Reproducible and Irreproducible Components in Ensemble Simulations with a Regional Climate Model
Bibliographic record
Abstract
Abstract High-resolution limited-area models (LAMs) have been widely employed to downscale coarse-resolution climate simulations or objective analyses. The growing evidence that LAM climate statistics can be sensitive to initial conditions suggests that a deterministic verification of LAM solutions in terms of finescale atmospheric features might be misguided. In this study a 20-member ensemble of LAM integrations with perturbed initial conditions, driven by NCEP–NCAR reanalyses, is conducted for a summer season over a midlatitude domain. Ensemble simulations allow for the separation of the downscaled information in two parts: a unique, reproducible component associated with lateral-boundary and surface forcing, and an irreproducible component associated with internal variability. The partition in the reproducible and irreproducible components and their seasonal statistics is examined as a function of horizontal length scale, geographical position within the domain, height, and weather episodes during the season. The scale analysis of time-dependent model variables shows that, at scales smaller than a few hundred kilometers, the irreproducible component dominates, on average, the model solution, implying that the downscaled information at these scales is mainly in stochastic form. The constraint exerted by the surface forcing on the internal variability is weak. For seasonal averages, the reproducible component dominates at all scales, although for precipitation the reproducible and irreproducible components are of the same order of magnitude at scales smaller than 150 km. These results imply a need for a probabilistic approach to LAM climate simulations and their verification, especially for shorter integration times, from months to seasons.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".