How Well Are the Distribution and Extreme Values of Daily Precipitation over North America Represented in the Community Climate System Model? A Comparison to Reanalysis, Satellite, and Gridded Station Data
Bibliographic record
Abstract
Abstract An intercomparison of the distribution and extreme values of daily precipitation between the National Center for Atmospheric Research Community Climate System Model, version 4 (CCSM4) and several observational/reanalysis data sources are conducted over the contiguous United States and southern Canada. The use of several data sources, from gridded station, satellite, and reanalysis products, provides a measure of errors in the reference datasets. An examination of specific locations shows that the global climate model (GCM) distributions closely match the observations along the East and West Coasts, with larger discrepancies in the Great Plains and Rockies. In general, the distribution of model precipitation is more positively skewed (more light and less heavy precipitation) in the Great Plains and the eastern United States compared to gridded station observations, a recurring error in GCMs. In the Rocky Mountains the GCMs generally overproduce precipitation relative to the observations and furthermore have a more negatively skewed distribution, with fewer lower daily precipitation values relative to higher values. Extreme precipitation tends to be underestimated in regions and time periods typically characterized by large amounts of convective precipitation. This is shown to be the result of errors in the parameterization of convective precipitation that have been seen in previous model versions. However, comparison against several data sources reveals that model errors in extreme precipitation are approaching the magnitude of the disparity between the reference products. This highlights the existence of large errors in some of the products employed as observations for validation purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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 teacher head, 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".