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Record W1978126541 · doi:10.5194/acp-11-3949-2011

Analysis of reactive bromine production and ozone depletion in the Arctic boundary layer using 3-D simulations with GEM-AQ: inference from synoptic-scale patterns

2011· article· en· W1978126541 on OpenAlexafffund
K. Toyota, J. C. McConnell, Alexandru Lupu, Lori Neary, C. A. McLinden, Andreas Richter, R. Kwok, K. Semeniuk, J. W. Kaminski, Sunling Gong, J. Jarosz, Martyn P. Chipperfield, Christopher E. Sioris

Bibliographic record

VenueAtmospheric chemistry and physics · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsYork UniversityEnvironment and Climate Change Canada
FundersNational Centre for Earth ObservationNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaUniversität BremenEuropean CommissionNational Oceanic and Atmospheric AdministrationSight Research UKCanadian Foundation for Climate and Atmospheric SciencesOntario Innovation Trust
KeywordsBromineSea iceSnowpackArcticAtmosphere (unit)OzoneSnowAtmospheric sciencesEnvironmental scienceOzone depletionArctic ice packClimatologyMixing ratioChemistryMeteorologyOceanographyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. Episodes of high bromine levels and surface ozone depletion in the springtime Arctic are simulated by an online air-quality model, GEM-AQ, with gas-phase and heterogeneous reactions of inorganic bromine species and a simple scheme of air-snowpack chemical interactions implemented for this study. Snowpack on sea ice is assumed to be the only source of bromine to the atmosphere and to be capable of converting relatively stable bromine species to photolabile Br2 via air-snowpack interactions. A set of sensitivity model runs are performed for April 2001 at a horizontal resolution of approximately 100 km×100 km in the Arctic, to provide insights into the effects of temperature and the age (first-year, FY, versus multi-year, MY) of sea ice on the release of reactive bromine to the atmosphere. The model simulations capture much of the temporal variations in surface ozone mixing ratios as observed at stations in the high Arctic and the synoptic-scale evolution of areas with enhanced BrO column amount ("BrO clouds") as estimated from satellite observations. The simulated "BrO clouds" are in modestly better agreement with the satellite measurements when the FY sea ice is assumed to be more efficient at releasing reactive bromine to the atmosphere than on the MY sea ice. Surface ozone data from coastal stations used in this study are not sufficient to evaluate unambiguously the difference between the FY sea ice and the MY sea ice as a source of bromine. The results strongly suggest that reactive bromine is released ubiquitously from the snow on the sea ice during the Arctic spring while the timing and location of the bromine release are largely controlled by meteorological factors. It appears that a rapid advection and an enhanced turbulent diffusion associated with strong boundary-layer winds drive transport and dispersion of ozone to the near-surface air over the sea ice, increasing the oxidation rate of bromide (Br−) in the surface snow. Also, if indeed the surface snowpack does supply most of the reactive bromine in the Arctic boundary layer, it appears to be capable of releasing reactive bromine at temperatures as high as −10 °C, particularly on the sea ice in the central and eastern Arctic Ocean. Dynamically-induced BrO column variability in the lowermost stratosphere appears to interfere with the use of satellite BrO column measurements for interpreting BrO variability in the lower troposphere but probably not to the extent of totally obscuring "BrO clouds" that originate from the surface snow/ice source of bromine in the high Arctic. A budget analysis of the simulated air-surface exchange of bromine compounds suggests that a "bromine explosion" occurs in the interstitial air of the snowpack and/or is accelerated by heterogeneous reactions on the surface of wind-blown snow in ambient air, both of which are not represented explicitly in our simple model but could have been approximated by a parameter adjustment for the yield of Br2 from the trigger.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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

Citations93
Published2011
Admission routes2
Has abstractyes

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