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Record W4408436878 · doi:10.5194/egusphere-egu25-7049

Modelling Arctic springtime ozone depletion events: role of snow sourced bromine chemistry and its impact on Arctic tropospheric ozone budget

2025· preprint· en· W4408436878 on OpenAlexaff
Wanmin Gong, S. R. Beagley, K. Toyota, Henrik Skov, Jesper Heile Christensen, Alexandru Lupu, Diane Pendlebury, Junhua Zhang, Ulaş İm, Yugo Kanaya, Alfonso Saiz‐Lopez, Roberto Sommariva, Peter Effertz, John W. Halfacre, Nis Jepsen, Rigel Kivi, Theodore K. Koenig, Katrin Müller, Claus Nordstrøm, Irina Petropavlovskikh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsOzone depletionSnowOzoneBromineTropospheric ozoneArcticAtmospheric sciencesEnvironmental scienceThe arcticClimatologyTroposphereMeteorologyOceanographyChemistryGeologyGeography

Abstract

fetched live from OpenAlex

A large portion of the Arctic is covered by ocean and sea ice, from which reactive halogen species can be emitted to the atmosphere. Springtime ozone depletion events (ODEs) have been primarily attributed to catalytic destruction of ozone by reactive bromine released from snowpacks and blowing snow over sea ice and cycled through heterogeneous reactions on aerosol surfaces. Mechanisms to represent polar springtime bromine explosions and ODEs have been developed and tested in various atmospheric models, by considering both blowing snow and snowpacks, with varying degrees of success when compared with observations of reactive bromine and ozone in polar regions. In this study, two independent chemical transport models (CTMs), DEHM (Danish Eulerian Hemispheric Model) and GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and Chemistry), were used to simulate Arctic lower tropospheric ozone for the year 2015. Both models include bromine chemistry and a representation of snow-sourced bromine mechanism: a blowing-snow bromine source mechanism in DEHM and a snowpack bromine source mechanism in GEM-MACH.The comparison of model simulation results with available observations in the Arctic showed that the model with the snowpack bromine source mechanism (GEM-MACH) was able to capture most of the observed springtime ODEs in the Arctic, while the model considering blowing-snow sourced bromine alone (DEHM) simulated much fewer ODEs. The snowpack-sourced mechanism is seen to be essential in sustaining the continued bromine production under a variety of meteorological conditions, while the blowing-snow bromine source mechanism triggered by high wind conditions tends to be more episodic. This is consistent with observational evidence that the ODEs observed in the Arctic tend to occur during calm wind conditions favouring the snowpack bromine source mechanism to take effect in the surface air with ODEs at high wind speed conditions to occur sporadically. The study demonstrated that the springtime ozone depletion process plays a central role in driving the surface ozone seasonal cycle in the Central Arctic, and that the bromine-mediated ODEs, while occurring most notably within the lowest few hundred metres of air above the Arctic Ocean, can induce a 5-7% of loss in the total pan-Arctic tropospheric ozone burden during springtime. The study also demonstrated that atmospheric aerosols play an integral role in the Arctic springtime bromine explosions and ODEs through heterogeneous cycling of reactive bromine, particularly over a deeper vertical layer and at distance from the snowpack bromine source area, which has implications for the potential role of Arctic haze aerosols that may play in the springtime ODEs. The uncertainty in parameterising the Arctic bromine source mechanism will also be discussed.

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 score0.162
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.225
Teacher spread0.217 · 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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