Energy cycle associated with Inter-member Variability in a large ensemble of simulations of the Canadian RCM (CRCM5)
Bibliographic record
Abstract
In an ensemble of Regional Climate Model \n(RCM) simulations where different members are initialised \nat different times but driven by identical lateral \nboundary conditions, the individual members provide \ndifferent, but equally acceptable, weather sequences. \nIn others words, RCM simulations exhibit the phenomenon \nof Internal Variability (or inter-member variability— \nIV), defined as the spread between members in an \nensemble of simulations. Our recent studies reveal that \nRCM’s IV is associated with energy conversions similar \nto those taking place in weather systems. By analogy \nwith the classical work on global energetics of weather \nsystems, a formulation of an energy cycle for IV has been \ndeveloped that is applicable over limited-area domains. \nPrognostic equations for ensemble-mean kinetic energy \nand available enthalpy are decomposed into contributions \ndue to ensemble-mean variables and those due to \ndeviations from the ensemble mean (IV). Together these \nequations constitute an energy cycle for IV in ensemble \nsimulations of an RCM. A 50-member ensemble of \n1-year simulations that differ only in their initial conditions \nwas performed with the fifth-generation Canadian \nRCM (CRCM5) over an eastern North America domain. \nThe various energy reservoirs of IV and exchange terms \nbetween reservoirs were evaluated; the results show a \nremarkably close parallel between the energy conversions \nassociated with IV in ensemble simulations of RCM and the energy conversions taking place in weather systems \nin the real atmosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".