Evidence map›Paper›PMID 41061060›Full record

ArticleScience advances2025

Dust devil migration patterns reveal strong near-surface winds across Mars.

Valentin T Bickel, Miguel Almeida, Matthew Read, Antonia Schriever, Daniela Tirsch, Ernst Hauber, Klaus Gwinner, Nicolas Thomas, Thomas Roatsch

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Valentin T BickelCenter for Space and Habitability, University of Bern, Bern, Switzerland.ORCID 0000-0002-7914-2516
Miguel AlmeidaSpace Research and Planetary Sciences, Physics Institute, University of Bern, Bern, Switzerland.ORCID 0000-0003-4632-1830
Matthew ReadSpace Research and Planetary Sciences, Physics Institute, University of Bern, Bern, Switzerland.ORCID 0009-0006-4918-2134
Antonia SchrieverInstitute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany.ORCID 0009-0007-7883-6333
Daniela TirschInstitute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany.ORCID 0000-0001-5905-5426
Ernst HauberInstitute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany.ORCID 0000-0002-1375-304X
Klaus GwinnerInstitute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany.
Nicolas ThomasSpace Research and Planetary Sciences, Physics Institute, University of Bern, Bern, Switzerland.ORCID 0000-0002-0146-0071
Thomas RoatschInstitute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany.ORCID 0000-0001-5712-5005

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dust devil migration is a direct expression of the dynamics of the lowermost martian atmosphere. These dynamics are responsible for dust lifting and atmospheric injection, a vital part of the dust cycle that governs modern Mars' weather and climate. Here, we use deep learning and two decades' worth of orbital images to track the global, diurnal, and seasonal migration patterns of dust devils across Mars, providing a distributed characterization of the dynamics of near-surface winds. Across Mars, derived wind stresses systematically exceed those predicted by global circulation models and frequently surpass the threshold required to initiate particle saltation and the lifting of dust. We identify instances of fast-moving dust devils, indicating strong near-surface winds, that are colocated with large-scale dust lifting events and storms. Our observations show that strong near-surface winds are abundant on Mars and play an important role in atmospheric dust sourcing, directly informing more accurate models of Mars' atmosphere, weather, and climate.

Identifiers

PMID41061060
PMCPMC12506970

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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.