Replication of atmospheric oscillations, and their patterns, in predictors derived from Atmosphere–Ocean Global Climate Model output
Bibliographic record
Abstract
Abstract Atmosphere–Ocean Global Climate Model (AOGCM) output is used for many climate change impact studies and to produce ‘predictor’ data sets for statistical downscaling methods. Quantitative and qualitative evaluation and validation are required to make informed choices concerning reliable variables and their optimum combinations for both forms of research. Previous study suggests that although mean sea‐level pressure is generally well represented in models, biases associated with over‐ or underestimated activity for the Pacific Decadal Oscillation and the El Nino Southern Oscillation may exist within certain AOGCMs. This potential bias in indices of large‐scale atmospheric variability is explored. Improvements in the replication of circulation indices are discovered between the second and third generations of the Canadian AOGCM (CGCM2 and CGCM3). With respect to reanalysis product, CGCM3 output shows less winter‐time bias for the Northern Annular Mode and the North Atlantic Index than evident for the other indices under consideration. Copyright © 2010 Royal Meteorological Society
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".