Using redundancy analysis to improve dynamical seasonal mean 500 hPa geopotential forecasts

In this study, we evaluate and compare 500 hPa geopotential height hindcast skill in two large dynamical hindcast experiments performed with the Canad ian Climate Centre second generation general circulation model (GCM). In on e hindcast experiment, seasonal hindcasts are made from lagged initial cond itions observed at the beginning of each season. The sea-surface temperatur es (SSTs) required by the model during each forecast period are forecast by persisting the SST anomalies observed during the month just prior to the f orecast period. The second hindcast experiment consists of an ensemble of s imulations in which continuously evolving observed SSTs are specified at th e model's lower boundary. These hindcasts do not benefit from re-specificat ion of the initial state at the beginning of each season, but they do enjoy the benefit of 'perfect' SST forecasts. We also demonstrate the use of a r egression technique, called redundancy analysis (RA), for statistically imp roving the skill of both types of dynamical hindcast. The results indicate that specification of the initial state at the beginning of each season add s skill to the seasonal hindcasts, even though SSTs at the lower boundary a re imperfectly specified. We also find that the model can predict the mean state of the North Atlantic Oscillation (NAO) with some skill in boreal win ter and spring when the initial state is specified at the beginning of each season. The results also indicate that statistical post-processing with th e RA technique improves the (cross-validated) skill of both types of dynami cal hindcast. Copyright (C) 2001 Royal Meteorological Society.