Enhance the meteoblue City Climate Model by Climate Projections to assess Urban Climate Hazard

Urban areas face unprecedented challenges due to the combination of growing cities and climate change, with impacts ranging from heatwaves and extreme precipitation to sea-level rise and urban flooding. Artificial surfaces in cities and the differences in the surface structure cause urban areas to overheat significantly compared to rural areas and lead to a high inner-city air temperature variability. As climate change and heat forcing increase, the urban heat island effect will intensify in the future. Understanding the future climate scenarios is crucial for effective planning of adaptation and mitigation strategies.

Climate projections referred to as Global Climate Models (GCMs) and Regional Climate Models (RCMs) do not accurately represent urban-scale climate variables. Computational constraints and limitations in simulating the complexity of earth system processes limit the spatial resolution of climate projections to the order of 10 – 100 km and the time resolution to a daily basis. Hence, climate models are not able to fully resolve the urban heat island effect and the urban air temperature variability which occur on a micro-scale.

This work highlights the importance of downscaled climate prediction data to capture localized effects and uncertainties associated with urban areas. Downscaling climate models to building-level is done by combining the meteoblue City Climate Model (mCCM) - a dynamic statistical downscaling model - with climate projections in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6).

The mCCM is based on a high-resolution model grid with a horizontal resolution of 10 m. It resolves the differences in the surface energy budget and help understanding the air temperature variability and dynamics in an urban environment. The model is fully driven by surface texture parameters derived from high-resolution satellites and meso-scale NWP models. In this framework, the climate signal of CMIP6 data is added to the hourly air temperature time series of the mCCM. This allows the calculation of temperature-related climate indices on a high-resolution grid of 10 m for future time periods and various SSP scenarios. Furthermore, this approach allows to estimate the probability that certain climate indices such as e.g., number of tropical nights, or number of hot days reach a critical threshold.

Enhancing the mCCM with climate predictions creates a reliable information basis for city planners, decision makers, and companies. It helps cities become more resilient, sustainable, and adaptable in the face of a changing climate, ultimately improving the quality of life for urban residents.