A new free drift sea ice velocity dataset for improved representations of ice drift trajectories
Bibliographic record
Abstract
Previous work shows that tracking the motion of sea ice in a Lagrangian framework can be used to produce skillful seasonal forecasts of sea ice at the pan-Arctic scale (Williams et al. 2016) and at the regional scale (Brunette et al. 2019) and can also be used to analyze socio-environmental impacts related to sea ice circulation (Newton et al. 2017). However, the Polar Pathfinder sea ice motion dataset from the National Snow and Ice Data Centre (Tschudi et al. 2019), which is commonly used for calculations of ice drift trajectories, contains biases in sea ice drift speed and angle. The bias is particularly strong in the summer when less satellite drift-vectors are available, and the Polar Pathfinder composite product relies more heavily on poorly-constrained free drift estimates (ice motion in response to wind forcing and ocean drag in the absence of internal stresses), that have up to a 60% low speed bias when compared to buoy drifts. These free drift estimates are notoriously ill-constrained, since information on the ocean forcing from below and lateral forces within the ice pack are lacking. To improve the quality of ice motion estimates in the summer, we propose to compile a new free drift sea ice motion dataset, based on surface winds from ERA-Interim and calibrated on drifting buoys from the International Arctic Buoy Program. We include dependencies of free drift velocity on sea ice concentration and thickness, which will improve the representation of temporal and spatial variability of sea ice in a free drift regime. We present work on the parameterization of an ice state dependent transfer coefficient between wind velocity and ice velocity, and estimates of the near surface oceanic currents that are necessary to constrain ice motion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".