MétaCan
Menu
← Back to cohort
Record W4289840390 · doi:10.5194/egusphere-2022-710

High interannual surface pCO <sub>2</sub> variability in the Southern Canadian Arctic Archipelago's Kitikmeot Sea

2022· preprint· en· W4289840390 on OpenAlexafffundabout
Richard Sims, Mohamed Ahmed, Brian Butterworth, Patrick J. Duke, Stephen F. Gonski, Samantha F. Jones, Kristina A. Brown, C. J. Mundy, William J. Williams, Brent Else

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of ManitobaFisheries and Oceans CanadaUniversity of VictoriaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationMarine Environmental Observation Prediction and Response NetworkPolar Knowledge CanadaUniversity of Calgary
KeywordsArcticArchipelagoBayOceanographySea iceArctic geoengineeringEnvironmental scienceArctic sea ice declineArctic dipole anomalyClimatologyArctic ice packSink (geography)GeologyGeographyDrift ice

Abstract

fetched live from OpenAlex

Abstract. Warming of the Arctic due to climate change means the Arctic Ocean is now ice-free for longer as sea ice melts earlier and refreezes later. It remains unclear how the extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand how air−sea CO2 fluxes will spatially and temporally vary in a changing Arctic Ocean. It is uncertain whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago. By using a research vessel that is based in the remote Inuit community of Cambridge Bay (Ikaluqtuutiak, Nunavut), we have been able to reliably survey pCO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. We present four years of consecutive summertime pCO2 measurements collected in the Kitikmeot Sea in the southern Canadian Arctic Archipelago. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was -8.3 mmol m-2 d-1) but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of 0.41, -7.70, -21.26 and -2.08 mmol m-2 d-1 during the 2016, 2017, 2018 and 2019 cruises respectively). Surface ocean pCO2 varied by up to 142 μatm between years; this highlights the importance of repeat observations in the Arctic as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the pCO2 value of the surface ocean at the time of ice melt is extremely important in constraining the magnitude of the air−sea flux throughout the ice-free season. Further constraining the flux in the Kitikmeot Sea will require a better understanding of how pCO2 changes outside of the summer season. Surface ocean pCO2 measurements made in the bays and inlets in the Kitikmeot Sea were ~20–40 μatm lower than in the main channels, and pCO2 measurements made close to ice breakup (i.e. within 2 weeks) were 50–100 μatm lower than measurements made >4 weeks after breakup. As basin-wide surveys of the CAA have focused on the deeper shipping channels and rarely measure close to the ice break-up date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the Canadian Arctic Archipelago. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2.

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 score0.023
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
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 head0.006
GPT teacher head0.190
Teacher spread0.183 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

Explore more

Same topicAtmospheric and Environmental Gas Dynamics→French-language works237,207→