Exploring Dynamics of Climate and Atmosphere Employing the Temperature Indices Using Bias-Corrected GCMS and Ensemble Model Approach

Urban areas significantly influence planetary processes by altering heat, moisture and chemical budgets and it plays a pivotal role in modifying planetary processes through their unique interactions with the environment. The reduction in natural vegetation and permeable surfaces limits evapotranspiration and alters the hydrological balance, often leading to increased surface runoff, reduced groundwater recharge and changes in local humidity levels. The current study evaluates the spatial and temporal variation of temperature extremes for the historical period (1951–2014) and the future scenarios of two Shared Socioeconomic Pathways; SSP245 and SSP 585 for the future periods of 2015-2100, divided into two periods; near future (2015-2050) and far future (2051-2100) for the major tributary of The River Godavari; The Wainganga Basin, India. The temperature data for the basin is sourced from five General Circulation Models (GCMs) and an ensemble model derived from them. The ensemble model incorporates climate forecasts and accounts for anticipated space-weather-related atmospheric perturbations, resulting in a more complete knowledge of fluctuations in temperature in the Wainganga River Basin. The temperature variation due to climate change is evaluated using the extreme climate indices influenced by minimum and maximum temperature, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) and Expert Team on Sector-Specific Climate Indices (ET-SCI). These indices provide a standardized framework for assessing the impacts of driving forces of dynamic temperature and atmospheric processes. The findings will showcase the impact of changes in temperature and their effects temporally, and spatially on the sub-basin level also address the change in atmosphere strongly with the type of driver, time, and location. As global urbanization continues, insights from studies like this are crucial for developing and evaluating adaptive strategies. Conclusively, findings can inform policies aimed at climate resilience, drawing parallels with urban climate adaptation efforts.