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.

中文翻译：

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气候模式子选择方法的基于流流的评估

气候变化及其影响的评估依赖于可用和/或子选择的模型集合。然而，对模拟气候变化影响有效性的评估并不简单，因为历史数据通常用于偏差调整、选择集合成员或定义比较影响的基线——自然地，没有观察到评估未来的预测。我们假设历史流观察包含有价值的信息来研究模型集合选择的实践。以维也纳的多瑙河为例，EURO-CORDEX 气候模拟驱动 COSERO 水文模型。对于每种选择方法，我们比较了从参考期（1960-1989 年）到评估期（1990-2014 年）的观察到的模拟流量变化。与没有选择的比较表明，在知情的情况下选择合奏成员可以改善气候变化影响的量化。然而，选择方法很重要，仅基于后报气候或流量的模型选择具有误导性，而保持完整集合的多样性和信息内容的方法是有利的。在进行气候影响评估之前，我们建议对长期历史数据进行拆分，并用它来测试气候模型性能、子选择方法以及它们在再现感兴趣指标方面的一致性，这进一步提供了可预期的近和远期影响评估。