) of ISMR. Other scores such as mean error, bias, RMSE, mean, standard deviation and coefficient of variation, indicate higher or comparable skill for April(L1)/May(L0) ICs. Though theory suggests that forecast skill degrades with increase in lead-time, CFSv2 shows highest skill with L3 ICs, due to predicting 1983 ISMR excess for which other ICs fail. But this correct prediction is caused by wrong forecast of La Nina or cooling of equatorial central Pacific (NINO3.4) during ISM season. In observation, normal sea surface temperatures (SSTs) prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean or EQUINOO, which CFSv2 failed to capture with all ICs. Major results are reaffirmed by analysing an optimum number of 5 experimental reforecasts by current version of CFSv2 with late-April/early-May ICs having short yet useful lead-time. These reforecasts showed least seasonal biases and highest ISMR correlation skill if 1983 is excluded. Model deficiencies such as over-sensitivity of ISMR to SST variation over NINO3.4 (ENSO) and unrealistic influence of ENSO on EQUINOO, contribute to errors in ISMR forecasting. Whereas, in observation, ISMR is influenced by both ENSO and EQUINOO. Forecast skill for Boreal summer ENSO is found to be deficient with lowest skill for L3/L4 ICs, hinting the possible influence of long lead-time induced dynamical drift. The results warrant the need for minimisation of bias in SST boundary forcing to achieve improved ISMR forecasts.