Assessment of the Paris urban heat island in ERA5 and offline SURFEX-TEB (v8.1) simulations using the METEOSAT land surface temperature product

Abstract

Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections.