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Evolving patterns of arctic aerosols and the influence of regional variations over two decades

2024· article· en· W4404420182 on OpenAlexaboutno aff
Kwon‐Ho Lee, Kyu‐Tae Lee, Il-Sung Zo, Joon-Bum Jee, Kwanchul Kim, Dasom Lee

Bibliographic record

VenueThe Science of The Total Environment · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNational Research Foundation of Korea
KeywordsArcticPhysical geographyEnvironmental scienceGeographyThe arcticClimatologyOceanographyGeology

Abstract

fetched live from OpenAlex

This study aims to analyze the trends, causes, and future prospects of aerosols in the Arctic region using ground-based observations, satellite data, and reanalysis model data. An analysis of aerosol remote sensing data from AERONET stations in the Arctic from 2000 to 2023 showed a long-term decrease in aerosol optical depth (AOD), aligning with emission regulations in Europe and North America and changes in atmospheric circulation patterns. However, the maximum AOD values observed at AERONET stations in Canada and Russia during the period of 2018-2023 were up to five times higher than the long-term average. This significant increase highlights the potential influence of regional variations and external inputs in Arctic aerosol loading, and emphasizes the need for further investigation into the underlying mechanisms driving these anomalies. Satellite observations confirmed that these highs were associated with regional factors, such as the transport of smoke aerosols from wildfires originating at lower latitudes. Notably, the increase in Arctic aerosols coincided with a decrease in mid-latitude and tropical regions, suggesting the influence of long-range atmospheric transport. From 2000 to 2023, wildfire activity has trended downward in tropical and mid-latitude regions, but upward in the Arctic. However, record wildfire activity in 2019 and 2021 was strongly associated with increased aerosols in the Arctic. This is likely a result of increased temperatures and drier conditions due to climate change, which have intensified the frequency and intensity of wildfires. In fact, mean air temperatures in the summers of 2019 and 2021 were about 5 K above the average of the past 19 years, favorable conditions for wildfires. And changes in barometric pressure and wind direction influenced regional-scale aerosol dispersion characteristics in the Arctic. In conclusion, the recent sudden increase in aerosols in the Arctic was found to be due to wildfire activity and climate change.

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)0.001
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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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