Sensitivity of sea ice to physical parameterizations in the GISS global climate model
Bibliographic record
Abstract
The GISS coupled model is used to investigate the sensitivity of sea ice to each of the following parameterizations: (1) two sea ice dynamics (CF: cavitating fluid; VP: viscous‐plastic), (2) the specification of oceanic isopycnal mixing coefficients in the Gent and McWillams isopyncal mixing (GM), and (3) the wajsowicz viscosity diffusion (WV). The large‐scale sea ice properties are highly sensitive to sea ice dynamics. With the inclusion of resistance to shear stress, VP captures the major observed sea ice drift features and improves the simulations of sea ice concentrations, thickness, and export through Fram Strait relative to CF. GM significantly improves the simulation of vertical temperature distributions in the Southern Ocean, although it leads to a dramatic reduction of Antarctic sea ice cover. The reduced oceanic isopycnal mixing coefficients lead to Arctic sea ice that tends to be less and thinner in almost the entire Arctic except in the North Pacific and Labrador Sea, while Antarctic sea ice that extends more equatorward throughout the circumpolar regions. The responses of sea ice to WV show an enlargement and thickening of sea ice in the Arctic, within the ice packs around the Antarctic and a reduction and thinning of sea ice in the northern Weddell and Ross Seas. On the basis of these experiments, two composite experiments with the best parameterizations are investigated. The atmospheric responses associated with sea ice changes are discussed. While improvements are seen, there are still many unrealistic aspects that will require further improvements to sea ice and ocean components.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".