Global multi-model projections of local urban climates
Cities are where major human-perceived climate change impacts occur. Many globally recognized climate threats such as heat stress, water scarcity, air pollution, energy shortage, extreme rainfall, and flooding are either rooted from or exacerbated by the unique urban climatology combined with the concentrated population and infrastructure. These hazardous risks are projected to be further worsen due to climate change coupled with rapid urbanization. Effective urban planning and adaptation for climate-driven risks relies on robust climate modeling that are specific to built landscapes with quantitative characterization of uncertainties. Such projections, however, are largely absent because of a near-universal lack of urban representation in global-scale Earth system models. In this seminar, I will present a newly developed urban climate emulator framework that combines process-based Earth system modeling and data-driven Physics-Guided Machine Learning (PGML), and its applications on understanding the local urban climate change, variability, and uncertainty, and climate impacts to built environments at the global scale.
