Application of LCZ in Mesoscale Meteorological Model Simulations and Climate Projection

This chapter provides an overview of the impacts of climate change on high-density urban areas, emphasizing the critical role of climate models and mesoscale downscaling in faciliating strategies making for climate change adaptation and mitigation, and in designing sustainable cities. It delves into a detailed case study of Local Climate Zones (LCZs) implementation in Hong Kong within a mesoscale model, drawing parallels with findings from other regions. The study meticulously quantifies various sources of uncertainty in climate modeling and underscores how different data preprocessing methods can significantly influence model outcomes and interpretations. A key focus is placed on the effective use of the WUDAPT level-0 dataset, especially when higher-level data (levels 1 and 2) are not available. This chapter highlights the importance of sub-grid averaging, a technique now facilitated by the updated W2W tool, and the use of region-specific look-up tables to minimize uncertainties. These tables can be refined through local expertise or assisted by online 3D mapping resources, which provide partial building data. Further, the chapter explores another case study that employs the regional WRF model for dynamical downscaling, demonstrating the utility of LCZs in climate research. This includes projecting heatwave patterns, offering valuable insights for policymakers in urban planning and climate change mitigation and adaptation strategies. Looking ahead, advancements in machine learning and the growing availability of open data in the near future, which are expected to enhance the precision of LCZ datasets and local look-up table Urban Canopy Parameters (UCPs) values. These developments are expected to reduce uncertainties further and improve the accuracy of climate and regional modeling, thereby offering more reliable tools for addressing the challenges posed by climate change in urban environments.