The Forced Response and Decadal Predictability of the North Atlantic Oscillation: Nonstationary and Fragile Skills
Bibliographic record
Abstract
Abstract We investigate the forced response of the North Atlantic Oscillation (NAO)—calculated as the ensemble mean—in different large ensembles of climate models including simulations with historical forcings and initialized decadal hindcasts. The forced NAO in the CMIP6 historical ensemble correlates significantly with observations after 1970. However, the forced NAO shows an apparent nonstationarity with significant correlations to observations only in the period after 1970 and in the period before 1890. We demonstrate that such apparent nonstationarity can be due to chance even when models and observations are independent. For the period after 1970 the correlation to the observed NAO continues to increase while the amplitude of the forced signal continues to decrease—although both with some signs of saturation—when the ensemble size grows. This behavior can be explained by a simple statistical model assuming a very small signal-to-noise ratio in the models. We find only rather weak evidence that initialization improves the skill of the NAO on decadal time scales. The NAO in the historical ensembles including only natural forcings, well-mixed greenhouse gases, or anthropogenic aerosols show skill that is not significantly different from zero. The same holds for a large single-model ensemble. The skills of these ensembles, except for the well-mixed greenhouse gas ensemble, are also significantly different from the skill of the larger full historical ensemble even though their ensemble sizes are smaller. Taken together, our results challenge the possibility of useful NAO predictions on decadal time scales.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".