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Record W4386956082 · doi:10.1029/2022gb007644

Arctic Permafrost Thawing Enhances Sulfide Oxidation

2023· article· en· W4386956082 on OpenAlexaboutno aff
Preston Kemeny, Gen Li, Madison M. Douglas, William M. Berelson, Austin J. Chadwick, Nathan F. Dalleska, Michael P. Lamb, William Larsen, John S. Magyar, Nick E. Rollins, J. C. Rowland, M. Isabel Smith, Mark A. Torres, Samuel M. Webb, Woodward W. Fischer, A. Joshua West

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNational Defense Science and Engineering GraduateResnick Sustainability Institute for Science, Energy and Sustainability, California Institute of TechnologyCalifornia Institute of TechnologyU.S. Department of EnergyNational Science Foundation
KeywordsPermafrostWeatheringAlkalinityBiogeochemical cycleSulfateEnvironmental chemistryCarbon cycleDissolved organic carbonArcticSulfideCarbon dioxideSedimentTotal organic carbonBiogeochemistryEnvironmental scienceSulfur cycleGeologyOceanographyGeochemistryChemistryGeomorphology

Abstract

fetched live from OpenAlex

Abstract Permafrost degradation is altering biogeochemical processes throughout the Arctic. Thaw‐induced changes in organic matter transformations and mineral weathering reactions are impacting fluxes of inorganic carbon (IC) and alkalinity (ALK) in Arctic rivers. However, the net impact of these changing fluxes on the concentration of carbon dioxide in the atmosphere ( p CO 2 ) is relatively unconstrained. Resolving this uncertainty is important as thaw‐driven changes in the fluxes of IC and ALK could produce feedbacks in the global carbon cycle. Enhanced production of sulfuric acid through sulfide oxidation is particularly poorly quantified despite its potential to remove ALK from the ocean‐atmosphere system and increase p CO 2 , producing a positive feedback leading to more warming and permafrost degradation. In this work, we quantified weathering in the Koyukuk River, a major tributary of the Yukon River draining discontinuous permafrost in central Alaska, based on water and sediment samples collected near the village of Huslia in summer 2018. Using measurements of major ion abundances and sulfate () sulfur ( 34 S/ 32 S) and oxygen ( 18 O/ 16 O) isotope ratios, we employed the MEANDIR inversion model to quantify the relative importance of a suite of weathering processes and their net impact on p CO 2 . Calculations found that approximately 80% of in mainstem samples derived from sulfide oxidation with the remainder from evaporite dissolution. Moreover, 34 S/ 32 S ratios, 13 C/ 12 C ratios of dissolved IC, and sulfur X‐ray absorption spectra of mainstem, secondary channel, and floodplain pore fluid and sediment samples revealed modest degrees of microbial sulfate reduction within the floodplain. Weathering fluxes of ALK and IC result in lower values of p CO 2 over timescales shorter than carbonate compensation (∼10 4 yr) and, for mainstem samples, higher values of p CO 2 over timescales longer than carbonate compensation but shorter than the residence time of marine (∼10 7 yr). Furthermore, the absolute concentrations of and Mg 2+ in the Koyukuk River, as well as the ratios of and Mg 2+ to other dissolved weathering products, have increased over the past 50 years. Through analogy to similar trends in the Yukon River, we interpret these changes as reflecting enhanced sulfide oxidation due to ongoing exposure of previously frozen sediment and changes in the contributions of shallow and deep flow paths to the active channel. Overall, these findings confirm that sulfide oxidation is a substantial outcome of permafrost degradation and that the sulfur cycle responds to permafrost thaw with a timescale‐dependent feedback on warming.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.004

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 head0.030
GPT teacher head0.262
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

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

Citations21
Published2023
Admission routes1
Has abstractyes

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