Streamflow-based evaluation of climate model sub-selection methods

The assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies.