| description abstract | rojections of future climate change are increasingly based on the output of many different models. Typically, the mean over all model simulations is considered as the optimal prediction, with the underlying assumption that different models provide statistically independent information evenly distributed around the true state. However, there is reason to believe that this is not the best assumption. Coupled models are of comparable complexity and are constructed in similar ways. Some models share parts of the same code and some models are even developed at the same center. Therefore, the limitations of these models tend to be fairly similar, contributing to the well-known problem of common model biases and possibly to an unrealistically small spread in the outcomes of model predictions.This study attempts to quantify the extent of this problem by asking how many models there effectively are and how to best determine this number. Quantifying the effective number of models is achieved by evaluating 24 state-of-the-art models and their ability to simulate broad aspects of twentieth-century climate. Using two different approaches, the amount of unique information in the ensemble is calculated and the effective ensemble size is found to be much smaller than the actual number of models. As more models are included in an ensemble, the amount of new information diminishes in proportion. Furthermore, this reduction is found to go beyond the problem of ?same center? models and systemic similarities are seen to exist across all models. The results suggest that current methodologies for the interpretation of multimodel ensembles may lead to overly confident climate predictions. | |
