Vol.:(0123456789) 1 3 Climate Dynamics https://doi.org/10.1007/s00382-018-4210-7 Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices Seung Beom Seo 1  · Young‑Oh Kim 2  · Youngil Kim 2  · Hyung‑Il Eum 3 Received: 7 September 2017 / Accepted: 10 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset refects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis–Kuo–Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few defcient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak fow and 7-day low fow) in some regional case studies. However, the application of the KKZ algorithm with a diferent set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selec- tion of a subset of GCM models. Keywords Climate change scenarios · Scenario selection · Global circulation model · Climate extremes indices · Katsavounidis–Kuo–Zhang algorithm 1 Introduction In climate change impact studies, an ensemble of global cir- culation model (GCM) outputs is often employed to capture a plausible range of future climate conditions. Although, it is generally considered desirable to employ as many GCMs as possible to quantify the inter-model variability, this task is complicated due to the large computational costs involved. Further, decision makers and stakeholders have difculty in formulating a decision while considering a large number of climate change scenarios. In this regard, ideally, a sub- set of climate change scenarios, which encompasses the maximum possible number of the entire range of simulated future changes, is selected (Cannon 2015). A variety of stud- ies have therefore considered the optimum way to select a subset of GCM outputs for climate change impact studies (e.g., Dubrovsky et al. 2015; Lee and Kim 2012; Knutti et al. 2013; McSweeney et al. 2012; Masson and Knutti 2011; Mendlik and Gobiet 2016; Wilcke and Bärring 2016). A subset of GCMs should accomplish the following: (1) favor models that accurately reconstruct patterns of historical climate (e.g., seasonality and annual cycles), thus enhancing the plausibility of results for future simu- lations in the areas of interest and (2) incorporate a range of possible future climate conditions in terms of the key variables related to the climate impact under investigation (Vano et al. 2015). While the former focuses on GCM per- formance in the reproduction of historical climate patterns, the latter focuses on the ability of a subset to capture the * Seung Beom Seo sbseo7@snu.ac.kr 1 Institute of Engineering Research, Seoul National University, Seoul, South Korea 2 Department of Civil and Environmental Engineering, Seoul National University, Seoul, South Korea 3 Alberta Environment and Parks (AEP), Environmental Monitoring and Science Division, Calgary, AB, Canada