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Record W4412122081 · doi:10.5194/epsc-dps2025-1681

Improved MarsWRF modeling of Martian dust storms.

2025· preprint· en· W4412122081 on OpenAlexaff
Claire Newman, Mark G. Richardson, Yuan Lian, Christopher Lee

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMartianAstrobiologyStormDust stormEnvironmental scienceAtmospheric sciencesAtmospheric dustMeteorologyGeologyMars Exploration ProgramPhysicsAerosol

Abstract

fetched live from OpenAlex

On Mars, dust storms are by far the largest contributor to interannual variability in climate and variations in sol-to-sol weather. The thin atmosphere and very small greenhouse effect mean that the impact of adding large atmospheric dust amounts on radiative transfer, and hence on temperature and circulation, is massive. In addition, the lack of water or vegetation on Mars mean that the bulk of the surface is a dust source, and that relatively little ‘scavenging’ (dust acting as cloud condensation nuclei) occurs to remove dust once it has been lifted by vortices or non-vortical winds.Unfortunately, accurately predicting dust storms on Mars has proven to be a very challenging problem. Atmospheric general circulation models (GCMs) must not only produce the large range of variability in dust storm onset times, locations, and sizes observed on Mars itself, but for true predictive skill must also be capable of producing the same type and timing of storm as was observed in any given year. If interannual variability in Mars dust storms is driven primarily by year-to-year variability in atmospheric conditions at the surface, then data assimilation and a denser set of atmospheric observations may ultimately be needed to correctly simulate those conditions and hence to produce the correct pattern of dust lifting and storms. However, if certain storms rely on surface dust being available in key source regions, then potentially an orbital assessment of this (e.g., via albedo changes) might be enough to determine whether such storms could occur in a given year (Newman and Richardson, 2015). A postulated weak coupling between the orbit and rotation of Mars, if sufficiently strong, would produce accelerations that modify surface wind patterns with a period and variation that is not linked to the annual cycle of solar forcing, hence may also provide a preference for certain storms to occur in any given year (Shirley and Mischna, 2017; Newman et al., 2019).All of this provides a strong motivation for investigating whether surface dust availability and/or orbit-spin coupling can improve Mars GCM modelling of dust storms, both in terms of the realism of individual storms and of the simulated interannual variability. Here we will present new results, obtained using the MarsWRF GCM, that expand on our previous investigations of these potential mechanisms.We find that allowing a limited dust supply to be self-consistently rearranged by the circulation, which both changes the position and increases the number of primary source locations for surface dust lifting, remains of key importance for generating more realistic dust storms and interannual variability. By exploring variable threshold formulations (in which the wind stress dust lifting threshold is increased when the surface dust cover drops below its original value), we find that these simulations are occasionally able to generate global dust storms beginning around southern spring equinox, which have been observed twice in recent Mars year (MY 25 and 34, 2001 and 2018) but have proven very difficult to generate spontaneously in models. We also simulate one late global storm, beginning after Ls 300°, which have been observed twice on Mars but have similarly been very hard to generate spontaneously in models. We will discuss the mechanisms at work behind the generation of each storm type in the MarsWRF model.Finally, we will discuss the impact on these limited-surface-dust simulations of including an active water cycle with dust-water microphysics (e.g., Lee et al., 2018), and whether introducing a limited dust supply into simulations with orbit-spin coupling activated improves the match to the observed time-series of dust storms over Martian history.Lee, C., M.I. Richardson, C.E. Newman and M.A. Mischna, The sensitivity of solsticial pauses to atmospheric ice and dust in the MarsWRF General Circulation Model, Icarus, 311, 23-34, doi:10.1016/j.icarus.2018.03.019, 2018.Newman, C.E. and M.I. Richardson, The impact of surface dust source exhaustion on the Martian dust cycle, dust storms and interannual variability, as simulated by the MarsWRF General Circulation Model, Icarus, 257, 47-87, doi:10.1016/j.icarus.2015.03.030, 2015.Newman, C.E., M.A. Mischna, M.I. Richardson and J.H. Shirley, Impact on Mars dust storm variability of postulated orbit-spin coupling with parameterized dust lifting and radiatively active dust transport, Icarus, doi:10.1016/j.icarus.2018.07.023, 317, 649-668, 2019.Shirley, J.H., Mischna, M.A., 2017. Orbit-spin coupling and the interannual variability of global-scale dust storm occurrence on Mars. Planet. Space Sci. 139, 37–50. https://doi.org/10.1016/j.pss.2017.01.001.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.237
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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