The AWI Climate Model: response to increased resolution in dynamically active regions
Bibliographic record
Abstract
State-of-the-art climate models do still exhibit pronounced deviations from the measured climate. Those deviations are often common between those models. The challenging problems in the Northern hemisphere include warming and salinization of the deep ocean being most pronounced in the northern North Atlantic, reduced deep water formation in the Labrador Sea which is sometimes accomplished by the sporadic ice coverage of the whole Labrador Sea, and an extensive ice presence in the Barents Sea. All these biases are often attributed in literature to the lack of oceanic resolution. \nThe multi-resolution approach used in the ocean component of the AWI climate model (ECHAM6-FESOM) allows to use enhanced horizontal resolution in dynamical active regions while keeping a coarse-resolution setup everywhere else. In this study we develop strategies for improving the climate model biases by means of increasing resolution in the ocean. The current computations have been performed on multi-centennial time scales using refinement in the different parts of the global ocean. Benefits from the local refinement have been analyzed. It is found that already with moderate refinement of the unstructured ocean grid, AWI-CM performs as well as some of the most sophisticated climate models participating in CMIP5.
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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 | 0.003 | 0.012 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".