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Record W2529007538 · doi:10.1111/gcb.13520

The positive net radiative greenhouse gas forcing of increasing methane emissions from a thawing boreal forest‐wetland landscape

2016· article· en· W2529007538 on OpenAlexafffundabout
Manuel Helbig, L. Chasmer, Natascha Kljun, William L. Quinton, Claire C. Treat, Oliver Sonnentag

Bibliographic record

VenueGlobal Change Biology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsWilfrid Laurier UniversityUniversity of LethbridgeUniversité de MontréalCenter for Northern Studies
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaSmithsonian Tropical Research InstituteCanada Research ChairsCanada Foundation for InnovationDeutscher Akademischer AustauschdienstSmithsonian InstitutionNational Science Foundation
KeywordsPermafrostWetlandEnvironmental sciencePeatRadiative forcingGreenhouse gasTaigaEddy covarianceBorealClimate changeAtmospheric sciencesPrimary productionHydrology (agriculture)EcosystemEcologyGeologyGeographyForestryOceanography

Abstract

fetched live from OpenAlex

Abstract At the southern margin of permafrost in North America, climate change causes widespread permafrost thaw. In boreal lowlands, thawing forested permafrost peat plateaus (‘forest’) lead to expansion of permafrost‐free wetlands (‘wetland’). Expanding wetland area with saturated and warmer organic soils is expected to increase landscape methane (CH4) emissions. Here, we quantify the thaw‐induced increase in CH4 emissions for a boreal forest‐wetland landscape in the southern Taiga Plains, Canada, and evaluate its impact on net radiative forcing relative to potential long‐term net carbon dioxide (CO2) exchange. Using nested wetland and landscape eddy covariance net CH4 flux measurements in combination with flux footprint modeling, we find that landscape CH4 emissions increase with increasing wetland‐to‐forest ratio. Landscape CH4 emissions are most sensitive to this ratio during peak emission periods, when wetland soils are up to 10 °C warmer than forest soils. The cumulative growing season (May–October) wetland CH4 emission of ~13 g CH4 m−2 is the dominating contribution to the landscape CH4 emission of ~7 g CH4 m−2. In contrast, forest contributions to landscape CH4 emissions appear to be negligible. The rapid wetland expansion of 0.26 ± 0.05% yr−1 in this region causes an estimated growing season increase of 0.034 ± 0.007 g CH4 m−2 yr−1 in landscape CH4 emissions. A long‐term net CO2 uptake of >200 g CO2 m−2 yr−1 is required to offset the positive radiative forcing of increasing CH4 emissions until the end of the 21st century as indicated by an atmospheric CH4 and CO2 concentration model. However, long‐term apparent carbon accumulation rates in similar boreal forest‐wetland landscapes and eddy covariance landscape net CO2 flux measurements suggest a long‐term net CO2 uptake between 49 and 157 g CO2 m−2 yr−1. Thus, thaw‐induced CH4 emission increases likely exert a positive net radiative greenhouse gas forcing through the 21st century.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.267
Teacher spread0.228 · 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 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

Citations97
Published2016
Admission routes3
Has abstractyes

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