Budget study of the internal variability in ensemble simulations of the Canadian Regional Climate Model at the seasonal scale
Bibliographic record
Abstract
[1] Previous investigations with nested regional climate models have revealed that simulations are sensitive to the initial conditions (IC). This results in internal variability (IV) in ensembles of simulations initialized with small differences in IC. In a previous study, a quantitative budget calculation has documented the physical processes responsible for the rapid growth of IV in simulations with the Canadian Regional Climate Model (CRCM). By using an ensemble of 20 simulations performed for the 1993 summer season, we extend the previous study to further our understanding about the physical processes responsible for the maintenance and fluctuations of IV in a seasonal simulation with CRCM. We have identified and quantified various terms in the prognostic budget equations of IV for the potential temperature and the absolute vorticity. For these studied variables, the covariance of fluctuations acting on the gradient of the ensemble mean of variables generally contributes to increasing the IV, indicating that the transport of heat and vorticity is down the gradient of ensemble mean potential temperature and absolute vorticity. The horizontal transport of IV by ensemble mean flow acts as a sink term, the IV transport out of the study domain contributing to reduce the IV. On average in the troposphere and at the seasonal scale, results confirm that there is no trend in IV although it greatly fluctuates in time. Our results also show that IV is a natural phenomenon arising from the chaotic nature of the atmosphere. In a time-averaged sense, the IV budget reduces to a balance between generation and destruction terms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".