SUMMARYEnsembles of meteorological forecasts can both provide more accurate long-term forecasts and help assess the uncertainty of these forecasts. No single method has however emerged to obtain large numbers of equiprobable scenarios from such ensembles. A simple resampling scheme, the 'best member' method, has recently been proposed to this effect: individual members of an ensemble are 'dressed' with error patterns drawn from a database of past errors made by the 'best' member of the ensemble at each time step. It has been shown that the best-member method can lead to both underdispersive and overdispersive ensembles. The error patterns can be rescaled so as to obtain ensembles which display the desired variance. However, this approach fails in cases where the undressed ensemble members are already overdispersive. Furthermore, we show in this paper that it can also lead to an overestimation of the probability of extreme events. We propose to overcome both difficulties by dressing and weighting each member differently, using a different error distribution for each order statistic of the ensemble. We show on a synthetic example and using an operational ensemble prediction system that this new method leads to improved probabilistic forecasts, when the undressed ensemble members are both underdispersive and overdispersive.

A simple and robust framework is proposed for the partitioning of the different components of internal variability and model uncertainty in an unbalanced multimember multimodel ensemble (MM2E) of climate projections obtained for a suite of statistical downscaling models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of the different modeling chains using a two-way analysis of variance (ANOVA) framework. The residuals from the noise-free signals are used to estimate the large- and small-scale internal variability components associated with each considered GCM–SDM configuration. This framework makes it possible to take into account all members available from any climate ensemble of opportunity. Uncertainty is quantified as a function of lead time for projections of changes in temperature and precipitation produced for a mesoscale alpine catchment. Internal variability accounts for more than 80% of total uncertainty in the first decades. This proportion decreases to less than 10% at the end of the century for temperature but remains greater than 50% for precipitation. Small-scale internal variability is negligible for temperature; however, it is similar to the large-scale component for precipitation, whatever the projection lead time. SDM uncertainty is always greater than GCM uncertainty for precipitation. It is also greater for temperature in the middle of the century. The response-to-uncertainty ratio is very high for temperature. For precipitation, it is always less than one, indicating that even the sign of change is uncertain.