Relationship of cloudiness to near surface temperature over land areas of the Northern Hemisphere
Bibliographic record
Abstract
Relationship of cloudiness to near surface temperature over land areas of the Northern Hemisphere for the past several decades is assessed using the data from surface meteorological weather stations, satellite observations, and the NCEP reanalysis project. The overall cloud relationship to near surface temperature is well represented by near surface humidity and surface conditions. Nighttime cloud-related surface warming decreases with the increase in near surface specific humidity. After cloud longwave-related temperature change and snow cover information are removed, one unit of cloud cover is empirically associated with a surface cooling of 0.59 K. The AMIP-1 models generally were able to reproduce the cold season cloud-temperature relationship, but not for the warm season and for the diurnal cycle. The daytime cloud-related surface cooling over China and the contiguous U.S. generally strengthened, but slightly weakened over Canada and the former USSR during the post WWII period. Since the 1970s a prominent increase in atmospheric humidity has weakened cloud longwave effect on surface temperature (best seen at nighttime) over the extratropical land areas. Significant changes and a general redistribution of cloudiness occurred over the contiguous U.S. and the former USSR (south of 60°N) during the past forty to fifty years. Low cloudiness increased over the contiguous U.S. while it decreased over the former USSR (south of 60°N) Total cloud amount and convective cloud frequency increased in both countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".