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Record W1969164087 · doi:10.1088/1748-9326/10/3/031001

The time lag between a carbon dioxide emission and maximum warming increases with the size of the emission

2015· article· en· W1969164087 on OpenAlexafffund
Kirsten Zickfeld, Tyler Herrington

Bibliographic record

VenueEnvironmental Research Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsDouglas CollegeSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsCarbon dioxideEnvironmental scienceGlobal warmingAtmospheric sciencesLagCarbon fibersEmission intensityCarbon dioxide in Earth's atmosphereClimate changeAtmosphere (unit)ClimatologyGreenhouse gasCarbon cycleMaximum temperatureMeteorologyMaterials scienceChemistryPhysicsOceanographyGeologyEcologyEcosystem

Abstract

fetched live from OpenAlex

Abstract In a recent letter, Ricke and Caldeira (2014 Environ. Res. Lett. 9 124002 ) estimated that the timing between an emission and the maximum temperature response is a decade on average. In their analysis, they took into account uncertainties about the carbon cycle, the rate of ocean heat uptake and the climate sensitivity but did not consider one important uncertainty: the size of the emission. Using simulations with an Earth System Model we show that the time lag between a carbon dioxide (CO 2 ) emission pulse and the maximum warming increases for larger pulses. Our results suggest that as CO 2 accumulates in the atmosphere, the full warming effect of an emission may not be felt for several decades, if not centuries. Most of the warming, however, will emerge relatively quickly, implying that CO 2 emission cuts will not only benefit subsequent generations but also the generation implementing those cuts.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
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 communication0.0000.000
Open science0.0010.001
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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Citations74
Published2015
Admission routes2
Has abstractyes

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