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Record W2128681823 · doi:10.5194/acp-12-10725-2012

Uncertainty associated with convective wet removal of entrained aerosols in a global climate model

2012· article· en· W2128681823 on OpenAlexafffund
Betty Croft, Jeffrey R. Pierce, Randall V. Martin, Corinna Hoose, Ulrike Lohmann

Bibliographic record

VenueAtmospheric chemistry and physics · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsAerosolScavengingAtmospheric sciencesConvectionEnvironmental scienceDeposition (geology)TroposphereMeteorologyChemistryClimatologyGeologyPhysics

Abstract

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Abstract. The uncertainties associated with the wet removal of aerosols entrained above convective cloud bases are investigated in a global aerosol-climate model (ECHAM5-HAM) under a set of limiting assumptions for the wet removal of the entrained aerosols. The limiting assumptions for the wet removal of entrained aerosols are negligible scavenging and vigorous scavenging (either through activation, with size-dependent impaction scavenging, or with the prescribed fractions of the standard model). To facilitate this process-based study, an explicit representation of cloud-droplet-borne and ice-crystal-borne aerosol mass and number, for the purpose of wet removal, is introduced into the ECHAM5-HAM model. This replaces and is compared with the prescribed cloud-droplet-borne and ice-crystal-borne aerosol fraction scavenging scheme of the standard model. A 20% to 35% uncertainty in simulated global, annual mean aerosol mass burdens and optical depth (AOD) is attributed to different assumptions for the wet removal of aerosols entrained above convective cloud bases. Assumptions about the removal of aerosols entrained above convective cloud bases control modeled upper tropospheric aerosol concentrations by as much as one order of magnitude. Simulated aerosols entrained above convective cloud bases contribute 20% to 50% of modeled global, annual mean aerosol mass convective wet deposition (about 5% to 10% of the total dry and wet deposition), depending on the aerosol species, when including wet scavenging of those entrained aerosols (either by activation, size-dependent impaction, or with the prescribed fraction scheme). Among the simulations, the prescribed fraction and size-dependent impaction schemes yield the largest global, annual mean aerosol mass convective wet deposition (by about two-fold). However, the prescribed fraction scheme has more vigorous convective mixed-phase wet removal (by two to five-fold relative to the size-dependent impaction scheme) since nearly all entrained accumulation and coarse mode aerosols are assumed to be cloud-droplet borne or ice-crystal borne, and evaporation due to the Bergeron-Findeisen process is neglected. The simulated convective wet scavenging of entrained accumulation and coarse mode aerosols has feedbacks on new particle formation and the number of Aitken mode aerosols, which control stratiform and convective cloud droplet number concentrations and yield precipitation changes in the ECHAM5-HAM model. However, the geographic distribution of aerosol annual mean convective wet deposition change in the model is driven by changes to the assumptions regarding the scavenging of aerosols entrained above cloud bases rather than by precipitation changes, except for sea salt deposition in the tropics. Uncertainty in the seasonal, regional cycles of AOD due to assumptions about entrained aerosol wet scavenging is similar in magnitude to the estimated error in the AOD retrievals. The uncertainty in aerosol concentrations, burdens, and AOD attributed to different assumptions for the wet scavenging of aerosols entrained above convective cloud bases in a global model motivates the ongoing need to better understand and model the activation and impaction processes that aerosols undergo after entrainment into convective updrafts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.678

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)0.0000.000

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 head0.006
GPT teacher head0.209
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 teacher head, 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

Citations62
Published2012
Admission routes2
Has abstractyes

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