An Evaluation Of The Ocean And Sea Ice Climate Of E3Sm Using Mpas And Interannual Core-Ii Forcing
Bibliographic record
Abstract
The Energy Exascale Earth System Model (E3SM) is a new coupled climate model sponsored by the US Department of Energy. Here we present E3SM global simulations using active ocean and sea ice and driven by the CORE-II inter-annual atmospheric forcing data set. The E3SM ocean and sea-ice components are MPAS-Ocean and MPAS-Seaice, which use the Model for Prediction Across Scales (MPAS) framework and run on unstructured horizontal meshes. For this study, grid cells vary from 30 to 60 km for the low resolution mesh and 6 to 18 km at high resolution. The vertical grid is a structured z-star coordinate and uses 60 and 100 layers for low and high resolution, respectively. The lower resolution simulation was run for five core cycles (300 years) with little drift in sea surface temperature or heat content. The meridional heat transport is within observational variability, while the meridional overturning circulation at 26.5N is low compared to observations. The largest temperature biases occur in the Labrador sea and western boundary currents, and the mixed layer is deeper than observations at high latitudes in the winter months. Sea-ice extent, volume and concentration agree well with observations. At high resolution, the sea surface height compares well with satellite observations in mean and variability, but is overly energetic in regions with strong currents.
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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 | 0.004 |
| 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 | 0.001 |
| 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".