Anthropogenic forcing of the Northern Annular Mode in CCMVal‐2 models
Bibliographic record
Abstract
We address the question of how ozone and long‐lived greenhouse gas changes impact the Northern Annular Mode (NAM). Using reanalyses and results from the Chemistry‐Climate Model Validation 2 (CCMVal‐2) initiative, we calculate seasonal NAM indices from geopotential height for winter and spring. From these, we determine the strength of stratosphere‐troposphere coupling in the model simulations and the reanalyses. For both seasons, we find a large spread in the ability of models to represent the vertical coherence of the NAM, although most models are within the 95% confidence interval. In winter, many models underestimate the vertical coherence derived from the reanalyses. Some models exhibit substantial differences in vertical coherence between simulations driven with modeled and observed ocean conditions. In spring, in the simulations using modeled ocean conditions, models with poorer horizontal or vertical resolution tend to underestimate the vertical coupling, and vice versa for models with better resolution. Accounting for model deficits in producing an appropriate troposphere‐stratosphere coupling, we show significant correlations of the NAM in winter with three indices representing the anthropogenic impact. Analysis of cross‐correlations between these indices suggests that increasing CO 2 is the main reason for these correlations in this season. In the CCMVal‐2 simulations, CO 2 increases are associated with a weakening of the NAM in winter. For spring, we show that the dominant effect is chemical ozone depletion leading to a transient strengthening of the NAM, with CO 2 changes playing an insignificant role.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".