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Record W2019090568 · doi:10.1029/2009gl038462

Coupled chemistry climate model simulations of stratospheric temperatures and their trends for the recent past

2009· article· en· W2019090568 on OpenAlexaff
J. Austin, R. J. Wilson, Hideharu Akiyoshi, Slimane Bekki, Neal Butchart, Chantal Claud, V. I. Fomichev, Piers Forster, Rolando R. García, Nathan P. Gillett, Philippe Keckhut, Ulrike Langematz, Elisa Manzini, Tatsuya Nagashima, William J. Randel, Eugene Rozanov, Kiyotaka Shibata, Keith P. Shine, H. Struthers, David W. J. Thompson, Fei Wu, Shigeo Yoden

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of VictoriaYork University
FundersEconomic and Social Research CouncilEuropean Centre for Medium-Range Weather ForecastsNatural Environment Research CouncilSight Research UK
KeywordsRadiosondeStratosphereClimatologyEnvironmental scienceTroposphereAtmospheric sciencesClimate modelVolcanoRange (aeronautics)MeteorologyClimate changeGeologyGeography

Abstract

fetched live from OpenAlex

Temperature results from multi‐decadal simulations of coupled chemistry climate models for the recent past are analyzed using multi‐linear regression including a trend, solar cycle, lower stratospheric tropical wind, and volcanic aerosol terms. The climatology of the models for recent years is in good agreement with observations for the troposphere but the model results diverge from each other and from observations in the stratosphere. Overall, the models agree better with observations than in previous assessments, primarily because of corrections in the observed temperatures. The annually averaged global and polar temperature trends simulated by the models are generally in agreement with revised satellite observations and radiosonde data over much of their altitude range. In the global average, the model trends underpredict the radiosonde data slightly at the top of the observed range. Over the Antarctic some models underpredict the temperature trend in the lower stratosphere, while others overpredict the trends.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.269

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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 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

Citations32
Published2009
Admission routes1
Has abstractyes

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