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Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions

2006· article· en· W3037342105 on OpenAlexaff
Steve Frolking, Nigel T. Roulet

Bibliographic record

VenueUniversity of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPeatRadiative forcingAtmospheric sciencesEnvironmental scienceHoloceneClimatologyGreenhouse gasMethaneFlux (metallurgy)Radiative transferRadiative fluxGlobal warmingCarbon cycleClimate changeGeologyOceanographyPhysicsChemistryGeographyEcologyEcosystem

Abstract

fetched live from OpenAlex

Throughout the Holocene, northern peatlands have both accumulated carbon and emitted methane. Their impact on climate radiative forcing has been the net of cooling (persistent CO2uptake) and warming (persistent CH4 emission). We evaluated this by developing very simple Holocene peatland carbon flux trajectories, and using these as inputs to a simple atmospheric perturbation model. Flux trajectories are based on estimates of contemporary CH4 flux (15–50 Tg CH4 yr−1), total accumulated peat C (250–450 Pg C), and peatland initiation dates. The contemporary perturbations to the atmosphere due to northern peatlands are an increase of ∼100 ppbv CH4 and a decrease of ∼35 ppmv CO2. The net radiative forcing impact northern peatlands is currently about −0.2 to −0.5 W m−2 (a cooling). It is likely that peatlands initially caused a net warming of up to +0.1 W m−2, but have been causing an increasing net cooling for the past 8000–11 000 years. A series of sensitivity simulations indicate that the current radiative forcing impact is determined primarily by the magnitude of the contemporary methane flux and the magnitude of the total C accumulated as peat, and that radiative forcing dynamics during the Holocene depended on flux trajectory, but the overall pattern was similar in all cases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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