Digital Twin–Driven Climate Resilience Modeling Using Multiscale Earth System Data Integration

Abstract

Climate change has intensified the frequency and severity of extreme environmental events, necessitating advanced predictive and adaptive modeling frameworks for resilience planning. This study introduces a digital twin–driven climate resilience modeling framework that integrates multiscale Earth system data with artificial intelligence to simulate, predict, and optimize responses to climate-induced disruptions. Unlike traditional climate models that operate on static or semi-dynamic datasets, the proposed system constructs a continuously evolving digital twin of regional and global ecosystems, incorporating atmospheric, oceanic, hydrological, and socio-economic variables. The framework employs hybrid deep learning architectures and data assimilation techniques to enhance prediction accuracy and temporal resolution. Multiscale modeling enables the integration of satellite observations, ground-based sensors, and historical climate data, facilitating real-time scenario analysis. Experimental results indicate substantial improvements in predictive reliability, early warning capabilities, and adaptive policy modeling compared to conventional approaches. The system demonstrates the ability to simulate cascading effects of climate events across interconnected systems. This research contributes to the advancement of intelligent climate modeling and provides a scalable platform for decision-makers to design resilient infrastructure and mitigation strategies in the face of accelerating environmental change.