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Record W4380998042 · doi:10.5194/os-19-837-2023

High interannual surface <i>p</i> CO <sub>2</sub> variability in the southern Canadian Arctic Archipelago's Kitikmeot Sea

2023· article· en· W4380998042 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

VenueOcean science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaFisheries and Oceans CanadaUniversity of VictoriaEsri (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans 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
KeywordsArcticBayArchipelagoSea iceEnvironmental scienceOceanographyArctic geoengineeringArctic sea ice declineClimatologyArctic dipole anomalySink (geography)Arctic ice packGeologyDrift iceGeography

Abstract

fetched live from OpenAlex

Abstract. Warming of the Arctic due to climate change means the Arctic Ocean is now free from ice for longer, as sea ice melts earlier and refreezes later. Yet, it remains unclear how this 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 spatial and temporal air–sea CO2 flux variability in the changing Arctic Ocean. There is also uncertainty as to whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago (CAA). By using a research vessel that is based in the remote Inuit community of Ikaluqtuutiak (Cambridge Bay, Nunavut), we have been able to reliably survey pCO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. Here we present 4 years of consecutive summertime pCO2 measurements collected in the Kitikmeot Sea in the southern CAA. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was −4.64 mmol m−2 d−1), but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of +3.58, −2.96, −16.79 and −0.57 mmol m−2 d−1 during the 2016, 2017, 2018 and 2019 cruises, respectively). Surface ocean pCO2 varied by up to 156 µatm between years, highlighting the importance of repeat observations in this region, as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the surface ocean pCO2 value at the time of ice melt is extremely important in constraining the magnitude of the air–sea CO2 flux throughout the ice-free season. However, further constraining the air–sea CO2 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 small bays and inlets of the Kitikmeot Sea were ∼ 20–40 µatm lower than in the main channels. Surface ocean pCO2 measurements made close in time to ice breakup (i.e. within 2 weeks) were ∼ 50 µatm lower than measurements made &gt; 4 weeks after breakup. As previous basin-wide surveys of the CAA have focused on the deep shipping channels and rarely measure close to the ice breakup date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the CAA. 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 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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