Multi-Scale Sea Ice Kinematics Modeling with a Grid Hierarchy inCommunity Earth System Model (version 1.2.1)
Bibliographic record
Abstract
Abstract. High-resolution sea ice modeling is becoming widely available for both operational forecasts and climate studies. Sea ice kinematics is the most prominent feature of high-resolution simulations, and with rheology models such as Viscous-Plastic, current models are able to reproduce multi-fractality and linear kinematic features in satellite observations. In this study, we carry out multi-scale sea ice modeling with Community Earth System Model (CESM) by using a grid hierarchy (22 km, 7.3 km, and 2.5 km grid stepping in the Arctic). By using atmospherically forced experiments, we simulate consistent sea ice climatology across the 3 resolutions. Furthermore, the model reproduces reasonable sea ice kinematics, including multi-fractal deformation and scaling properties that are temporally changing and dependent on circulation patterns and forcings (e.g., Arctic Oscillation). With the grid hierarchy, we are able to evaluate the model's effective spatial resolution regarding the statistics of kinematics, which is estimated to be about 6 to 7 times that of the grid's native resolution. Besides, we show that in our model, the convergence of the Elastic-Viscous-Plastic (EVP) rheology scheme plays an important role in reproducing reasonable kinematics statistics, and more strikingly, simulates systematically thinner sea ice than the standard, non-convergent experiments in landfast ice regions of Canadian Arctic Archipelago. Given the wide adoption of EVP and subcycling settings in current models, it highlights the importance of EVP convergence especially for climate studies and projections. The new grids and the model integration in CESM are openly provided for public use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".