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Record W2102170561 · doi:10.5194/acp-8-5353-2008

A multi-model assessment of pollution transport to the Arctic

2008· article· en· W2102170561 on OpenAlexaff
Drew Shindell, Mian Chin, Frank Dentener, Ruth M. Doherty, G. Faluvegi, Arlene M. Fiore, Peter Hess, D. Koch, Ian A. MacKenzie, Michael Sanderson, Martin G. Schultz, Michael Schulz, David S. Stevenson, H. Teich, C. Textor, Oliver Wild, D. Bergmann, I. Bey, Huisheng Bian, C. Cuvelier, B. N. Duncan, Gerd Folberth, Larry W. Horowitz, Jan Eiof Jonson, J. W. Kaminski, Elina Marmer, R. Park, K. J. Pringle, S. Schroeder, Sophie Szopa, Toshihiko Takemura, Guang Zeng, Terry Keating, A. Zuber

Bibliographic record

VenueAtmospheric chemistry and physics · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsYork University
FundersNatural Environment Research CouncilSight Research UKDepartment for Environment, Food and Rural Affairs, UK GovernmentNational Aeronautics and Space Administration
KeywordsEnvironmental scienceArcticAerosolTroposphereChemical transport modelAtmospheric sciencesDeposition (geology)ClimatologyBorealEast AsiaPollutionAltitude (triangle)PollutantOzoneOceanographyStructural basinMeteorologyGeographyGeologyChemistryEcology

Abstract

fetched live from OpenAlex

Abstract. We examine the response of Arctic gas and aerosol concentrations to perturbations in pollutant emissions from Europe, East and South Asia, and North America using results from a coordinated model intercomparison. These sensitivities to regional emissions (mixing ratio change per unit emission) vary widely across models and species. Intermodel differences are systematic, however, so that the relative importance of different regions is robust. North America contributes the most to Arctic ozone pollution. For aerosols and CO, European emissions dominate at the Arctic surface but East Asian emissions become progressively more important with altitude, and are dominant in the upper troposphere. Sensitivities show strong seasonality: surface sensitivities typically maximize during boreal winter for European and during spring for East Asian and North American emissions. Mid-tropospheric sensitivities, however, nearly always maximize during spring or summer for all regions. Deposition of black carbon (BC) onto Greenland is most sensitive to North American emissions. North America and Europe each contribute ~40% of total BC deposition to Greenland, with ~20% from East Asia. Elsewhere in the Arctic, both sensitivity and total BC deposition are dominated by European emissions. Model diversity for aerosols is especially large, resulting primarily from differences in aerosol physical and chemical processing (including removal). Comparison of modeled aerosol concentrations with observations indicates problems in the models, and perhaps, interpretation of the measurements. For gas phase pollutants such as CO and O3, which are relatively well-simulated, the processes contributing most to uncertainties depend on the source region and altitude examined. Uncertainties in the Arctic surface CO response to emissions perturbations are dominated by emissions for East Asian sources, while uncertainties in transport, emissions, and oxidation are comparable for European and North American sources. At higher levels, model-to-model variations in transport and oxidation are most important. Differences in photochemistry appear to play the largest role in the intermodel variations in Arctic ozone sensitivity, though transport also contributes substantially in the mid-troposphere.

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)0.002
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 score0.145
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
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)0.0010.000

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 head0.016
GPT teacher head0.227
Teacher spread0.212 · 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

Citations504
Published2008
Admission routes1
Has abstractyes

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