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Record W7010469744

The Influence of Local Sea Ice and Remote Northern Hemisphere Teleconnections on Cyclones in Baffin Bay, Davis Strait, and Labrador Sea

2020· dissertation· en· W7010469744 on OpenAlexaboutno aff

Bibliographic record

VenueOakTrust (Texas A&M University Libraries) · 2020
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceCyclone (programming language)Arctic ice packArctic oscillationNorthern HemisphereArctic sea ice declineTeleconnectionExtratropical cycloneCryosphere
DOInot available

Abstract

fetched live from OpenAlex

Cyclones are a primary mode of energy transport between the midlatitudes and the polar region in the Northern Hemisphere (NH). Due to the North Atlantic experiencing some of the highest frequencies of cyclones, this region has been extensively researched. However, Baffin Bay (BB), Davis Strait (DS), and Labrador Sea (LS) are also part of the North Atlantic, yet limited literature exists regarding cyclones and the influences of local and remote drivers on their variability in these subregions. This thesis focuses on determining the influence of local sea ice cover and remote NH teleconnections on cyclone variability in BB, DS, and LS from 1980-2015. To represent the local sea ice area driver, the NH’s monthly sea ice cover was subsetted for each subregion. To establish the remote atmospheric driver, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific Decadal Oscillation, Pacific North American Pattern, Polar Eurasian Pattern, and East Atlantic/West Russia Pattern’s monthly indices were obtained for 1980-2015. Cyclone variability was represented with the annual count, central pressure, cyclogenesis and cyclolysis events, local laplacian, latitude, and longitude from NSIDC’s NH Cyclone Locations and Characteristics record. Local drivers, remote drivers, and cyclone variables were analyzed using linear least-squares regression trends, compared using correlations (95% confidence interval), and all were seasonally standardized and detrended before each cyclone variable was compared with each local and remote driver at the long-term and individual monthly timescales. Results indicate that sea ice area in BB, DS, and LS decreased significantly, but at lower rates than the NH. Furthermore, each subregion’s sea ice variability is unique from the NH’s and from the other subregions, however periods of similar sea ice area deviation occurs across all subregions. The overall study area’s (BB-DS-LS) total number of cyclogenesis events had the only significant trend (positive) out of all cyclone variables. Comparing subregions, cyclone variability was noticeably different (especially between BB and LS). At the long-term and individual monthly timescales, local drivers had the most significant correlations mainly with the longitudinal position and cyclolysis stage of cyclones while the remote drivers had the most with their central pressures in all subregions. Overall, the NAO and AO had the most frequent and strongest correlations. While local sea ice area was significantly related with certain cyclone variables, these associations were typically weaker and less frequent. Therefore, overall cyclone variability in BB, DS, and LS is influenced more by remote atmospheric forcing than by local sea ice cover. Comparing BB, DS, and LS, it appears that, although they are geographically connected, each experiences unique cyclone variability. Finally, because each subregion’s cyclone variability is significantly related with local sea ice cover and remote teleconnections at different magnitudes and times, these subregions should be considered independently from one another and from the North Atlantic, to better capture atmospheric variability and interactions with the local environment.

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)0.000
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 score0.267
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.169
Teacher spread0.164 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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