Radiative and Cloud Microphysical Effects of Forest Fire Smoke over North America and Siberia
Bibliographic record
Abstract
Aerosol affects climate both through direct radiative effects and by indirect effects on cloud development. Absorbing aerosols have additional influence on the vertical temperature profile of the atmospheric column. Radiative effects of smoke are studied for the case of a Canadian smoke plume that blanketed the U.S. mid-Atlantic seaboard. Optical properties derived from aircraft in situ measurements demonstrate that the smoke formed a layer with a base 2 km above the surface, and absorptive heating in this layer could have strengthened and maintained the subsidence inversion responsible for the layer structure. An optical model of the smoke formed from a blend of aircraft and AERONET measurements allows retrieval of the smoke aerosol by satellite, so that comparisons are possible to measurements made by any instrument in the region. For this case an optical model based purely on AERONET measurements provides the best satellite retrieval of optical depth, but a model based mainly on aircraft measurements agreed best with spectrum wide-forcing measurements, demonstrating the dangers of a simple optical model for all retrievals. A study done in the Amazonian burning season demonstrates that sun/observation geometry is useful to control bias from shadowed and illuminated portions of clouds. Sub-pixel mixing of cloud and aerosol also produces bias that is minimized for optically thick clouds. Since such biases can never be fully eliminated, the only valid study is a comparison of two data sets with equivalent geometry and so, presumably, equal bias. Canada and Siberia were chosen so that forested areas are compared at the same latitudes. Summertime Canadian aerosol is primarily smoke, while Europe contributes a great deal of sulfate to Siberia aerosol. The average cloud droplet size was significantly smaller in Siberia, as expected from the higher sulfate load with greater activity as cloud condensation nuclei (CCN). The aerosol indirect effect on cloud microphysics increases with aerosol loading in both regions, but much more so in Canada. This is attributed to a large sulfate background in Siberia, so the addition of smoke makes a smaller percentage change to the amount of cloud CCN.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".