An evaluation of decadal probability forecasts from state-of-the-art climate models

While state-of-the-art models of the Earth�s climate system have improved tremendously over the last twenty years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of those models not only with each other but also with their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU ENSEMBLES project is contrasted with several empirical models. Both the ENSEMBLES models and a �Dynamic Climatology� empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The Dynamic Climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today�s state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extend to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision-support. Ultimately the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress towards that goal which is not available in model-model intercomparisons.