Inter-comparison of state-of-the-art MSS and geoid models in the Arctic Ocean
Bibliographic record
Abstract
State-of-the-art Arctic Ocean mean sea surface (MSS) and geoid models are used to support sea ice freeboard estimation from satellite altimeters, and for oceanographic studies. However, errors in a given model in the high frequency domain, e.g. due to unresolved gravity features, can result in errors in the estimated freeboard heights, especially in areas with a sparse lead distribution in consolidated ice conditions. Additionally these errors can impact ocean geostrophic current estimates and remaining biases in the models may impact longer-term, multi-sensor oceanographic time-series of sea level change.This study, part of the ESA CryoVal Sea Ice project, focuses on an inter-comparison of various state-of-the-art Arctic MSS models (UCL13/DTU13/ICEn) and commonly-used geoid models (EGM08). We show improved definition of gravity features, such as the Gakkel ridge, in the latest MSS models. We quantify remaining errors due to unresolved gravity features and inter-satellite biases within commonly-used models and we show the implications of these potential error sources on freeboard derivation. To identify and quantify the spatial effect of the unresolved features, primarily in the high-frequency domain, we combine a climatology of lead distributions with the gradient of the slope of the sea surface anomalies. The differences between the models are analyzed and used to support improvements in future models.
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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 | 0.004 | 0.005 |
| 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 | 0.002 | 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".