Intraseasonal Variation of Winter Precipitation over the Western United States Simulated by 14 IPCC AR4 Coupled GCMs
Bibliographic record
Abstract
Abstract This study evaluates the intraseasonal variation of winter precipitation over the western United States in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of each model’s twentieth-century climate simulation are analyzed. The focus is on the two dominant intraseasonal modes for the western U.S. precipitation: the 40-day mode and the 22-day mode. The results show that the models tend to overestimate the northern winter (November–April) seasonal mean precipitation over the western United States and Canada. The models also tend to produce overly strong intraseasonal variability in western U.S. wintertime precipitation, in spite of the overly weak tropical intraseasonal variability in most of the models. All models capture both the 40-day mode and the 22-day mode, usually with overly large variances. For the 40-day mode, models tend to reproduce its deep barotropic vertical structure and three-cell horizontal structure, but only 5 of the 14 models capture its northward propagation, and only 2 models simulate its teleconnection with the Madden–Julian oscillation in the tropical Pacific. For the 22-day mode, 8 of the 14 models reproduce its coherent northward propagation, and 9 models capture its teleconnection with precipitation in the tropical Pacific.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".