Land‐atmosphere coupling over North America in CRCM5
Bibliographic record
Abstract
Abstract Land‐atmosphere coupling and its impact on extreme precipitation and temperature events over North America are studied using the fifth generation of the Canadian Regional Climate Model (CRCM5). To this effect, two 30 year long simulations, spanning the 1981–2010 period, with and without land‐atmosphere coupling, have been performed with CRCM5, driven by the European Centre for Medium‐Range Weather Forecasts reanalysis at the boundaries. In the coupled simulation, the soil moisture interacts freely with the atmosphere at each time step, while in the uncoupled simulation, soil moisture is replaced with its climatological value computed from the coupled simulation, thus suppressing the soil moisture‐atmosphere interactions. Analyses of the coupled and uncoupled simulations, for the summer period, show strong soil moisture‐temperature coupling over the Great Plains, consistent with previous studies. The maxima of soil moisture‐precipitation coupling is more spread out and covers the semiarid regions of the western U.S. and parts of the Great Plains. However, the strength of soil moisture‐precipitation coupling is found to be generally weaker than that of soil moisture‐temperature coupling. The study clearly indicates that land‐atmosphere coupling increases the interannual variability of the seasonal mean daily maximum temperature in the Great Plains. Land‐atmosphere coupling is found to significantly modulate selected temperature extremes such as the number of hot days, frequency, and maximum duration of hot spells over the Great Plains. Results also suggest additional hot spots, where soil moisture modulates extreme events. These hot spots are located in the southeast U.S. for the hot days/hot spells and in the semiarid regions of the western U.S. for extreme wet spells. This study thus demonstrates that climatologically wet/dry regions can become hot spots of land‐atmosphere coupling when the soil moisture decreases/increases to an intermediate transitional level where evapotranspiration becomes moisture sensitive and large enough to affect the climate.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".