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

• 成果类型: Article
• 署名作者: Berk, S.; Joshi, M. M.; Goodess, C. M.; Nowack, P.
• 署名单位: University of East Anglia; Helmholtz Association; Karlsruhe Institute of Technology; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.25028731231
• 发表日期: 2026-02-10
• 页码: e2502873123
• 关键词: urban heat island; climate change; Machine Learning; urban climate; URBAN HEAT-ISLAND; MODEL; projections; magnitude; city

摘要

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 degrees 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.