Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions
Bibliographic record
Abstract
Commonly termed “added value”, the additional regional details gained by high-resolution regional climate models (RCMs) over the coarser resolution reanalysis driving data are often indistinguishable at the 0.44° grid mesh computationally affordable large CORDEX domains. In an attempt to highlight the benefits of finer resolutions to study the RCM added value, five North American weather phenomena are evaluated in RCM hindcast simulations using grid meshes of 0.44°, 0.22° and 0.11° with available observations. The results show that the orographic precipitation on the west coast of North America is enhanced and more realistic, with two distinct rain bands in the finer resolution simulation. The spatial distribution of precipitation in August and the high frequency of summer precipitation extremes over southwestern United States reveal that the North American monsoon is improved with increasing resolution. Only the finer RCM simulation shows skill at producing snowbelts around the Great Lakes by capturing lake-effect snow. A comparison of wind roses in the St. Lawrence River Valley indicates that only the finer RCM simulation is able to reproduce wind channeling by resolving complex orography. Finally, the simulation of the summer land-sea breezes by the RCM simulations leads to added value in the diurnal cycle of precipitation over the Florida peninsula and the Caribbean islands. Overall, the almost systematic improvements of the finer resolution simulations suggest that higher resolutions, only computationally affordable over smaller domains, might get a higher priority to promote RCM added value.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".