Urban Climate–Energy Interactions from Global to Local Scales
Advisor: Professor Lei Zhao
Location: CEEB (Hydro) 3019, or Zoom (link)
Abstract
Earth’s climate and energy systems are closely intertwined through complex interactions. Urban energy use both drives and is affected by anthropogenic climate change. Previous studies have predominantly examined the global scale interactions between energy use and future warmer climates. However, the local scale interactions between urban energy use and the urban climates are frequently ignored in future energy projections, due to methodological, scale, and computational challenges. These local interactions can have global scale effects. Ignoring such interactions underestimates climate-driven energy risks on global to local scales, undermining our climate preparedness in energy planning.
This dissertation aims to advance our understanding on the mechanisms and impacts of local urban climate-energy interactions on global-to-local energy demand in a changing climate. I achieve this goal by establishing a hybrid modeling framework integrating process-based modeling and machine learning, and by improving the representation of urban climate-energy interactions in a global Earth system model (ESM).
Using the hybrid modeling framework, I first show the prevalent energy projection methods misrepresent the magnitude, nonlinearity, and uncertainty in the climate-driven projections of future building energy demand due to the missing two-way feedbacks between urban climate and energy. I find a 220% increase (47% decrease) in cooling (heating) energy demand with amplified uncertainty by 2099 under a very high emission scenario, roughly twice that projected by previous methods. Cities’ building energy demand responses to future warming climates are spatially diverse, which necessitates urban energy planning accounting for the unique local climate–energy interactions. This work underscores the critical necessity of explicit and dynamic modeling of urban building energy use for climate-sensitive energy planning.
Next, I improve the building energy parameterization in a global ESM. I establish a new scheme that represents air-conditioning (AC) adoption explicitly through an AC adoption rate parameter, and build a global, present-day, survey-based, and spatially explicit AC adoption rate dataset at country and sub-country level. The new scheme and dataset significantly improve the accuracy of AC energy demand modeling and enable the evaluation of the effects of changing AC adoption on urban energy and climate across scales through global physics-based dynamic modeling. This work represents continued efforts in better representing urban processes and coupled human-urban-Earth dynamics in ESMs.
Finally, I examine the effect of humidity on AC energy demand across global cities under climate change. By modeling building latent heat load as part of AC energy demand in an ESM, I show humidity increases AC energy demand substantially on hot and humid days and may cause unexpected demand spikes on mildly hot days across diverse climate zones. This effect is further exacerbated by climate change. Divergent humidity-driven shifts will occur in cities’ building energy design space as a result of the interplay of local temperature and humidity changes under climate change.
Through development of new model schemes, construction of data product, and hybrid modeling simulations, this dissertation demonstrates the importance of capturing urban climate-energy interactions for comprehensive climate impact assessment, science-based policy-making, and inter-region coordination on climate-sensitive energy planning. This necessitates continued improvement in physical representations of urban energy systems in large-scale models as an important direction of future work.