Evaluating the E3SMv2-MPAS ocean–sea ice coupled unstructured model in the Arctic: Atlantification processes and systematic biases
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 ocean–sea ice system are systematically evaluated using multi-source observational data. The model employs a latitudinally varying mesh, with resolution increasing from 60 km in the Southern Hemisphere to 10 km in the Arctic. This design balances computational efficiency with the accurate integration of low-latitude oceanic influences, while the unstructured mesh also enhances the geometric representation of Arctic straits. Together, these features form a simulation framework capable of resolving processes from seasonal to decadal timescales. 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) faithful reproduction of both the freshwater content and transports through key Arctic gateways; (3) 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; 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".