This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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Abstract
Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modelling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3-7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual mean canopy urban heat island from 0.8°C in 2015 to 0.2°C by 2099. Compared to high-density and mid-density areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. The scheme developed in this study offers potential for non-policy applications, like simulating natural albedo variations across urban surfaces, and enables the inclusion of other urban canopy parameters, such as surface emissivity.
DOI
https://doi.org/10.31223/X5040S
Subjects
Atmospheric Sciences, Civil Engineering, Climate, Computational Engineering, Environmental Engineering, Environmental Sciences
Keywords
urban climate modelling, urban climate adaptation, Climate Engineering, Earth System Models, urban models, albedo
Dates
Published: 2024-04-03 09:30
Last Updated: 2024-04-03 16:30
License
CC BY Attribution 4.0 International
Additional Metadata
Data Availability (Reason not available):
The source code of UrbanDynAlbMod scheme, as well as the scripts and data to reproduce the results and figures, are available at https://github.com/envdes/code_DynamicUrbanAlbedo or DOI: 10.5281/zenodo.10903399.
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