A new atmospheric dataset for forcing ice–ocean models: Evaluation of reforecasts using the Canadian global deterministic prediction system
Bibliographic record
Abstract
Abstract Despite the availability of several atmospheric reanalyses (e.g. ERA‐Interim) there exists both considerable uncertainty in surface forcing fields for ice/ocean modelling and sensitivity to the choice of product used. Here we introduce a relatively high‐resolution alternative forcing dataset for ice–ocean models derived from the Canadian Meteorological Centre's (CMC) global deterministic prediction system (GDPS). A set of daily 30 h reforecasts is produced using the GDPS 33 km resolution model providing hourly atmospheric forcing fields for the period 2002–2011. The CMC GDPS reforecasts (CGRF) are compared with ERA‐Interim and several observational datasets to evaluate their suitability for forcing ocean models. In particular, the surface temperature, humidity and winds of the CGRF show equivalent biases to those found in ERA‐interim. Moreover, the higher resolution of the CGRF permit a more detailed representation of atmospheric structures and topographic steering, resulting in finer‐scale coastal features and wind‐stress curl. Although the CGRF dataset is not a reanalysis and thus is expected to be less well constrained by available observations, its higher resolution and small bias make it an attractive alternative for forcing ice‐ocean 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".