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Record W3173898751 · doi:10.5194/bg-18-4937-2021

Estimated effect of the permafrost carbon feedback on the zero emissions commitment to climate change

2021· article· en· W3173898751 on OpenAlexafffund
Andrew H. MacDougall

Bibliographic record

VenueBiogeosciences · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsPermafrostGreenhouse gasEnvironmental scienceClimate changeCarbon cycleCarbon fibersEarth system scienceAtmospheric sciencesLagAtmosphere (unit)Global warmingCoupled model intercomparison projectClimate modelClimatologyMeteorologyGeologyPhysicsEcosystemEcologyMathematicsComputer science

Abstract

fetched live from OpenAlex

Zero Emissions Commitment (ZEC), the expected change in global temperature following the cessation of anthropogenic greenhouse gas emissions, has recently been assessed by the Zero Emissions Commitment Model Intercomparison Project (ZECMIP). ZECMIP concluded that the component of ZEC from CO 2 emissions will likely be close to zero in the decades following the cessation of emissions. However, of the 18 Earth system models that participated in ZECMIP only 2 included a representation of the permafrost carbon feedback to climate change. To better assess the potential impact of permafrost carbon decay on ZEC, a series of perturbed parameter experiments are here conducted with an Earth system model of intermediate complexity. The experiment suggests that the permafrost carbon cycle feedback will directly add 0.06 [0.02 to 0.14] ∘ C to the benchmark the ZEC value assesses 50 years after 1000 Pg C of CO 2 has been emitted to the atmosphere. An additional 0.04 [0 to 0.06] ∘ C is likely to been added relative to the benchmark ZEC value from the thaw-lag effect unaccounted for in the ZECMIP experiment design. Overall I assess that the permafrost carbon feedback is unlikely to change the assessment that ZEC is close to zero on decadal timescales; however, the feedback is expected to become more important over the coming centuries.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.061
GPT teacher head0.275
Teacher spread0.214 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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