Abstract
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.
Highlights
It is a common practice to analyze multiple ensemble members for climate change impact studies, which is important to account for the variability and uncertainty in the projections (Melsen et al 2018; Krysanova et al 2017)
A number of methods have been proposed to deal with the multi-ensemble problem, e.g., selections based on best-performing climate depiction (Ruane and McDermid 2017); best representation of the target variable, e.g., streamflow (Kiesel et al 2019b); keeping model diversity and independence (Abramowitz et al 2019); weighted ensemble members driven by the model performance (Knutti et al 2017); and trading-off the information content, gain, and redundancy in the ensemble set (Pechlivanidis et al 2018)
We argue that historic streamflow observations can be used as an evaluation criterion to assess (1) hindcasted climate model skill and (2) ensemble selection methods
Summary
It is a common practice to analyze multiple ensemble members for climate change impact studies, which is important to account for the variability and uncertainty in the projections (Melsen et al 2018; Krysanova et al 2017). While the practice of sub-selecting large model ensembles has been criticized in the past (Mote et al 2011; Christensen et al 2010), it is a generally accepted approach (Eyring et al 2019; Herger et al 2018; Knutti et al 2017). This is mainly due to the fact that informed model sampling and reduction of informational redundancy in the ensemble are expected to improve climate change impact assessments (Eyring et al 2019; Pechlivanidis et al 2018). A number of methods have been proposed to deal with the multi-ensemble problem, e.g., selections based on best-performing climate depiction (Ruane and McDermid 2017); best representation of the target variable, e.g., streamflow (Kiesel et al 2019b); keeping model diversity and independence (Abramowitz et al 2019); weighted ensemble members driven by the model performance (Knutti et al 2017); and trading-off the information content, gain, and redundancy in the ensemble set (Pechlivanidis et al 2018)
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