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Record W4416965570 · doi:10.1029/2025gl120749

Tropical Cyclones Drive Enhanced Inorganic Iodine in the Mid‐Latitude Upper Troposphere

2025· article· en· W4416965570 on OpenAlexaff
Karolin Voss, Bärbel Vogel, Thorsten Diederich, Andreas Engel, Jens‐Uwe Grooß, Timo Keber, Flora Kluge, Meike Rotermund, Tanja Schuck, Benjamin Weyland, A. Butz, Klaus Pfeilsticker

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersEuropean Centre for Medium-Range Weather ForecastsDeutsche Forschungsgemeinschaft
KeywordsTroposphereIodineStratosphereAtmosphere (unit)HalogenTropical cyclone

Abstract

fetched live from OpenAlex

Abstract Halogens deplete tropospheric and stratospheric ozone, but the role of iodine is still elusive. Atmospheric iodine mainly originates from marine inorganic (I 2 , HOI) and organic (CH 3 I, CH 2 I 2 , CH 2 IBr, and CH 2 ICl) emissions. We report on airborne measurements of atmospheric iodine oxide (IO) concentrations up to 15 km altitude from two flights of the WISE campaign over the mid‐Atlantic in September and October 2017. IO is retrieved from limb scattered skylight in the upper troposphere (UT) using the airborne mini‐DOAS instrument on board the German High Altitude and LOng range research aircraft (HALO). Elevated IO (maximum , mean over ) was observed in the UT in air masses processed by category 5 hurricanes Irma and Maria. Our findings show that enhanced IO mixing ratios are driven by fast vertical transport through tropical cyclones and potentially enhanced marine iodine emissions due to associated high surface winds.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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

Explore more

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