Streamflow-based evaluation of climate model sub-selection methods
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Streamflow-based evaluation of climate model sub-selection methods Jens Kiesel 1,2 & Philipp Stanzel 3 & Harald Kling 3 Sonja C. Jähnig 1 & Ilias Pechlivanidis 4
& Nicola Fohrer
2
&
Received: 30 December 2019 / Accepted: 30 August 2020/ # The Author(s) 2020
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. Keywords Hindcast . Climate uncertainty . Ensemble selection . EURO-CORDEX . Climate change impact This article is part of a Special Issue on “How evaluation of hydrological models influences results of climate impact assessment,” edited by Valentina Krysanova, Fred Hattermann, and Zbigniew Kundzewicz Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-02002854-8) contains supplementary material, which is available to authorized users.
* Jens Kiesel kiesel@igb–berlin.de Extended author information available on the last page of the article
Climatic Change
1 Introduction 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). Use of a large model ensemble of climate projections is favored for impact modeling with the aim to quantify the inherent uncertainties in the projections (Clark et al.
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