Diagnosis of cloud amount increase from an analogue model of a "warming -world
Bibliographic record
Abstract
Warming world analogue model diagnosis of total cloud amount trends is reviewed. Using cloud amount records for the continental U. S. A. (published), Canada, including parts of the Arctic (in preparation), Europe (published) and the Indian sub-continent (new results), cloudiness changes have been analyzed in the context of the analogue model which compares records of two contrasting twenty year periods. Cloud amount is found to increase over practically the entire U. S. A., Canada, most of the Indian sub-continent, and parts of Europe in all seasons. These results have been derived for a wide range of climates and considerably strengthen the more tentative findings of Henderson-Sellers (l986a, b) and MeGuffie and Henderson-Sellers (1987) that total cloud amount increases in a warming world. On the other hand, the record of total cloud amount since the 1900s has suffered from changes in observing and reporting practice from differing emphasis on observer training and from time sampling biases. These aspects of the record are considered here in detail. Moreover it must be recognized that the historical record reviewed here is land-based only, contained within the northern hemisphere and excludes many areas especially the tropics and equatorial regions. The results achieved so far could indicate that the current real-world transient experiment in which CO 2 and temperatures are increasing includes a negative feedback on increasing temperatures due to increasing cloud amount. However the very restricted area considered also means that the apparent trend may be much less than global. Specifically results are not inconsistent with numerical model predictions of storm tracks shifted poleward in doubled CO 2 experiments. At the least, the predictions of cloud changes made by numerical models could be re-examined in the light of the results described here.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".