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Record W2581187222 · doi:10.1088/1748-9326/aa564a

What determines the warming commitment after cessation of CO <sub>2</sub> emissions?

2017· article· en· W2581187222 on OpenAlexafffund
Dana Ehlert, Kirsten Zickfeld

Bibliographic record

VenueEnvironmental Research Letters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute Canada
KeywordsEnvironmental scienceGlobal warmingAtmospheric sciencesGreenhouse gasCarbon dioxideFraction (chemistry)ThermalClimate changeMeteorologyChemistryEcologyPhysics

Abstract

fetched live from OpenAlex

Previous studies have shown that global mean surface air temperature remains elevated after cessation of CO 2 emissions. However, studies differ in whether the temperature continues to increase, slowly decreases, or remains constant after cessation of emissions. An understanding of this committed warming is of importance because it has implication for the estimation of carbon budgets compatible with temperature targets. Here, we investigate the effect of the state of thermal and bio-geochemical equilibration at the time emissions are set to zero on the committed warming as the latter is determined by the balance of these two equilibration processes. We find that the effect of thermal equilibration, expressed as fraction of realized warming, dominates over the bio-geochemical equilibration, expressed as ratio of the airborne fraction to the equilibrium airborne fraction. This leads to a positive warming commitment, and a commitment that declines the later emissions are zeroed along a trajectory of constant atmospheric CO 2 concentration. We furthermore show that the scenario prior to zeroed emissions has the strongest effect on the warming commitment, compared to the time of zeroed emissions and the time horizon over which the commitment is calculated.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.024
GPT teacher head0.291
Teacher spread0.266 · 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

Citations47
Published2017
Admission routes2
Has abstractyes

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