Bibliographic record
Abstract
Sea ice has been highlighted as a climate change indicator [IPCC, 1995]. Models are useful tools to study Arctic sea ice on decadal and longer time scales [Vinnikov et al., 1999] and the viscous-plastic model has been identified as the best model available to simulate ice motion [Kreyscher et al., 2000]. Here we investigate whether a stand-alone viscous-plastic model reproduces observed ice thickness. The Kiel sea ice model and the UCL model, documented in this thesis, are compared to ERS radar altimeter estimates of ice freeboard. Compared to the observations, ice thickness is over estimated by 2 m in both models. Near the Canadian Archipelago this thickness difference increases to 3 m. We investigate whether a large thickness error in the model can be explained by errors in the model force balance. A sensitivity study of ocean and wind drag coefficients and maximum ice strength shows that the model ice thickness cannot be improved by only varying the maximum ice strength. The viscous-plastic model is computationally expensive to solve accurately, which hinders its use in GCMs. We revise the numerical solution, introducing a new overrelaxation method which guarantees the stress solution is always within the yield criterion. The convergence of the velocity vector is much improved compared to the iterative scheme of Zhang and Hibier [1997]. Finally a numerical error is identified in the traditional velocity correction scheme which accounts for up to 0.5 m of the model ice thickness error. An efficient algorithm is designed and implemented to ensure the fully coupled mass-momentum solution is found to numerical accuracy. In this thesis we find that the viscous-plastic model over estimates Arctic ice thickness in the late 1990s. Up to 25% of this error may be attributed to unresolved mass-momentum coupling, and we suggest other errors may lie in thermodynamic modelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".