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Record W2948787665 · doi:10.1525/elementa.
\n229

Modelling carbon exchange in the air, sea, and ice of the Arctic Ocean

2019· article· en· W2948787665 on OpenAlexaboutno aff
Eric Mortenson

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticOceanographyEnvironmental scienceSea iceClimatologyArctic ice packMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the evolution of the Arctic Ocean’s carbon
\nuptake capacity and impacts on ocean acidification with the changing sea-ice scape. In
\nparticular, I study the influence on air-ice-sea fluxes of carbon with two major updates to
\ncommonly-used carbon cycle models I have included. One, incorporation of sea ice algae
\nto the ecosystem, and two, modification of the sea-ice carbon pump, to transport brineassociated
\nDissolved Inorganic Carbon (DIC) and Total Alkalinity (TA) to the depth of
\nthe bottom of the mixed layer (as opposed to releasing it in the surface model layer). I
\ndeveloped the ice algal ecosystem model by adding a sympagic (ice-associated) ecosystem
\ninto a 1D coupled sea ice-ocean model. The 1D model was applied to Resolute Passage in
\nthe Canadian Arctic Archipelago and evaluated with observations from a field campaign
\nduring the spring of 2010. I then implemented an inorganic carbon system into the model.
\nThe carbon system includes effects on both DIC and TA due to the coupled ice-ocean
\necosystem, ikaite precipitation and dissolution, ice-air and air-sea carbon exchange, and
\nice-sea DIC and TA exchange through a formulation for brine rejection to depth and
\nfreshwater dilution associated with ice growth and melt. The 1D simulated ecosystem was
\nfound to compare reasonably well with observations in terms of bloom onset and seasonal
\nprogression for both the sympagic and pelagic algae. In addition, the inorganic carbon
\nsystem showed reasonable agreement between observations of upper water column DIC
\nand TA content. The simulated average ocean carbon uptake during the period of open
\nwater was 10.2 mmol C m−2 day−1 ( 11 g C m−2 over the entire open-water season).
\nUsing the developments from the 1D model, a 3D biogeochemical model of the Arctic Ocean
\nincorporating both sea ice and the water column was developed and tested, with a focus
\non the pan-Arctic oceanic uptake of carbon in the recent era of Arctic sea ice decline (1980
\n– 2015). The model suggests the total uptake of carbon for the Arctic Ocean (north of
\n66.5 N) increases from 110 Tg C yr−1 in the early eighties (1980 – 1985) to 140 Tg C yr−1
\nfor 2010 – 2015, an increase of 30%. The rise in SST accounts for 10% of the increase
\nin simulated pan-Arctic sea surface pCO2. A regional analysis indicated large variability
\nbetween regions, with the Laptev Sea exhibiting low sea surface pH relative to the pan-
\nArctic domain mean and seasonal undersaturation of 
\narag by the end of the standard run. Two sensitivity studies were performed to assess the effects of sea-ice algae and the sea-ice carbon pump in the pan-Arctic, with a focus on sea surface inorganic carbon properties. Excluding the sea ice-carbon-pump showed a marked decrease in seasonal variability of sea-surface DIC and TA averaged over the Arctic Ocean compared to the standard run, but only a small change in the net total carbon uptake (of 1% by the end of the no icecarbon-pump run). Neglecting the sea ice algae, on the other hand, exhibits only a small change in sea-surface DIC and TA averaged over the pan-Arctic Ocean, but a cumulative effect on the net total carbon uptake of the Arctic Ocean (reaching 5% less than that of the standard run by the end of the no-ice-algae run).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.221
Teacher spread0.203 · 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.

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

Citations13
Published2019
Admission routes1
Has abstractyes

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