Amplified warming in tropical and subtropical cities under 2 °C climate change

Berk, Sarah, Joshi, Manoj, Nowack, Peer and Goodess, Clare (2026) Amplified warming in tropical and subtropical cities under 2 °C climate change. Proceedings of the National Academy of Sciences of the United States of America, 123 (6). ISSN 0027-8424

[thumbnail of Knowledge and Confidence of the Mental Capacity Act (2005) in a sample of Clinical Psychologists in East Anglia] Microsoft Word (OpenXML) (Knowledge and Confidence of the Mental Capacity Act (2005) in a sample of Clinical Psychologists in East Anglia) - Accepted Version
Available under License Creative Commons Attribution No Derivatives.

Download (110kB)

Abstract

Cities are often warmer than rural surroundings due to a phenomenon known as the urban heat island, which can be influenced by various factors, such as regional climate and land surface types. Under climate change, cities face not only the challenge of increasing temperatures in their surrounding hinterland but also the challenge of potential changes in their heat islands. However, even high-resolution global Earth system models (ESMs) with "urban tiles" can only properly resolve the largest urban areas or megacities. Here, we address these limitations by applying a process-based statistical learning model to ESM outputs to provide projections of changes in land surface temperature (LST) for 104 medium-sized cities of population 300 K to 1 M in the subtropics and tropics. Under a 2 °C global warming scenario, annual mean LST in 81% of these cities is projected to increase faster than the surrounding area. In 16% of these cities, mostly in India and China, mean LST is projected to increase by an additional 50-112% above ESM projections of the surrounding area. Our findings underscore the importance of investigating the specific effects of climate change on urban heat exposure.

Item Type: Article
Additional Information: Data, Materials, and Software Availability: Code is publically available on Zenodo (83). All data used in this study are publically available, including city population and location (2), coastal distance (44), water proximity (45), topography (46), landcover (47), LST (48), vegetation index (55, 56), albedo (57, 58) and CMIP6 ESM (68, 69, 71, 72, 74, 75, 77, 78, 80, 81). All other data are included in the manuscript and/or SI Appendix.
Uncontrolled Keywords: climate change,machine learning,urban climate,urban heat island,general,sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/1000
Faculty \ School: Faculty of Science > School of Environmental Sciences
University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups: Faculty of Science > Research Groups > Climatic Research Unit
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research
Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 19 Feb 2026 10:30
Last Modified: 25 Feb 2026 10:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/101979
DOI: 10.1073/pnas.2502873123

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item