Tuning ice model parameters to improve Arctic sea-ice simulation using the ERA5 atmospheric reanalysis forcing
Bibliographic record
Abstract
Two sets of simulations for 1993–2005 are carried out with a medium-resolution ocean and sea-ice model covering the North Pacific, Arctic and North Atlantic Oceans. The first set, using the same model parameters and three different atmospheric forcing datasets (DFS5.2, JRA55-do and ERA5), all show too fast melting of Arctic in spring and summer compared with the ice concentration based on satellite remote sensing. The simulation using ERA5 obtains the smallest ice concentration (largest deviation from satellite data) in summer, and the smallest ice thickness in both summer and winter, corresponding to the largest warm bias of surface air temperature over the Arctic sea-ice. In the second set of simulations using ERA5, changing either the snow conductivity (in W m−1 K−1, from the constant value of 0.31 to 0.15 during April –September and 0.5 during October–March) or the albedo of bare puddled ice (from 0.53 to 0.63) leads to an increase in ice concentration in summer, and ice thickness in both summer and winter. The simulation using ERA5 with both parameters altered is from October 1993 to March 2023, and obtains seasonal, interannual and long-term variations of ice area generally consistent with satellite data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".