An integrated approach for characterizing and selecting climate change scenarios based on variability and extremeness

This study presents a novel integrated approach for selecting optimal combinations of Global Climate Models (GCMs) and Shared Socioeconomic Pathways (SSPs) to assess the impact of climate change on the aquatic environment. The method proposed in this study considers the comprehensive spatial and temporal ranges of climate projections, specifically focusing on the variability and extremeness of climate change across all accessible regions and timescales. This approach uses entropy and frequency analyses to integrate multiple climate indices related to precipitation and air temperature into a single metric representing the unique variability and extreme characteristics of each scenario. In this study, 35 GCM-SSP combinations were analyzed, yielding the following major findings. While variability and extremeness in climate scenarios tended to increase under severe global warming scenarios, this trend was not always consistent. These findings suggest that the general insights into GCMs and SSPs should be broadened. Suitable GCM-SSP combinations were selected by ranking unique characteristics using the Katsavounidis-Kuo-Zhang algorithm, enabling the capture of the full range of GCM-SSP combinations with a minimal number of combinations. Although precipitation and air temperature were the primary focus, the method can be expanded to include other weather variables, such as wind speed and solar radiation. The results demonstrate that this integrated approach effectively represents a wide range of climate scenarios, providing a comprehensive understanding of the projected climates across different regions and timescales. By transforming high-dimensional data into a single dimension, this approach simplifies interpretation, supporting a more effective identification of GCM-SSP combinations suitable for diverse climate adaptation strategies.