Abstract
Climate change is placing increasing pressure on urban areas, threatening millions of lives. In this context, diverse climate models have been utilized to predict future changes in precipitation and temperature. Despite substantial research in this field, there is a lack of synthesis on the analysis of various climate models, their predictive reliability, and their geographical focus. The current systematic review, covering studies from 2009 to 2023, examines the use of different climate models to forecast precipitation and temperature trends in cities. We employed the PRISMA framework for literature search and selection, organizing the studies into two primary categories: the climate models used and their geographical focus. Using deductive content analysis, our analysis found that combined neural networks and (Coupled Model Intercomparison Project Phase 6) CMIP6 models were the most used by researchers. Time series models, particularly those optimized with machine learning, demonstrated superior accuracy compared to other models. Furthermore, scenarios such as SSP5-8.5 and RCP8.5 projected significant changes in precipitation and temperature across cities in Europe, North America, Asia, and the Gulf Cooperation Council (GCC) countries. These changes could intensify the frequency of heatwaves, floods, and droughts in urban areas by the end of the 21st century. A key gap in the existing literature is the insufficient analysis of small cities in predicting temperature and precipitation changes, indicating a promising area for future research. Overall, this study provides valuable insights for policymakers in climate change adaptation and urban resilience planning.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
TEMPERATURE, PRECIPITATION, SSPs and RCPs, CLIMATE CHANGE, CITIES, CLIMATE MODELS
