Assessment of summer extremes and climate variability over the north‐east of North America as simulated by the Canadian Regional Climate Model
Bibliographic record
Abstract
Abstract The present study focuses on the evaluation and comparison of the ability of two versions of the Canadian Regional Climate Model (CRCM) driven by re‐analyses (NCEP–NCAR) to reproduce the observed extremes and climate variability in summer (1961–1990). The analysed variables are daily precipitation, minimum and maximum temperatures over three regions located in north‐eastern North America that are characterized by different topography and observation density. The validation has been performed with multiple climate extreme indices characterizing the frequency, intensity and duration of precipitation and temperature events. The assessment of the ability of the CRCM is done through an in‐depth analysis of the statistical distribution, performance scores and interannual variability of extreme indices. The reference database has been constructed by kriging the daily observed data from local meteorological stations onto the CRCM 45‐km grid. The vast majority of results over the three regions show that, with respect to the previous (i.e. 3.7.1) CRCM version, the latest version (4.1.1) improves in general the simulated extreme events. In particular, the intensity of extreme hot summer temperature, diurnal temperature range, wet days occurrence, seasonal dry spell, and to a lesser extent extreme cold summer temperature and heavy rainfall. The study suggests that improvements in the simulated extremes in the latest version are due mainly to the introduction of the new land surface scheme (CLASS 2.7), with a more sophisticated representation of the soil moisture content. This suggests the importance of surface processes parameterization as a potential cause of errors in simulated extremes. Copyright © 2011 Royal Meteorological Society
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".