Variability of sea ice deformation rates in the Arctic and their relationship with basin-scale wind forcing
Bibliographic record
Abstract
Abstract. The temporal variability of the moments of probability distribution functions (pdfs) of total sea ice deformation rates in the Arctic is analyzed in the context of the basin-scale wind forcing acting on the ice. The pdfs are estimated for 594 satellite-derived sea ice deformation maps from 11 winter seasons between 1996/1997 and 2007/2008, provided by the RADARSAT Geophysical Processor System. The temporal scale analyzed equals 3 days. The moments of the pdfs, calculated for a range of spatial scales (12.5–900 km), have two dominating components of variability: a seasonal cycle, with deformation rates decreasing throughout winter towards a minimum in March; and a short-term, synoptic variability, strongly correlated with the area-averaged magnitude of the wind stress over the Arctic, estimated based on the NCEP-DOE Reanalysis-2 data (correlation coefficient of 0.71 for the mean deformation rate). Due to scaling properties of the moments, logarithms of higher moments are strongly correlated with the wind stress as well. Exceptions are observed only at small spatial scales, as a result of extreme deformation events, not directly associated with large-scale wind forcing. By repeating the analysis within regions of different sizes and locations, we show that the wind–ice deformation correlation is largest at the basin scale and decreases with decreasing size of the area of study. Finally, we suggest that a positive trend in seasonally averaged correlation between sea ice deformation rates and the wind forcing, present in the analyzed data, may be related to an observed decrease in the multi-year ice area in the Arctic, indicating possibly even stronger correlations in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".