Large uncertainties of LCZ-based urban canopy parameters in urban climate modeling

The Local Climate Zone (LCZ) framework standardizes a common descriptive methodology to classify land surfaces into ten built and seven natural land cover types, each associated with some prescribed ranges of values for a subset of parameters, providing a more detailed alternative to supply spatially explicit urban parameters to regional and global models than conventional binary or density-class based urban typologies. While recent high-resolution LCZ maps have greatly advanced our understanding of urban areas at large scales in a “universal” way, challenges remain in determining accurate urban canopy parameters (UCPs) from these classifications. The common look-up-table approach, which assigns predefined value ranges to each LCZ type regardless of geographic location, oversimplifies the complexity and heterogeneity of urban surfaces within and across nations with different construction policies and building codes. Furthermore, LCZs, by their very nature, describe primarily urban morphologies, which can be frequently decoupled from radiative properties and construction materials, making the model inputs internally inconsistent. This study explores these limitations using the newly developed global 1km spatially continuous urban surface property dataset (U-Surf). U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for urban canopy models at the facet- and canopy-level. Our analysis reveals substantial variabilities and uncertainties in LCZ-derived UCPs across regions and raises questions about the wide adoption of coarse-grained urban representation in urban climate modeling. Through simulations using the Community Earth System Model (CESM), we further discuss the implications of these discrepancies for detecting urban-specific meteorological signals from local to regional scales. Our results highlight the importance of spatially continuous, internally consistent UCPs in high-resolution urban climate modeling.