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The analyses of the three models selected for detailed assessment - MPI-ESM-LR, GFDL-ESM2G and CCSM4 - as well as the selection of the LBC-avail subset presented in this paper use the same temperature and precipitation datasets as the original studies and therefore the selected models are not expected to be different in terms of the quality of the downscaled climate. However, the selection of the models using a regional approach that has the potential to yield benefits across multiple regions introduces the potential for model bias due to climatic forcing differences between regions. It is also important to note that the selection of the models is made by experts (not by automated processes) and is based on well-established criteria rather than the “best available” criteria. We also note that the model evaluation reported in this paper is not exhaustive; although the models selected are representative of the LBC-avail subset, no attempt has been made to include all models in LBC, and there are potentially other good models available outside the LBC-avail subset. Our purpose here is to provide an example of how we might select models for a regional downscaling exercise that can be applied to other regions.
The determination of the number of criteria that should be used to assess climate models in downscaling studies is not a trivial exercise, and there are no clear objective approaches. The choice of criteria (e.g. climate sensitivity, skill in regional downscaling, representation of important climatic features) depends on the scientific question posed and the suitability of the tool used to assess the models. We make a pragmatic choice based on the purpose of our assessment and the fact that no single evaluation tool is likely to be able to provide a comprehensive assessment of all aspects of the models (see also Boyer et al. 2013). While the number of criteria which need to be considered is not trivial, there is potential to increase the scope of the assessment by including additional criteria as the body of information on model performance becomes more readily available, including more data-rich evaluation products such as the GCMs-RCMs assessment of Boyer et al. (2013).
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