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Record W2522616261 · doi:10.5194/acp-17-3637-2017

Contribution of feldspar and marine organic aerosols to global ice nucleating particle concentrations

2017· article· en· W2522616261 on OpenAlexaff
Jesús Vergara‐Temprado, Benjamin J. Murray, T. W. Wilson, Daniel O’Sullivan, Jo Browse, K. J. Pringle, Karin Ardon‐Dryer, Allan K. Bertram, Susannah M. Burrows, Darius Čeburnis, Paul J. DeMott, Ryan H. Mason, Colin O’Dowd, Matteo Rinaldi, K. S. Carslaw

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
FundersBiological and Environmental ResearchEuropean CommissionSight Research UKSeventh Framework ProgrammeNatural Environment Research CouncilU.S. Department of EnergyOffice of ScienceEuropean Research CouncilNational Science Foundation
KeywordsIce nucleusAerosolEnvironmental scienceAtmospheric sciencesSea iceAtmosphere (unit)Ice crystalsFlux (metallurgy)Particle (ecology)NucleationClimatologyOceanographyChemistryMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. Ice-nucleating particles (INPs) are known to affect the amount of ice in mixed-phase clouds, thereby influencing many of their properties. The atmospheric INP concentration changes by orders of magnitude from terrestrial to marine environments, which typically contain much lower concentrations. Many modelling studies use parameterizations for heterogeneous ice nucleation and cloud ice processes that do not account for this difference because they were developed based on INP measurements made predominantly in terrestrial environments without considering the aerosol composition. Errors in the assumed INP concentration will influence the simulated amount of ice in mixed-phase clouds, leading to errors in top-of-atmosphere radiative flux and ultimately the climate sensitivity of the model. Here we develop a global model of INP concentrations relevant for mixed-phase clouds based on laboratory and field measurements of ice nucleation by K-feldspar (an ice-active component of desert dust) and marine organic aerosols (from sea spray). The simulated global distribution of INP concentrations based on these two species agrees much better with currently available ambient measurements than when INP concentrations are assumed to depend only on temperature or particle size. Underestimation of INP concentrations in some terrestrial locations may be due to the neglect of INPs from other terrestrial sources. Our model indicates that, on a monthly average basis, desert dusts dominate the contribution to the INP population over much of the world, but marine organics become increasingly important over remote oceans and they dominate over the Southern Ocean. However, day-to-day variability is important. Because desert dust aerosol tends to be sporadic, marine organic aerosols dominate the INP population on many days per month over much of the mid- and high-latitude Northern Hemisphere. This study advances our understanding of which aerosol species need to be included in order to adequately describe the global and regional distribution of INPs in models, which will guide ice nucleation researchers on where to focus future laboratory and field work.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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 teacher head, not a consensus.

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

Citations276
Published2017
Admission routes1
Has abstractyes

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