Evaluation of Northern Hemisphere Blocking Climatology in the Global Environment Multiscale Model
Bibliographic record
Abstract
Abstract The performance of the Global Environmental Multiscale (GEM) model, the Canadian operational numerical model, in reproducing atmospheric low-frequency variability is evaluated in the context of Northern Hemisphere blocking climatology. The validation is conducted by applying a comprehensive but relatively simple blocking detection algorithm to a 20-yr (1987–2006) integration of the GEM model in climate mode. The comparison to reanalysis reveals that, although the model can reproduce Northern Hemisphere blocking climatology reasonably well, the maximum blocking frequency over the North Atlantic and western Europe is generally underestimated and its peak season is delayed from late winter to spring. This contrasts with the blocking frequency over the North Pacific, which is generally overestimated during all seasons. These misrepresentations of blocking climatology are found to be largely associated with the biases in climatological background flow. The modeled stationary waves show a seasonal delay in zonal wavenumber 1 and an eastward extension in zonal wavenumber-2 components consistent with blocking frequency biases. High-frequency eddies are, however, consistently underestimated both in the North Atlantic and Pacific, indicating that the biases in eddy fields might not be the main reason for the blocking biases in the North Pacific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".