Multimodel probabilistic prediction of 2 m-temperature anomalies on the monthly timescale

Abstract. The 2 m-temperature anomalies from the reforecasts of the CNR-ISAC and ECMWF monthly prediction systems have been combined in a multimodel super-ensemble. Tercile probability predictions obtained from the multimodel have been constructed using direct model outputs (DMO) and model output statistics (MOS), like logistic and nonhomogeneous Gaussian regression, for the 1990–2010 winter seasons. Verification with ERA-Interim reanalyses indicates that logistic regression gives the best results in terms of ranked probability skill scores (RPSS) and reliability diagrams for low–medium forecast probabilities. Also, it is argued that the logistic regression would not yield further improvements if a larger dataset was used.