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Record W7105660170 · doi:10.24400/527896/a03-2025.4018

Convection within atmospheric storms organized by ocean submesoscale fronts

2025· article· W7105660170 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2025
Typearticle
Language
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsSemtech (Canada)
Fundersnot available
KeywordsMesoscale meteorologyConvectionDiabaticStormBoundary currentExtratropical cycloneSea surface temperatureCold frontMoistureAtmospheric convection

Abstract

fetched live from OpenAlex

The dynamics of mid-latitude storms are driven by moisture processes, convection, and associated precipitation. Over the past two decades, studies have emphasized the role of western boundary currents in the ocean, such as the Gulf Stream and the Kuroshio Extension, in providing moisture to the atmosphere, thereby intensifying convective activity (e.g., clouds and rain) and storms intensity. While the influence of oceanic mesoscale (~200 km-size) and larger scales on storm tracks is relatively understood, the impact of oceanic submesoscale fronts (~10-20 km-size), characterized by strong sea surface temperature gradients of 5°C per 10 km, remains unknown. Using a global coupled ocean-atmosphere simulation at a km-scale resolution, we show that half of latent heat flux variability at the air-sea interface is driven by oceanic motions at the mesoscale (~40%) and submesoscale (~10-20 km-size, <10%) in the Kuroshio Extension during winter. The analysis further demonstrates that ocean submesoscale fronts drive a secondary circulation, extending above the planetary boundary layer up to 4 km within the troposphere, which enhances diabatic processes and convective precipitations within storms. In the warm sector of storms, ocean submesoscale fronts locally account for half of the total diabatic heating and half of the total precipitations, averaging 14 mm/day over five days. In contrast, diabatic heating and precipitations associated with submesoscale fronts are respectively three and twelve times smaller in the cold sector. As such, ocean submesoscale fronts pump moisture from the ocean to the atmosphere and have the potential to affect storms intensification. Overall, these results suggest that SWOT can identify the influence of ocean fine-scales on weather systems by measuring air-sea exchanges down to the submesoscales. Reference: Vivant, F., Siegelman, L., Klein, P., Torres, H. S., Menemenlis, D., & Molod, A. M. (2025). Ocean submesoscale fronts induce diabatic heating and convective precipitation within storms. Communications Earth & Environment, 6(1), 69. https://doi.org/10.1038/s43247-025-02002-z

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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 designObservational
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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