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Record W2120321089 · doi:10.1029/2006jd007325

Testing convective parameterizations with tropical measurements of HNO<sub>3</sub>, CO, H<sub>2</sub>O, and O<sub>3</sub>: Implications for the water vapor budget

2006· article· en· W2120321089 on OpenAlexaff
Ian Folkins, P. F. Bernath, C. D. Boone, Leo J. Donner, A. Eldering, Glen Lesins, Randall V. Martin, Björn‐Martin Sinnhuber, Kaley A. Walker

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of WaterlooDalhousie University
Fundersnot available
KeywordsTroposphereRadiosondeAtmospheric sciencesWater vaporConvectionEnvironmental scienceOutflowOzoneRelative humidityAtmosphere (unit)ClimatologyMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

The updraft and downdraft mass flux profiles generated by convective parameterizations differ significantly from each other. Most convective parameterizations are tested against temperature and relative humidity profiles from radiosondes. Chemical tracers provide important additional constraints on the vertical redistribution of mass by convective parameterizations. We compile tropical climatologies of water vapor (H 2 O), ozone (O 3 ), carbon monoxide (CO), and nitric acid (HNO 3 ) from a variety of satellite, aircraft, and balloon‐based measurement platforms. These climatologies are compared with the profiles predicted by a variant of the Emanuel convective parameterization, a two‐column model of the tropical atmosphere, and by the implementations of the Relaxed Arakawa Schubert (RAS) and Zhang and McFarlane (ZM) parameterizations in a three‐dimensional global forecast model. In general, the models with more pronounced convective outflow in the upper troposphere compare more favorably with observations. These models are associated with increased evaporative moistening in the middle and lower 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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.287
Teacher spread0.241 · 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 designBench or experimental
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

Citations67
Published2006
Admission routes1
Has abstractyes

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