Multidecadal trends in aerosol radiative forcing over the Arctic: Contribution of changes in anthropogenic aerosol to Arctic warming since 1980
Bibliographic record
Abstract
Abstract Arctic observations show large decreases in the concentrations of sulfate and black carbon (BC) aerosols since the early 1980s. These near‐term climate‐forcing pollutants perturb the radiative balance of the atmosphere and may have played an important role in recent Arctic warming. We use the GEOS‐Chem global chemical transport model to construct a 3‐D representation of Arctic aerosols that is generally consistent with observations and their trends from 1980 to 2010. Observations at Arctic surface sites show significant decreases in sulfate and BC mass concentrations of 2–3% per year. We find that anthropogenic aerosols yield a negative forcing over the Arctic, with an average 2005–2010 Arctic shortwave radiative forcing (RF) of −0.19 ± 0.05 W m −2 at the top of atmosphere (TOA). Anthropogenic sulfate in our study yields more strongly negative forcings over the Arctic troposphere in spring (−1.17 ± 0.10 W m −2 ) than previously reported. From 1980 to 2010, TOA negative RF by Arctic aerosol declined, from −0.67 ± 0.06 W m −2 to −0.19 ± 0.05 W m −2 , yielding a net TOA RF of +0.48 ± 0.06 W m −2 . The net positive RF is due almost entirely to decreases in anthropogenic sulfate loading over the Arctic. We estimate that 1980–2010 trends in aerosol‐radiation interactions over the Arctic and Northern Hemisphere midlatitudes have contributed a net warming at the Arctic surface of +0.27 ± 0.04 K, roughly one quarter of the observed warming. Our study does not consider BC emissions from gas flaring nor the regional climate response to aerosol‐cloud interactions or BC deposition on snow.
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 | 0.000 | 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".