Hectometric-scale modelling of Parisian green spaces’ cooling capacity

Heat poses a significant risk to human health in cities due to the urban heat island (UHI) effect. Understanding the diverse impacts of heat across different neighbourhoods is essential, as is recognising the potential of urban vegetation to mitigate temperatures. This study focuses on Paris and its green spaces, where the limited observational data make numerical modelling necessary to estimate the cooling capacity of urban green areas.

The primary aim of this research is to examine the role of parks and urban forests in reducing air temperatures during the summer. Simulations were carried out for 2022 and 2023 using the Meso-NH model, a non-hydrostatic atmospheric research model. Meso-NH is driven by atmospheric boundary conditions from the French convective-scale operational Numerical Weather Prediction model AROME-France. The model is coupled with the land surface model SURFace EXternalised (SURFEX), which incorporates the Town Energy Balance (TEB) model for urban elements, supported by a high-resolution surface database. The simulations use three nested domains with grid resolutions of 1200 m, 300 m (covering the Paris region), and 100 m (over the city of Paris). Model validation of 2-m air temperature was performed using data from the PANAME intensive measurement campaign.

Previous studies have shown, through local observations, that different weather types produce contrasting UHI regimes, leading to varying cooling capacities of parks. This study demonstrates that hectometric-scale modelling successfully replicates these observed patterns. Furthermore, we explore the potential of parks to cool surrounding neighbourhoods under varying turbulent mixing conditions.

This research offers valuable insights into the role of urban green spaces in mitigating the urban heat island effect, providing potential strategies to enhance urban resilience against heatwaves.