It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes, Figure 1) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. The model ensemble was used to simulate yields of winter and spring wheat at sites in Finland, Germany and Spain. Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9☌) and precipitation (−50 to +50%). Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions.
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