A PLANETARY-SCALE DATA-MODEL INTEGRATION FRAMEWORK TO ADDRESS GRAND CLIMATE-DRIVEN CHALLENGES IN GLOBAL URBAN ENVIRONMENTS

Climate change coupled with urbanization represents the biggest challenge of our generation. Knowledge about urban areas in a changing climate has become central in understanding the present and future of our living conditions. Many globally recognized climate-driven threats such as heatwaves, flooding, droughts, and energy security are either rooted from or exacerbated by the unique urban environments. Absent measures to ameliorate them, these risks are expected to further intensify due to the rapid urban development coupled with global climate change. The inevitable massive urbanization will expose cities and their residents to substantial risks across the world, but also presents a historic and time-sensitive opportunity to mitigate the negative impacts of climate change and to advance global sustainable growth. However, despite the central role of cities to ensuring Earth’s sustainable future, the science is lagging behind. Robust and globally consistent urban-specific data and climate projections, and an internationally coordinated effort of global urban climate modeling are largely missing. This plan will develop a novel planetary-scale data-model integration framework that combines process-based Earth system modeling and data-driven machine learning to characterize climate-driven risks to global urban environments. Building upon this new framework, Professor Zhao and his team will further initiate an internationally-coordinated effort of global urban climate modeling, comparison, and assessment—Urban Model Intercomparison Project (UrbanMIP)—to provide robust scientific guidance for urban climate mitigation and adaptation decision-making. The proposed plan leverages Zhao’s recent accomplishments and access to high-performance computing as well as recent advances in urban climate theories, data, modeling, and artificial intelligence/machine learning methods. Outcomes of this project will enhance global climate models’ capability and fidelity over urban areas and advance the knowledge frontier in urban climate change as well as the associated impacts and risks.