Evidence map›Paper›PMID 41632847›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

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

S Berk, M M Joshi, C M Goodess, P Nowack

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

S BerkClimatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ, United Kingdom.ORCID 0000-0002-3958-3827
M M JoshiClimatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ, United Kingdom.ORCID 0000-0002-2948-2811
C M GoodessClimatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ, United Kingdom.
P NowackInstitute of Theoretical Informatics, Institute of Meteorology and Climate Research Atmospheric Trace Gases and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany.ORCID 0000-0003-4588-7832

Funding

UKRI | Natural Environment Research Council (NERC) NE/S007334/1
6 · The paper itself

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.

Indexed as

climate changemachine learningurban climateurban heat island

Identifiers

PMID41632847
PMCPMC12890902

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.