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Record W2283000394 · doi:10.20381/ruor-13132

Coupled sea ice and climate variability from modern observations and proxy reconstructions

2009· dissertation· en· W2283000394 on OpenAlexaboutno aff
Christophe Kinnard

Bibliographic record

VenueuO Research (University of Ottawa) · 2009
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsProxy (statistics)ClimatologyPaleoclimatologySea iceEnvironmental scienceClimate changeOceanographyPhysical geographyGeologyGeographyComputer science

Abstract

fetched live from OpenAlex

Coupled climate and sea ice variability in the Arctic was investigated using a combination of modern, historical and proxy observations. In the Canadian Arctic, operational sea ice charts were homogenized into a spatially and temporally consistent gridded dataset. A complete climatic analysis of this dataset revealed the presence of dominant modes of sea ice variability related to driving climate patterns and atmospheric circulation indices such as the North Atlantic Oscillation (NAO) and El Nino Southern Oscillation (ENSO). On a hemispheric scale, the late-summer ice cover extent is decreasing at a much faster rate than the maximum winter ice cover. The disappearing perennial ice is partly replaced by seasonal ice, the areal extent of which has increased steadily over the last century. The enhanced seasonal sea ice freeze-thaw cycle is predicted to increase the salinity of surface waters over continental shelves, thereby enhancing haline convection and ventilation of the deeper Arctic Ocean. Coupled sea ice and climate proxies for the North Baffin Bay region were developed from an existing ice core from Devon Ice Cap and a new ice core from the Prince-of-Wales (POW) Icefield on Ellesmere Island. A sea-salt concentration record from the Devon ice core was found to relate with sea ice concentration in nearby Baffin Bay. The record was used to study past sea ice conditions in Baffin Bay over the last 200 years in relation with temperature proxies (melt %, delta18O). Sea ice extent variations in northern Baffin Bay appear to be mostly dynamically driven, with sea ice decreasing when Nares Strait becomes congested with ice from the Arctic Ocean, and northerly winds advect ice from Baffin Bay southward. A new high-resolution melt record was developed using digital image analysis of the POW ice core. The record was used to show that melting affects the solid conductivity signal of the core, which compromises dating by seasonal layer counting, and hinders the identification of acidic volcanic horizons. The POW melt record, a proxy for summer warmth, was shown to be site-specific, which may be explained by the close presence of the North Open Water polynya and the peculiar position of the ice cap which rests on the shifting boundary between the maritime climate of Baffin Bay and the drier, colder climate of the high Arctic. The long-term, natural variability of late-summer Arctic sea ice was reconstructed from a network of 68 climate proxies from the circum-Arctic region. The proxy network contains both a temperature and a sea ice signal. Past sea ice extent was reconstructed using multivariate statistical calibration of the network against historical sea ice observations over the last century. The record shows that the decline in sea ice extent of the last two decades is anomalous in the context of the last 900 years. Non-linear processes are responsible for much of the variability in ice extent over the past millennium, and the same processes may be enhancing the greenhouse gas-induced decrease in ice extent currently observed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.030
GPT teacher head0.251
Teacher spread0.221 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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