Diagnosing the stratosphere-troposphere stationary wave response to climate change in a general circulation model
Bibliographic record
Abstract
[1] A stationary wave model (SWM) that captures the stratospheric and tropospheric stationary wave field is developed and applied to the problem of the stationary wave response to climate change. The SWM solution is controlled by damping settings that need to be tuned differently for observational and for modeling applications. The SWM is used to separately diagnose the effects of changes to the zonally asymmetric component of diabatic heating and of changes to the zonal mean basic state on the Northern Hemisphere winter stationary wave response to greenhouse gas forced climate change simulated by the Canadian Middle Atmosphere Model (CMAM). The SWM analysis shows that changes to the zonal mean basic state with diabatic heating held fixed explain much of the stationary wave response. In particular, changes to the zonal mean wind in the Northern Hemisphere subtropical upper troposphere dominate the subtropical and extratropical stationary wave response. CMAM simulates an increase in stratospheric wave driving in response to greenhouse forcing, in common with many climate models. In the SWM, this wave driving response, which is sensitive to the spatial structure of the waves, is not dominated by the subtropical jet response but involves several aspects of the zonal mean wind response and the diabatic heating response, all of which contribute to enhanced stratospheric wave driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".