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Record W1642882620 · doi:10.82308/2586

Modeling the variability of the liquid freshwater export from the Arctic Ocean

2010· article· en· W1642882620 on OpenAlexfundaboutno aff
Alexandra Jahn

Bibliographic record

VenueeScholarship@McGill (McGill) · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationStudienstiftung des Deutschen VolkesCanadian Foundation for Climate and Atmospheric SciencesArcticNetOregon State UniversityMcGill UniversityUniversity of VictoriaUniversity of California, Los AngelesNational Science Foundation
KeywordsOcean gyreEnvironmental scienceArcticClimatologyForcing (mathematics)ArchipelagoOceanographyGeologySubtropicsEcologyBiology

Abstract

fetched live from OpenAlex

In this thesis an analysis of the variability of the liquid freshwater (FW) export from the Arctic Ocean on annual and seasonal timescales is presented. Due to missing long-term observations, the variability of the liquid FW export is not well known or understood. Model simulations are therefore currently the only way to study the variability of the FW export from the Arctic. To investigate the role of the atmospheric forcing for the variability of the liquid FW export, a model simulation for 1950-2007 from the University of Victoria Earth System Climate Model (UVic ESCM) is analyzed. It is shown that large-scale atmospheric circulation changes generally control the variability of the FW export through changes in the FW storage in the Beaufort Gyre. These changes have a large influence on the variability of the FW export through the Canadian Arctic Archipelago (CAA), whereas the Fram Strait FW export is also influenced by changes in the FW storage in the Eurasian basin. In order to better understand the differences between the mechanisms driving the export variability through Fram Strait and the CAA, passive dye tracers are added to the ocean module of a state-of-the-art global general circulation model, the Community Climate System Model Version 3 (CCSM3). These tracers allow the identification of FW from different sources, and therefore the individual investigation of the export variability of FW from individual sources. It is shown that the Fram Strait FW export is made up mainly of Eurasian runoff and Pacific FW, whereas the FW exported through the CAA comes primarily from Pacific FW and North American runoff. The variability of the FW exports from individual sources is largely in phase in the CAA, as the CAA FW export is mainly driven by velocity anomalies, not FW concentration anomalies. In Fram Strait on the other hand, FW concentration anomalies contribute as much to the FW export variability as velocity anomalies. The variability of the Fram Strait FW concentrations from the two main FW sources is not in phase, as Pacific FW and Eurasian runoff have different pathways to Fram Strait and their variability is governed by different mechanisms. Whereas the Eurasian runoff export depends strongly on the release of FW from the Eurasian shelf during years with an anticyclonic circulation anomaly (negative Vorticity index), the variability of the Pacific export is mainly controlled by changes in the Pacific FW stored in the Beaufort Gyre, with increased export during years with a cyclonic circulation anomaly (positive Vorticity index). A high vertical resolution of the ocean model is found to be important to resolve the role of FW concentration changes for the Fram Strait FW export variability. The model simulation also shows that in contrast to the interannual variability, the seasonal variability of the Fram Strait FW export is driven almost entirely by the seasonal cycle of sea-ice melt, with a smaller influence of velocity changes or advected FW concentration changes. The disappearance of the summer sea-ice cover in the Arctic during the 21st century might therefore affect the seasonal cycle of the Fram Strait FW export.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations0
Published2010
Admission routes2
Has abstractyes

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