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Record W6987152502

Sea-ice strength and internal stresses from in-situ measurements

2018· dissertation· en· W6987152502 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsBuoyArcticStress (linguistics)Sea iceArctic ice packThermalInternal stressArchipelago
DOInot available

Abstract

fetched live from OpenAlex

A knowledge of sea-ice dynamics is key to achieving proper Arctic climate simulations. The goal of this thesis is to lay the foundation for a three-dimensional ice dynamics model that considers both thermal and dynamic stresses. This thesis presents in-situ observations from buoys deployed in the Canadian Arctic Archipelago (CAA). In the CAA, the sea ice is landfast for approximately six months in winter. In a first step, we have analyzed in-situ data from a sea-ice stress buoy deployed in the Viscount Melville Sound. Results demonstrate that thermal stress is the dominant source of internal stress in the CAA with few short-lived important dynamic stress events caused by ice floe collisions in the free drift season prior to the landfast season. This is in contrast with similar internal sea-ice stress measurements made in the Arctic Ocean, in which both dynamic and thermal stresses are of similar magnitude. Prior to landfast ice onset, the thermal stresses are isotropic, as hypothesized in prior analyses of ice internal stress data measured in the Arctic Ocean. After landfast ice onset, however, the ther- mal stresses become anisotropic. Results from the buoy data, together with results from a 1.5D thermal stress model (forced with simulated internal sea-ice temperatures), demonstrate that the anisotropy in thermal stress arises from land confinement induced by the coastline in the direction of the short-axis of the channel. Results from the model are in good agreement with the observed stress in the direction of both principal stresses. They suggest that anisotropy in thermal stress could impact the mode of failure of sea ice in the CAA. The results also suggest that viscous creep stress relaxation is important and acts on time scale of several days, which is longer than the time scale (several hours) suggested from the previous measurements. In a second step, we derive estimates of the sea-ice compressive strength parameter (P∗) based on a simple force balance and known external forcing (surface air-ice and ice-ocean stresses) and whether sea ice drifts under the action of these external loads or not. Results from a proof of concept experiment using internally consistent data from a fully coupled ice-ocean model (the Regional Ice Operation Prediction System, RIOPS) demonstrate that it is indeed possible to estimate P∗, which is a known quantity in the model, from the simple force balance presented. When the same method is applied to in-situ observations and reanalysis data, the method only produces meaningful bounds of P∗ ( = 94.4 ± 4.4 kN/m2) when the pack ice is mostly composed of first-year ice with little multi-year ice present. This P∗ estimate is approximately three times the value currently used in the modelling community. This highlights the fact that the ice drift measured at a point may not be representative of the ice behaviour on average in a region. For instance, results suggest that larger tidal ocean currents in the region are enough to prevent a landfast ice cover to develop locally while the pack ice is mostly landfast on larger scales. The failures of the method when it is applied to some buoy data also suggest that there may be error in the surface forcing from the CGRF (Cana- dian Meteorological Center’s Global Deterministic Prediction System Reforecast). The analysis of these errors is left for future work. Results discussed in this thesis highlight the importance of thermal stresses in sea-ice models. The only forcing required by a thermal stress model is the internal temperature profile. Therefore, a thermal stress model could be implemented in current sea-ice thermodynamic models with a little effort. It would then be coupled with the dynamical part of the model by developing yield criteria for ice failure that are a function of the total (dynamical and thermal) stresses at a point rather than the depth average internal stress as currently done in the community.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.227
Teacher spread0.208 · 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
Published2018
Admission routes2
Has abstractyes

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