meteoblue City Climate Model (mCCM): High-resolution Modeling for Urban Heatwave Management

As urban areas continue to expand, they become increasingly vulnerable to the impacts of anthropogenic climate change, particularly the rising frequency of heatwaves. Variations in surface structures, such as the contrast between vegetated areas and paved surfaces, induce disparities in the surface energy budget due to different heat storage capacities. This leads to pronounced variability in air temperatures within urban environments.

Official WMO ground weather stations are typically located in the peripheries of cities, where air temperatures are generally lower than those in urban centers. Consequently, Numerical Weather Prediction (NWP) models often inadequately capture the urban heat island (UHI) effect, resulting in a systematic underestimation of urban air temperatures. Furthermore, since urban air temperature variability occurs on a micro-scale, current meso-scale NWP models insufficiently represent the small-scale temperature variations within urban areas.

This study focuses on the meteoblue city-climate model (mCCM), a dynamic statistical downscaling model specifically designed to simulate the UHI effect and the small-scale air temperature variability characteristic of urban areas. The mCCM provides critical data for real-time monitoring and urban planning, enabling a comprehensive understanding of urban climate dynamics and addressing the needs of cities and their inhabitants.

The mCCM operates within a high-resolution model domain, featuring a 10-meter horizontal resolution, being fully driven by meso-scale NWP models and surface texture parameters derived from high-resolution satellite data. Micro-scale parameterizations, which depend on prevailing meteorological conditions as initialized by meso-scale NWP models, resulting in an enhanced representation of local urban climate dynamics. In the mCCM framework, the ECMWF reanalysis dataset ERA5 is integrated to retrospectively generate high-resolution time series with hourly temporal resolution, extending back to 1940. The mCCM framework also facilitates the downscaling of Global Climate Models (GCMs) to building scale by incorporating climate projections from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This is accomplished by adding the climate signal from CMIP6 data onto the hourly air temperature reference time series produced by the mCCM.

The computational efficiency of the mCCM allows for the rapid calculation of urban air temperature fields, enabling the modeling of urban areas covering several hundred square kilometers within seconds. Through integration with weather forecast models, the mCCM can also generate direct forecasts up to seven days in advance, with hourly temporal resolution. This capability supports real-time applications such as urban dashboards, providing the public with valuable information for managing urban heat. Additionally, coupling with the ERA5 reanalysis dataset supports long-term climatological analysis of urban heat islands, while integration of GCMs enables the assessment of local impacts and uncertainties associated with future climate change in urban areas.

The data generated by the mCCM provides a robust foundation for city planners, policymakers, and businesses, aiding in the development of more resilient, sustainable, and adaptive urban environments in response to a changing climate, ultimately enhancing the quality of life for urban residents.