Uncertainties in hydro-climatic projections are (in part) related to various components of the production chain. An ensemble of numerous projections is usually considered to characterize the overall uncertainty; however in practice a small set of scenario combinations are constructed to provide users with a subset that is manageable for decision-making. Since projections are unavoidably uncertain, and multiple projections are typically informationally redundant to a considerable extent, it would be helpful to identify an informationally representative subset in a large model ensemble. Here a framework rooted in the information theoretic Maximum Information Minimum Redundancy concept is proposed for identifying a representative subset from an available ensemble of hydro-climatic projections. We analyze an ensemble of 16 precipitation and temperature projections for Sweden, and use these as inputs to the HBV hydrological model to project river discharge until the mid of this century. Representative subsets are judged in terms of different statistical properties of three essential climate variables (precipitation, temperature and discharge), whilst we further assess the sensitivity of the optimized subset for different seasons and future periods. Our results indicate that a quarter to a third of the available set of projections can represent more than 80% of the total information of hydro-climatic changes. We find that the representative subsets are sensitive to the regional hydro-climatic characteristics and the choice of variables, seasons and periods of interest. Therefore we recommend that any selection process should not be solely driven by climatic variables but, rather, should also consider variables of the impact model.
Keywords: climate models; impact studies; information theory; maximum information minimum redundancy; representative subset.