Simulating Global and North American Climate Using the Global Environmental Multiscale Model with a Variable-Resolution Modeling Approach
Bibliographic record
Abstract
Abstract Results from two simulations using the Global Environmental Multiscale (GEM) model in a variable-resolution modeling approach are evaluated. Simulations with a highly resolved domain positioned over North America and over the tropical Pacific–eastern Indian Ocean are assessed against the GEM uniform grid control run, 40-yr ECMWF Re-Analysis (ERA-40), and available observations in terms of regional and global climate and interannual variability. It is found that the variable-resolution configurations realistically simulate global and regional climate over North America with seasonal means and variability generally closer to ERA-40 or observations than the control run. Systematic errors of the control run are still present within the variable-resolution simulations but alleviated to some extent over their respective highly resolved domains. Additionally, there is some evidence of performance deterioration due to the increased resolution. There is little evidence that an increased resolution over the tropical Pacific–eastern Indian Ocean, with better-resolved local processes (e.g., convection and equatorial waves), has a significant impact on the extratropical time mean fields. However, in terms of simulating the Northern Hemisphere atmospheric flow anomaly associated with the dominant mode of sea surface temperature interannual variability in the equatorial eastern Pacific (i.e., El Niño), both stretched configurations have more realistic teleconnection patterns than the control run.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".