Evidence map›Paper›PMID 35866061›Full record

ArticleGeophysical research letters2022

Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust.

Jun Meng, Yue Huang, Danny M Leung, Longlei Li, Adeyemi A Adebiyi, Claire L Ryder, Natalie M Mahowald, Jasper F Kok

Abstract read
In one paragraph

Article in Geophysical research letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Estimates of submicron particulate matter (PMThe Lancet. Planetary health · 2025
    Article
  3. 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

8 authors.

Jun MengDepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USA.ORCID https://orcid.org/0000-0001-9716-1051
Yue HuangDepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USA.ORCID https://orcid.org/0000-0002-7818-8432
Danny M LeungDepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USA.ORCID https://orcid.org/0000-0002-9879-9978
Longlei LiDepartment of Earth and Atmospheric Sciences Cornell University Ithaca NY USA.ORCID https://orcid.org/0000-0003-2107-8459
Adeyemi A AdebiyiDepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USA.ORCID https://orcid.org/0000-0001-7091-6872
Claire L RyderDepartment of Meteorology University of Reading Reading UK.ORCID https://orcid.org/0000-0002-9892-6113
Natalie M MahowaldDepartment of Earth and Atmospheric Sciences Cornell University Ithaca NY USA.ORCID https://orcid.org/0000-0002-2873-997X
Jasper F KokDepartment of Atmospheric and Oceanic Sciences University of California Los Angeles CA USA.ORCID https://orcid.org/0000-0003-0464-8325

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aircraft measurement campaigns have revealed that super coarse dust (diameter >10 μm) surprisingly accounts for approximately a quarter of aerosols by mass in the atmosphere. However, most global aerosol models either underestimate or do not include super coarse dust abundance. To address this problem, we use brittle fragmentation theory to develop a parameterization for the emitted dust size distribution that includes emission of super coarse dust. We implement this parameterization in the Community Earth System Model (CESM) and find that it brings the model in good agreement with aircraft measurements of super coarse dust close to dust source regions. However, the CESM still underestimates super coarse dust in dust outflow regions. Thus, we conclude that the model underestimation of super coarse atmospheric dust is in part due to the underestimation of super coarse dust emission and likely in part due to errors in deposition processes.

Indexed as

brittle fragmentation theoryCESMdust emissiondust modellingdust particle size distributionsuper coarse dust particle

Identifiers

PMID35866061
PMCPMC9286626

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LicenceCC BY
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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.