Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe
Bibliographic record
Abstract
Abstract. Central European weather and climate is closely related to atmospheric mass advection triggered by the North Atlantic Oscillation (NAO) which is a relevant index for quantifying natural variability on multi-annual time scales. It remains unclear, though, how large-scale circulation variability affects local climate characteristics when downscaled using a regional climate model. In this study, 50 members of a single-model initial-condition large ensemble (LE) (http://www.climex-project.org/) are analyzed for a climate–NAO relationship, especially its inter-member spread and its transfer from the driving model CanESM2 into the driven model CRCM5. The NAO pressure dipole is quantified in the CanESM2-LE by an extended station-based index; responses of mean surface air temperature and total precipitation to changes in the index value are determined for a Central European domain (CEUR) in both the CanESM2-LE and CRCM5-LE. NAO–response relationships are expressed via Pearson correlation coefficients (strength) and the change per unit index change for historical (1981–2010) and future (2070–2099) winters. Results show that (a) statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions and (b) impulses from the NAO in the CanESM2-LE produce correct responses in the high-resolution CRCM5-LE. Relationships weaken in the future period, but the amplitude of their inter-member spread shows no significant change. Among others, the results strengthen the validity of the climate module in the ClimEx model chain for further impact modelling and stress the importance of single-model ensembles for evaluating internal variability.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".