Usefulness of ensemble forecasts from NCEP Climate Forecast System in sub-seasonal to intra-annual forecasting

Typically, sub-seasonal to intra-annual climate forecasts are based on ensemble mean (EM) predictions. The EM prediction provides only a part of the information available from the ensemble forecast. Here we test the null hypothesis that the observations are randomly distributed about the EM predictions using a new metric that quantifies the distance between the EM predictions from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the observations represented by CFSv2 Reanalysis. The null hypothesis cannot be rejected in this study. Hence, we argue that the higher order statistics such as ensemble standard deviation are also needed to describe the forecast. We also show that removal of systematic errors that are a function of the forecast initialization month and lead time is a necessary pre-processing step. Finally, we show that CFSv2 provides useful ensemble climate forecasts from 0 to 9 month lead time in several regions.