A stochastic method for improving seasonal predictions

Abstract

Ensemble seasonal forecasts during boreal winter suffer from insufficient spread and systematic errors. In this study we suggest a new stochastic dynamics method to address both issues at a time. Our technique relies on random additive corrections of initial tendency error estimates of the atmospheric component of the CNRM‐CM5.1 global climate model, using ERA‐Interim as a reference over a 1979–2010 hindcast period. The random method improves deterministic scores for 500‐hPa geopotential height forecasts over the Northern Hemisphere extratropics (NH Z500), and increases the ensemble spread. An optimal method consisting in drawing the error corrections within the current month of the hindcast period illustrates the high potential of future improvements, with NH Z500 anomaly correlation reaching 0.65 and North Atlantic Oscillation index correlation 0.71 with ERA‐Interim. These substantial improvements using current year corrections pave the way for future forecasting methods using classification criteria on the correction population.