Eddy statistics validation of an ORCA12 ocean and sea ice model for the Arctic with satellite data
Bibliographic record
Abstract
As sea ice and ocean models are moving to higher resolution it becomes possible to permit eddy formation even in the Arctic Ocean. Eddies can affect the three dimensional ocean state through causing mixing and even ventilation of subsurface ocean layers if they are deep enough. To ensure models have the potential to simulate the density structure correctly it is therefore necessary to start doing model validation of not only the large scale ocean state, but also of the eddy field. Eddy statistics for the Arctic are available from satellite for the Western Arctic Ocean and the Fram Strait, in particular on number, size and cyclonicity of eddies for open ocean versus ice covered sites. These are compared to a NEMO-LIM 1/12 degree sea ice and ocean simulation (resolution 2-5km), upon which the model based statistics are expanded to the whole Arctic. In the model it is also possible to examine the depth structure of eddies, allowing to generate size vs. depth statistics. This, together with climatological mixed layer depth, provides a first estimate to get satellite-based information on mixing from eddies in the Arctic. We also map the maximum depth of eddies, to examine ventilation and identify sites with especially deep eddies, for instance at the boundary current. Acknowledgements: Grant NE/R000654/1 “Towards a Marginal Sea Ice Cover” funded by the UK Natural Research Council (NERC) and the UK-Russia Arctic bursaries program funded by the United Kingdom’s Department for Business, Energy and Industrial Strategy. The study is also supported from the project “The Advective Pathways of nutrients and key Ecological substances in the Arctic (APEAR)” (grant NE/R012865/1) funded by the Joint UK NERC/German Federal Ministry of Education and Research Changing Arctic Ocean Programme. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821926 (IMMERSE). IK acknowledges the support from RFBR grant No 18-35-20078.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".