Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs

A new approach to combine precipitation forecasts from multiple models is evaluated by analyzing the skill of the candidate models contingent on the forecasted predictor(s) state. Using five leading coupled GCMs (CGCMs) from the ENSEMBLES project, we develop multimodel precipitation forecasts over the continental United States (U. S) by considering the forecasted Nino3.4 from each CGCM as the conditioning variable. The performance of multimodel forecasts is compared with individual models based on rank probability skill score and reliability diagram. The study clearly shows that multimodel forecasts perform better than individual models and among all multimodels, multimodel combination conditional on Nino3.4 perform better with more grid points having the highest rank probability skill score. The proposed algorithm also depends on the number of years of forecasts available for calibration. The main advantage in using this algorithm for multimodel combination is that it assigns higher weights for climatology and lower weights for CGCM if the skill of a CGCM is poor under ENSO conditions. Thus, combining multiple models based on their skill in predicting under a given predictor state(s) provides an attractive strategy to develop improved climate forecasts. Citation: Devineni, N., and A. Sankarasubramanian (2010), Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs, Geophys. Res. Lett., 37, L24704, doi: 10.1029/2010GL044989.