An EC-Earth coupled atmosphere-ocean single-column model(AOSCM) for studying coupled marine and polar processes
Bibliographic record
Abstract
Abstract. Single-column models (SCM) have been used as a tool to develop numerical weather prediction and global climate models for several decades. SCMs decouple small-scale processes from large-scale forcing and thus allow to test physical parameterizations in a controlled environment with reduced computational cost. Typically, either the ocean, sea-ice or atmosphere is fully modelled and assumptions have to be made on the boundary conditions from other subsystems, adding a potential source of errors. Here, we present a fully coupled atmosphere-ocean SCM (AOSCM), including sea-ice, which is related to the global climate model EC-Earth, consisting of NEMO3.6, LIM3, OpenIFS cycle 40r1, and OASIS3-MCT. The AOSCM is tested at three locations: the tropical Atlantic, the midlatitude Pacific and the Arctic. At all three locations in-situ observations are available for comparison. Evaluating model performance with buoy data, soundings and ship based observations, we find that the coupled AOSCM can capture the observed atmospheric and oceanic evolution. Model evolution is sensitive to the initial conditions and forcing data imposed on the column. Coupling several model components while alongside using them individually can help disentangle model feedbacks. Although the model can be extended, we demonstrate that already in the current setup it is a valuable tool to advance our understanding in marine and polar boundary layer processes and the interactions of their coupled components.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".