An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. We highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version-2. While all ensemble members often agree on the sign of predicted El Nino Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO predictions and a more decisive impact of ENSO on seasonal climate increases the ensemble mean ENSO-Indian Summer Monsoon Rainfall correlation. Thus, a model’s teleconnection skills, which often help interpret physical mechanisms, should be studied using individual members rather than ensemble averages.

This study identifies the role of mid-latitude teleconnection in determining the interannual variability of the Indian summer monsoon in the CFSv2 model. Since CFSv2 has been identified as a potential forecast model for the Indian summer monsoon, it is important to understand the factors that determine its prediction skills at seasonal timescales. ENSO is one of the most important factors driving Indian monsoon variability at seasonal timescales. It is represented realistically in CFSv2. The model, however, misses associated mid-latitude teleconnections. We show that the inadequate strength of mid-latitude teleconnections, especially from the North Atlantic and North-western Pacific can be the primary reasons for the weaker monsoon variability, despite strong ENSO-Monsoon relationship in the model.