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Record W2092530280 · doi:10.1029/2002gl016549

Dynamical aspects of climate sensitivity

2003· article· en· W2092530280 on OpenAlexaff
G. J. Boer, Bin Yu

Bibliographic record

VenueGeophysical Research Letters · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMixed layerRadiative transferClimatologyClimate sensitivityPositive feedbackEnvironmental scienceForcing (mathematics)Radiative forcingArcticClimate modelNegative feedbackAtmospheric sciencesThe arcticClimate changeOceanographyGeologyPhysics

Abstract

fetched live from OpenAlex

Dynamical aspects of climate feedback/sensitivity are investigated in climate change simulations with a common atmospheric general circulation model coupled to a full ocean model, which responds both dynamically and thermo‐dynamically, and to a mixed‐layer ocean component which responds only thermodynamically. Temperature responses differ with a warmer tropics and an El Niño‐like pattern in the full ocean case but not in the mixed‐layer case. There is also more warming in Arctic and less in Antarctic regions. The geographical patterns of radiative feedback also differ with positive feedback in the tropical Pacific in the full ocean case contrasting with negative feedback in the mixed‐layer case. Positive feedback in the tropical Pacific directly supports an El Niño‐like response to positive (and a La Niña‐like response to negative) radiative forcing. Radiative feedback depends implicitly on dynamical quantities and differences are a consequence of missing oceanic transport pathways in the mixed‐layer ocean.

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)0.001
metaresearch head score (Gemma)0.006
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.305
Teacher spread0.275 · 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

Citations45
Published2003
Admission routes1
Has abstractyes

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