Sensitivity of short-range forecasts to sea ice thickness data assimilation parameters in a coupled ice-ocean syste
Bibliographic record
Abstract
Sea ice thickness (SIT) estimates derived from CryoSat-2 radar freeboard measurements are assimilated into the Met Office's global ocean–sea ice forecasting system, FOAM. We test the sensitivity of short-range forecasts to the snow depth, radar freeboard product and assumed radar penetration through the snowpack in the freeboard-to-thickness conversion. We find that modifying the snow depth has the biggest impact on the modelled SIT, changing it by up to 0.88 m (48%), compared to 0.65 m (33%) when modifying the assumed radar penetration through the snowpack and 0.55 m (30%) when modifying the freeboard product. We find a doubling in the thermodynamic volume change over the winter season when assimilating SIT data, with the largest changes seen in the congelation ice growth. Next, we determine that the method used to calculate the observation uncertainties of the assimilated data products can change the mean daily model SIT by up to 36%. Compared to measurements collected at upward-looking sonar moorings and during the Operation IceBridge campaign, we find an improvement in the SIT forecasts’ variability representation when assuming partial radar penetration through the snowpack and when improving the method used to calculate the CryoSat-2 observation uncertainties. This paper highlights a concern for future SIT data assimilation and forecasting, with the chosen parameterisation of the freeboard-to-thickness conversion having a substantial impact on model results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".