Junjie Yu, Yuan Sun , Sarah Lindley, Caroline Jay, David O Topping, Keith W Oleson, Zhonghua Zheng 

The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. Despite its advanced capabilities, CLMU presents significant challenges for users unfamiliar with numerical modeling due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, a toolkit was developed, including (1) an operating system-independent containerized application developed to streamline the execution of CLMU and (2) a Python-based tool (Pyclmuapp) used to interface the containerized CLMU and create urban surface data and atmospheric forcing data for the model. This toolkit enables users to simulate urban climate and explore climate-related variables such as urban building energy consumption, urban water balance, and human thermal stress. It also supports the simulation under future climate conditions and the exploration of urban climate responses to various surface properties, providing a foundation for evaluating urban climate adaptation strategies. Overall, this toolkit makes urban climate modeling more accessible, promoting broader applications from research to practical urban planning and policy-making.