Scaling intra-urban climate fluctuations
- 1Laboratory of Urban and Environmental Systems, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Abstract
Urban-induced microclimate variations, such as urban heat islands and air pollution, scale with city size, producing distinctive relations between average climate variables and city-scale quantities (for example, total population). However, these relations are sensitive to city boundary definitions and overlook intra-urban variability. Here we overcome these limitations by using high-resolution data of urban temperatures, air quality, population and street networks from 142 cities worldwide, showing that their marginal and joint probability distributions collapse onto a set of general functions inspired by finite-size scaling in statistical physics. Through a logarithmic relation linking urban spatial features to climate variables, we find that average street network properties are sufficient to characterize the full variability of temperature and air pollution fields within and across cities. These findings show that intra-urban climate variability follows general scaling functions, enabling the integration of climate information into reduced-complexity models of urban systems to better inform future urban planning.
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BibTeX
@article{duransala2026scaling,
title = {{Scaling intra-urban climate fluctuations}},
author = {Marc Duran-Sala and Martin Hendrick and Gabriele Manoli},
journal = {Nature Cities},
year = {2026},
month = may,
doi = {10.1038/s44284-026-00441-z},
url = {https://doi.org/10.1038/s44284-026-00441-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - Scaling intra-urban climate fluctuations
AU - Marc Duran-Sala
AU - Martin Hendrick
AU - Gabriele Manoli
PY - 2026
DA - 2026/05/19
JO - Nature Cities
DO - 10.1038/s44284-026-00441-z
UR - https://doi.org/10.1038/s44284-026-00441-z
AB - Urban-induced microclimate variations, such as urban heat islands and air pollution, scale with city size, producing distinctive relations between average climate variables and city-scale quantities (for example, total population). However, these relations are sensitive to city boundary definitions and overlook intra-urban variability. Here we overcome these limitations by using high-resolution data of urban temperatures, air quality, population and street networks from 142 cities worldwide, showing that their marginal and joint probability distributions collapse onto a set of general functions inspired by finite-size scaling in statistical physics. Through a logarithmic relation linking urban spatial features to climate variables, we find that average street network properties are sufficient to characterize the full variability of temperature and air pollution fields within and across cities. These findings show that intra-urban climate variability follows general scaling functions, enabling the integration of climate information into reduced-complexity models of urban systems to better inform future urban planning.
SN - 2731-9997
ER -