Bibliographic record
Abstract
Abstract. Thermodynamic and dynamic sea ice thickness processes are affected by differing mechanisms in a changing climate. Independent observational datasets of each are essential for model validation and accurate projections of future sea ice conditions. Here we present the first long-term, sub-seasonal temporal resolution, basin-wide and Eulerian climatology of dynamically and thermodynamically driven sea ice thickness effects across the Arctic. Basin-wide estimates of thermodynamic growth rate are determined by coupling passive microwave retrieved snow–ice interface temperatures to a simple sea ice thermodynamic model, total growth is calculated from weekly Alfred Wegener Institute (AWI) CS2SMOS sea ice thickness spanning fall 2010 through spring 2021, and the dynamics component is calculated as their difference. The dynamic effects are further separated into advection and deformation effects using a sea ice motion dataset. Thermodynamic growth varies from less than 0.04 m wk-1 in the central Arctic to greater than 0.08 m wk-1 in the seasonal ice zones. High positive dynamic effects of greater than 0.04 m wk-1, as high as twice that of thermodynamic growth, are found north of the Canadian Arctic Archipelago where the Transpolar Drift and Beaufort Gyre deposit ice. Strong negative dynamic effects of greater than 0.08 m wk-1 are found where the Transpolar Drift originates, nearly equal to thermodynamic effects in these regions. Yearly results from the winter of 2019–2020 compare well with a recent study of the dynamic and thermodynamic effects on sea ice thickness along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift track during the winter of 2019–2020. Couplets of deformation and advection effects with opposite sign are common across the Arctic, with positive advection effects and negative deformation effects found in the Beaufort Sea and negative advection effects and positive deformation effects found in most other regions. The seasonal cycle shows deformation effect and overall dynamic effects increasing as the winter season progresses.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.175 | 0.145 |
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".