The Dynamical Response to Snow Cover Perturbations in a Large Ensemble of Atmospheric GCM Integrations
Bibliographic record
Abstract
Abstract Variability in the extent of fall season snow cover over the Eurasian sector has been linked in observations to a teleconnection with the winter northern annular mode pattern. Here, the dynamics of this teleconnection are investigated using a 100-member ensemble of transient integrations of the GFDL atmospheric general circulation model (AM2). The model is perturbed with a simple persisted snow anomaly over Siberia and is integrated from October through December. Strong surface cooling occurs above the anomalous Siberian snow cover, which produces a tropospheric form stress anomaly associated with the vertical propagation of wave activity. This wave activity response drives wave–mean flow interaction in the lower stratosphere and subsequent downward propagation of a negative-phase northern annular mode response back into the troposphere. A wintertime coupled stratosphere–troposphere response to fall season snow forcing is also found to occur even when the snow forcing itself does not persist into winter. Finally, the response to snow forcing is compared in versions of the same model with and without a well-resolved stratosphere. The version with the well-resolved stratosphere exhibits a faster and weaker response to snow forcing, and this difference is tied to the unrealistic representation of the unforced lower-stratospheric circulation in that model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".