Validation of the North American Ice Service iceberg drift model using a novel database of in-situ iceberg drift observations
Bibliographic record
Abstract
Icebergs calved from high-latitude glaciers and ice shelves pose a threat to vessels and offshore infrastructure at a time when Arctic shipping and offshore resource exploration are increasing. Knowledge of the location of potential ice hazards is therefore critical to ensure safe and efficient operations in this remote region. The Canadian Ice Service provides information to stakeholders on the observed and predicted distribution of icebergs in Canadian waters by combining iceberg observations with forecasts from the North American Ice Service (NAIS) iceberg drift model. The NAIS model estimates the forces acting on an iceberg to predict its future position and velocity and is widely used for the East Coast of Canada. However, the model is unproven for areas >60°N and suffers from insufficient validation due to a lack of reliable in-situ observations of iceberg drift. In this study, we use a newly compiled iceberg tracking beacon database to assess the skill of the NAIS iceberg model's predictions of iceberg drift and investigate sensitivity to morphology and environmental forcing (e.g., ocean currents, winds). Hindcast simulations of the observed tracks of 44 icebergs over the period 2008-2019 were run using ocean currents from three ocean models (CECOM, GLORYS and RIOPS) and wind and wave inputs from the ERA5 reanalysis. Comparisons of several distance error metrics between observed and modelled drift tracks indicate that the NAIS model produces realistic simulations of iceberg drift in Baffin Bay. The root mean square error after the initial 24-hour hindcast period ranged from 18-22 km and increased at a daily rate of 11-13 km, which is comparable to operational forecasts elsewhere. Improved model performance was observed for longer (>250 m) and deeper-keeled (>100 m) icebergs, which appears to counteract the model’s tendency to overestimate drift by reducing the influence of stronger surface ocean currents acting on the iceberg. Ocean current direction, wind direction, and iceberg keel geometry were identified by a sensitivity analysis as the model parameters and environmental driving forces that have the greatest influence on modelled iceberg drift. These results emphasize the need for accurate environmental information and underscore the importance of properly representing the physical characteristics of icebergs in drift models.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".