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Record W2121539051 · doi:10.5194/acp-16-5009-2016

Aerosol optical properties in the southeastern United States in summer – Part 2: Sensitivity of aerosol optical depth to relative humidity and aerosol parameters

2016· article· en· W2121539051 on OpenAlexaff
C. A. Brock, N. L. Wagner, B. E. Anderson, A. J. Beyersdorf, Pedro Campuzano‐Jost, Douglas A. Day, Glenn S. Diskin, T. D. Gordon, J. L. Jiménez, D. A. Lack, J. Liao, M. Z. Markovic, A. M. Middlebrook, A. E. Perring, M. Richardson, Joshua P. Schwarz, André Welti, Luke D. Ziemba, Daniel M. Murphy

Bibliographic record

VenueAtmospheric chemistry and physics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsEnvironment and Climate Change Canada
FundersGeorgia Institute of TechnologyNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsAerosolRelative humidityAtmospheric sciencesEnvironmental scienceRange (aeronautics)HumidityMass concentration (chemistry)Boundary layerMeteorologyChemistryMaterials scienceGeologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract. Aircraft observations of meteorological, trace gas, and aerosol properties were made between May and September 2013 in the southeastern United States (US). Regionally representative aggregate vertical profiles of median and interdecile ranges of the measured parameters were constructed from 37 individual aircraft profiles made in the afternoon when a well-mixed boundary layer with typical fair-weather cumulus was present (Wagner et al., 2015). We use these 0–4 km aggregate profiles and a simple model to calculate the sensitivity of aerosol optical depth (AOD) to changes in dry aerosol mass, relative humidity, mixed-layer height, the central diameter and width of the particle size distribution, hygroscopicity, and dry and wet refractive index, while holding the other parameters constant. The calculated sensitivity is a result of both the intrinsic sensitivity and the observed range of variation in these parameters. These observationally based sensitivity studies indicate that the relationship between AOD and dry aerosol mass in these conditions in the southeastern US can be highly variable and is especially sensitive to relative humidity (RH). For example, calculated AOD ranged from 0.137 to 0.305 as the RH was varied between the 10th and 90th percentile profiles with dry aerosol mass held constant. Calculated AOD was somewhat less sensitive to aerosol hygroscopicity, mean size, and geometric standard deviation, σg. However, some chemistry–climate models prescribe values of σg substantially larger than we or others observe, leading to potential high biases in model-calculated AOD of ∼ 25 %. Finally, AOD was least sensitive to observed variations in dry and wet aerosol refractive index and to changes in the height of the well-mixed surface layer. We expect these findings to be applicable to other moderately polluted and background continental air masses in which an accumulation mode between 0.1–0.5 µm diameter dominates aerosol extinction.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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

Citations61
Published2016
Admission routes1
Has abstractyes

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