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Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. ; Participation of S. Herrera and J.M. Gutiérrez was partially supported by the project AfriCultuReS (European Union's Horizon 2020 program, grant agreement no, 774652). S. Lange acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 641816 (CRESCENDO). ; Peer reviewed

Intro -- Foreword -- Contents -- Editors and Contributors -- Introductory Overview -- A World of Multi-Dimensional Crises -- The Sustainable Development Goals -- Sustainable Development and Universities in Austria -- The Faculty of Environmental, Regional and Educational Sciences and the Aim of This Edited Volume -- How this Volume was Organized -- A. Sustainability Challenges in Cities, Communities and Regions -- B. Sustainability Learning in Schools and Other Educational Institutions -- C. Actors for Learning and Implementing Sustainability -- References -- Sustainability Challenges in Cities, Communities, and Regions -- < -- InlineMediaObject> -- < -- ImageObject FileRef="520355_1_En_2_Figa_HTML.gif" Format="GIF" Color="BlackWhite" Type="Linedraw" Rendition="HTML"/> -- < -- /InlineMediaObject> -- The Challenge of Providing Information About Regional Climate Change -- Introduction -- The Context of Regional Climate Research -- Foundations and Sources of Climate Information -- Observations -- Limitations of Observational Data -- Role of Observations -- Climate Model Simulations -- How do Climate Models Work? -- Climate Model Experiments -- Projection Uncertainties -- Process Understanding -- Generating and Communicating Relevant Climate Information -- User Collaboration -- Information Construction -- Organisation of Regional Climate Research -- Conclusions -- References -- < -- InlineMediaObject> -- < -- ImageObject FileRef="520355_1_En_3_Figa_HTML.png" Format="PNG" Color="Color" Type="LinedrawHalftone" Rendition="HTML" Resolution="300" Width="1276" Height="86"/> -- < -- /InlineMediaObject> -- Littering in Municipal Public Places: The Role of Pers -- List of Abbreviations -- Introduction -- Method -- Results -- Descriptive Statistics -- Multiple Regression Analyses -- Personal Intention to Avoid Littering.

The volume provides a unique view on multidimensional crises, their interplay, and possible resolutions for sustainable life patterns and is therefore broadly related to the Sustainable Development Goals. Traditional unidimensional and technocratic strategies often fall short. Ultimately, people, their behavior and their habits are at the source of many problems. Therefore, it is imperative to take people, their multifaceted nature and the necessary learning and educational processes into account when striving towards a better life for everyone. The Editors Dr. Sandra Hummel is a Research Associate at the department of Empirical Learningworld Research and Higher Education Didactics at the University of Graz. Dr. Philipp Assinger is an Assistant Professor at the Institute of Educational Science at the University of Graz. Dr. Christian Bauer is Senior Lecturer at the Institute of Geography and Regional Science at the University of Graz. Dr. Thomas Brudermann is an Associate Professor at the Institute of Systems Sciences, Innovation and Sustainability Research at the University of Graz. Dr. Andrea Jany is a Research Associate at the Institute of Geography and Regional Science at the University of Graz. Dr. Martin Jury is a Research Associate at the Wegener Center for Climate and Global Change at the University of Graz. Dr. Romana Rauter is Associate Professor on Sustainability and Innovation Management at the Institute of Systems Sciences, Innovation and Sustainability Research at the University of Graz. Dr. Mireille van Poppel is a Professor at the Institute of Human Movement Science, Sport and Health at the University of Graz.

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. ; We acknowledge the E‐OBS dataset from the EU‐FP6 project UERRA (https://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://eca.knmi.nl). The authors are grateful to the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO‐ESSP). This study contributes to the EURO‐CORDEX pillar on statistical downscaling, which is a follow‐up of the EU COST Action ES1102 VALUE (Validating and Integrating Downscaling Methods for Climate Change Research). Participation of S. Herrera and J.M. Gutiérrez was partially supported by the project AfriCultuReS (European Union's Horizon 2020 program, grant agreement no, 774652). S. Lange acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 641816 (CRESCENDO). The authors are also grateful to three anonymous reviewers who helped to improve the original manuscript. ; Peer Reviewed ; Postprint (published version)

ABSTRACT: Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature-empirical or parametric-, fitted parameters and tren-preservation) for a case study in the Iberian Peninsula. The quantile tren-preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP-ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. ; Participation of S. Herrera and J.M. Gutiérrez was partially supported by the project AfriCultuReS (European Union's Horizon 2020 program, grant agreement no, 774652). S. Lange acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 641816 (CRESCENDO)