## 1. Introduction

The concept of weather regimes has long been invoked to explain the perception that weather conditions appear to persist longer than the passage of individual systems. This idea was initially closely related to the concept of weather analogues: the assumption that similar large-scale flow patterns are associated with similar weather types and evolve in a similar manner. In this vein, catalogues of regime classifications such as Grosswetterlagen (Hess and Brezowsky, 1952) aimed to provide a qualitative partitioning of the observed atmosphere into a discrete set of flow types, each associated with different weather conditions. The advent of dynamical systems theory and the discovery of chaos (Lorenz, 1963) both debunked the atmospheric analogues idea and appeared to provide an explanation for the existence of atmospheric regimes. In low-dimensional nonlinear systems, the regimes are associated with stable (or weakly unstable) equilibrium solutions to the dynamical equations to which the state remains close. The wings of the Lorenz (1963) ‘butterfly’ are the classic example of this behaviour. Whilst there have been attempts to explain atmospheric regimes through equilibrium solutions to low-dimensional atmospheric models (Charney and DeVore, 1979; Crommelin, 2003), the link to high-dimensional atmospheric global circulation models and the actual atmosphere remains unclear. Regimes in such high-dimensional systems are usually diagnosed from output data by examination of probability density function estimates for evidence of multimodality (Silverman, 1981; Corti *et al.*, 1999; Ambaum, 2008; Woollings *et al.*, 2010b) and applying statistical techniques such as clustering (Smyth *et al.*, 1999; Hannachi, 2007; Cassou, 2008; Franzke *et al.*, 2009), rather than by analysis of the dynamical equations themselves.

One of the motivating factors for interest in regimes is their implications for predictability. These implications are something of a double-edged sword: on the one hand, knowing that you have entered a persistent regime may provide useful predictive skill for extended-range forecasting, but conversely failing to predict a change of regime accurately may lead to a significant loss in skill. One of the stated purposes of medium-range ensemble forecasting is to account for the possibility of small uncertainties in initial conditions leading to large differences in forecast outcomes, due to the nonlinear nature of the atmosphere. As such, if regimes (which are an inherently nonlinear phenomenon) exist, ensemble forecasts should, by design, be able to capture the transitions between them. Regardless of the existence (or not) of atmospheric regimes, cluster analysis provides a low-dimensional approximation to the atmospheric phase space, which optimally characterizes the broad characteristics of atmospheric data with respect to a chosen measure. This article addresses the question of whether operational medium-range ensemble forecasts replicate the statistics and predict the future state of such low-dimensional representations of the atmosphere. This is approached by examining the ability of the global 15 day ensemble forecasts from three different forecasting centres taken from the Thorpex Interactive Grand Global Ensemble (TIGGE) dataset (Park *et al.*, 2008) to replicate the transition statistics of a threecluster model designed to characterize the behaviour of the North Atlantic eddy-driven jet (Woollings *et al.*, 2010a). The ensemble forecasts used in the study come from the European Centre for Medium-Range Weather Forecasts (ECMWF), the (UK) Met Office and the Meteorological Service of Canada (CMC). For details on the forecast models and data the reader is referred to http://tigge.ecmwf.int.

The rest of the article is divided into four sections. Section 2 provides an introduction to the three North Atlantic eddy-driven jet regimes and the clustering method used to identify them in forecast data. Section 3 contains an examination of the ability of the forecast models to replicate the climatological probabilities of regime transition. In section 4 the skill of the forecasts in predicting regime transitions is assessed. A summary and conclusions are contained in section 5.