Bibliographic record
Abstract
Abstract. Advancing high-resolution Arctic ocean-sea ice modeling is critical for understanding polar amplification and improving climate projections but faces challenges from computational limits and cross-scale interactions. The simulation capabilities of the ocean-sea ice coupled model (E3SMv2-MPAS) from the Energy Exascale Earth System Model (E3SM) 2.1 for the Arctic sea ocean-sea ice system are systematically evaluated using multi-source observational data and model outputs. A latitudinally varying mesh (60 km in the Southern Hemisphere to 10 km in the Arctic) balances computational efficiency while integrating low-latitude oceanic influences. Unstructured meshes enhance geometric representation of Arctic straits, coupled with a suitable mesoscale eddy transport parameterization to establish a multi-scale simulation framework. Numerical results demonstrate E3SMv2-MPAS's superior Arctic simulation performance: (1) Accurate reproduction of spatial heterogeneity in sea ice concentration, thickness, and sea surface temperature, including their 1995–2020 trend patterns; (2) Successful reconstruction of three-dimensional thermohaline structures within the Atlantic Water layer, capturing Atlantic Water's decadal warming trends and accelerated Atlantification processes – specifically mid-layer shoaling, heat content amplification, and reduced heat transfer lag times in the Eurasian Basin. Persistent systematic biases are identified: 0.5–1 m sea ice thickness overestimation in the Canadian Basin compared to ICESat observations; Coordinated sea surface temperature/salinity underestimation and sea ice concentration overestimation in the Greenland and Barents Seas; Atlantic Water core temperature overestimation; Regional asymmetries in decadal thermohaline field evolution. These systematic biases may be attributed to three principal sources: inadequate representation of eddy dynamics, limitations in mixing parameterizations, and insufficient resolution of cross-scale interactions in key gateways (e.g., Fram Strait).
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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 | 0.002 | 0.020 |
| 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 | 0.002 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.193 | 0.141 |
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".