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Record W2618855805 · doi:10.5194/bg-15-847-2018

Explaining CO <sub>2</sub> fluctuations observed in snowpacks

2018· article· en· W2618855805 on OpenAlexafffundabout
L. M. Graham, David Risk

Bibliographic record

VenueBiogeosciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaSt. Francis Xavier University
KeywordsSnowpackSnowAdvectionEnvironmental scienceAtmospheric sciencesWind speedDiurnal cycleDiffusionCarbon dioxideClimatologyChemistryMeteorologyGeologyOceanographyGeography

Abstract

fetched live from OpenAlex

Winter soil carbon dioxide (CO 2 ) respiration is a significant and understudied component of the global carbon (C) cycle. Winter soil CO 2 fluxes can be surprisingly variable, owing to physical factors such as snowpack properties and wind. This study aimed to quantify the effects of advective transport of CO 2 in soil–snow systems on the subdiurnal to diurnal (hours to days) timescale, use an enhanced diffusion model to replicate the effects of CO 2 concentration depletions from persistent winds, and use a model–measure pairing to effectively explore what is happening in the field. We took continuous measurements of CO 2 concentration gradients and meteorological data at a site in the Cape Breton Highlands of Nova Scotia, Canada, to determine the relationship between wind speeds and CO 2 levels in snowpacks. We adapted a soil CO 2 diffusion model for the soil–snow system and simulated stepwise changes in transport rate over a broad range of plausible synthetic cases. The goal was to mimic the changes we observed in CO 2 snowpack concentration to help elucidate the mechanisms (diffusion, advection) responsible for observed variations. On subdiurnal to diurnal timescales with varying winds and constant snow levels, a strong negative relationship between wind speed and CO 2 concentration within the snowpack was often identified. Modelling clearly demonstrated that diffusion alone was unable to replicate the high-frequency CO 2 fluctuations, but simulations using above-atmospheric snowpack diffusivities (simulating advective transport within the snowpack) reproduced snow CO 2 changes of the observed magnitude and speed. This confirmed that wind-induced ventilation contributed to episodic pulsed emissions from the snow surface and to suppressed snowpack concentrations. This study improves our understanding of winter CO 2 dynamics to aid in continued quantification of the annual global C cycle and demonstrates a preference for continuous wintertime CO 2 flux measurement systems.

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)0.001
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 score0.351
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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

Citations14
Published2018
Admission routes3
Has abstractyes

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