Assessment of the dehydration‐greenhouse feedback over the Arctic during February 1990
Bibliographic record
Abstract
Abstract The effect of pollution‐derived sulfuric acid aerosols on the aerosol‐cloud‐radiation interactions is investigated over the Arctic for February 1990. Observations suggest that acidic aerosols can decrease the heterogeneous nucleation rate of ice crystals and lower the homogeneous freezing temperature of haze droplets. On the basis of these observations, we hypothesize that the cloud thermodynamic phase is modified in polluted air mass (Arctic haze). Cloud ice number concentration is reduced, thus promoting further ice crystal growth by the Bergeron–Findeisen process. Hence, ice crystals reach larger sizes and low‐levkel ice crystal precipitation from mixed‐phase clouds increases. Enhanced dehydration of the lower troposphere contributes to decreased water vapour greenhouse effect and cools the surface. A positive feedback is created between surface cooling and air dehydration, accelerating cold air production. This process is referred to as the dehydration‐greenhouse feedback (DGF). Simulations performed using an Arctic regional climate model for February 1990 are used to assess the potential effect of the DGF on the Arctic climate during February. Results show that the DGF has an important effect on cloud, atmospheric dehydration, and temperature over the Central and Eurasian Arctic, which is the coldest part of the Arctic. Cloud ice is significantly reduced and the total atmospheric water path is decreased by as much as 12%. This results in a surface cooling ranging between 0 and − 3 K. Moreover, the lower tropospheric cooling over the Eurasian and Central Arctic strengthens the atmospheric circulation at the upper level, thus increasing the aerosol transport from the mid‐latitudes and enhancing the DGF. Over warmer areas, the increased aerosol concentration (caused by the DGF) leads to longer cloud lifetime, which contributes to warm these areas. Copyright © 2006 Royal Meteorological Society
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".