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Record W1539293059 · doi:10.1029/2006gb002733

What determines the magnitude of carbon cycle‐climate feedbacks?

2007· article· en· W1539293059 on OpenAlexaff
H. Damon Matthews, Michael Eby, Tracy Ewen, Pierre Friedlingstein, B. J. Hawkins

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of VictoriaConcordia University
Fundersnot available
KeywordsCarbon cycleEnvironmental scienceClimate changeCarbon dioxide in Earth's atmosphereAtmospheric sciencesClimate modelCarbon fibersClimatologyCarbon dioxideRunaway climate changeEcosystemGlobal warmingEffects of global warmingEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Positive feedbacks between climate change and the carbon cycle have the potential to amplify the growth of atmospheric carbon dioxide and accelerate future climate warming. However, both the magnitude of and the processes which drive future carbon cycle‐climate feedbacks remain highly uncertain. In this study, we use a coupled climate‐carbon model to investigate how the response of vegetation photosynthesis to climate change contributes to the overall strength of carbon cycle‐climate feedbacks. We find that the feedback strength is particularly sensitive to the model representation of the photosynthesis‐temperature response, with lesser sensitivity to the parameterization of soil moisture and nitrogen availability. In all simulations, large feedbacks are associated with a climatic suppression of terrestrial primary productivity and consequent reduction of terrestrial carbon uptake. This process is particularly evident in the tropics and can explain a large part of the range of carbon cycle‐climate feedbacks simulated by different coupled climate‐carbon models.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.517

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 studies0.0000.001
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 head0.011
GPT teacher head0.252
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 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

Citations66
Published2007
Admission routes1
Has abstractyes

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