From the weather to the climate: a dimensional transition?
Bibliographic record
Abstract
Based on anisotropic space-time turbulence theory, on lidar and satellite radiances, on in situ spectra of temperature, humidity and wind measurements, and on numerical models of the atmosphere as well as reanalyses, we propose an objective basis upon which to distinguish the weather from the climate. We show that the latter accurately follow the predictions of multiplicative cascade models up to about 7- 10 days. This marks the beginning of a weather/climate transition region which extends up to the cascade outer scale (time T) of about 20- 30 days (depending somewhat on the atmospheric field), after which the climate regime begins. We bolster this interpretation by empirically constructing space-time ("Stommel") diagrammes; we obtain near linear relations between time and (horizontal) space and theoretically predicted power law relations between the vertical and time up until the end of the weather regime (10000 km in the horizontal, 10 km in the vertical, 10 days in time). Going beyond the weather regime to time scales higher than T, we see that the spectra flatten out into a "spectral plateau". Using multiplicative space-time cascade models over scales much longer than T, we find that the flattening is nearly as expected and is caused by the transition from a full space-time weather process at scales below T to an increasingly time only type process at larger scales. Further evidence for this "dimensional transition" is the empirical finding that the same type of statistical variability (i.e. probability distributions) persists up to about a year in in situ temperature, humidity and wind statistics and up to tens of years in the 20th Century reanalyses. For long enough periods however we find that new scaling climate processes begin to dominate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".