How a city resolving European climate dataset over the past decade enables future urban climate projections

Mesoscale models serve as a critical bridge between urban microclimate dynamics and larger atmospheric processes simulated by earth system models, enabling a deeper understanding of weather and climate phenomena in cities. Long-term regional projections with high spatiotemporal resolution are currently lacking which limits comprehensive research addressing climate change effects in cities. Therefore, a high-resolution European climate dataset was constructed for the 2011-2021 period by downscaling ERA5 reanalysis data to 2.5 km, with an intermediate step at 12.5 km, using the ALARO-1 regional climate model coupled online with the SURFEX land surface model.

 By focusing on city resolving scales, the dataset allows to investigate how extreme weather events are manifested in cities. Next to facilitating detailed investigations into extreme weather events, the dataset serves as a training set to construct a computational cheap machine learning emulator. The emulator, a U-Net model, downscales surface temperature, wind and relative humidity simultaneously from 12.5 km to 2.5 km permitting us to efficiently downscale EURO-CORDEX projections to city-resolving scales. This novel approach enables the creation of an ensemble of urban-scale climate projections, enhancing our ability to evaluate the impact of different climate scenarios on European cities.

Applications include investigating the interplay between heatwaves and the urban heat island effect based on the historical dataset. Furthermore, the climate projections at high spatiotemporal resolution will permit future impact studies on heritage and health. Hence, this research provides valuable insight into urban planning and resilience strategies.