Sea-ice strength and internal stresses from in-situ measurements
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".