Radiative effects of black carbon in the Arctic due to recent extreme summer fires
Bibliographic record
Abstract
Black carbon (BC) affects the Arctic climate via aerosol‒radiation‒cloud interaction and snow/ice albedo feedback. Fires have become a substantial source of the Arctic BC in recent years, while the radiative effects of BC in the Arctic due to the recent extreme fires remain unclear. In this study, the atmospheric and snow radiative forcing of BC in the Arctic due to the extreme fires in summer 2019 were investigated based on numerical simulations, and the effects on meteorological variables and snow albedo were explored. Biomass burning BC in summer 2019 caused negative radiative forcing at the bottom of the atmosphere in Greenland and the central Arctic Ocean, and it caused positive radiative forcing in Europe, central Siberia, and northern Canada, with values that can reach −9 W/m 2 and 18 W/m 2 , respectively. The radiative forcing was spatially heterogeneous, which was mainly induced by the dominant role of semi-direct and indirect radiative effects of BC related to cloud changes. The air temperature in the higher troposphere increased in the central Arctic Ocean and Greenland, and the near-surface air temperature increased in Europe, central Siberia, and northern Canada. The responses of wind field and relative humidity were mainly linked with the air temperature changes, and the cyclone activity anomaly can be observed in the central Arctic. Biomass burning BC caused positive snow radiative forcing in Greenland of 0.4–1.4 W/m 2 , and the maximum snow albedo reduction was about 0.005. Overall, this study highlights the importance of BC from fires on the Arctic climate.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".