Evidence map›Paper›PMID 40771712›Full record

ArticleNPJ clean air2025

Widespread reduction of ozone extremes in storylines of future climate.

Tamara Emmerichs, Domenico Taraborrelli, Fuzhen Shen, Sergey Gromov, Michaela I Hegglin, Andreas Wahner

Abstract read
In one paragraph

Article in NPJ clean air, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

6 authors.

Tamara EmmerichsInstitute of Climate and Energy Systems: Troposphere (ICE-3), Forschungszentrum Jülich GmbH, Jülich, Germany.
Domenico TaraborrelliInstitute of Climate and Energy Systems: Troposphere (ICE-3), Forschungszentrum Jülich GmbH, Jülich, Germany.
Fuzhen ShenInstitute of Climate and Energy Systems: Stratosphere (ICE-4), Forschungszentrum Jülich GmbH, Jülich, Germany.
Sergey GromovMax Planck Institute for Chemistry, Mainz, Germany.
Michaela I HegglinCenter for Advanced Simulation and Analytics (CASA), Forschungszentrum Jülich GmbH, Jülich, Germany.
Andreas WahnerInstitute of Climate and Energy Systems: Troposphere (ICE-3), Forschungszentrum Jülich GmbH, Jülich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High ozone levels harm people and the environment, especially during extreme weather. Climate change is expected to increase the frequency and intensity of these events, exacerbating vegetation-atmosphere interactions. However, current models predict inconsistent responses to warming, potentially due to simplified vegetation representations. We address this uncertainty by incorporating realistic vegetation responses to abiotic stresses into a global atmospheric chemistry model. By constructing storylines of future climate with fixed anthropogenic emissions, we quantify how temperature and humidity changes affect ozone and associated mortality. Here, we show that locally, vegetation and photochemistry often act in concert to amplify ozone pollution extremes, while increased humidity in the free troposphere tends to suppress background ozone levels. The latter effect becomes more dominant with increasing temperatures, leading to a widespread decrease in ozone pollution across the Northern hemisphere. The storyline approach is an effective method for disentangling drivers of air pollution perturbed by climate change.

Indexed as

AbioticAtmospheric chemistryAtmospheric scienceChemistryClimate and Earth system modellingClimate changeClimate-change impacts

Identifiers

PMID40771712
PMCPMC12321569

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.