Evaluation of the direct and indirect radiative and climate effects of aerosols over the western Arctic
Bibliographic record
Abstract
From the observations of recent years, there is still not enough evidence to verify the Arctic warming as most global circulation models (GCMs) suggested. This study is dedicated to quantifying the aerosol effect on the Arctic climate change by Northern Aerosol Regional Climate Model (NARCM). The direct and indirect radiative and climate effects of aerosols such as Arctic haze sulfate, black carbon, sea salt, organics, and dust have been evaluated from our NARCM simulations. Within the Arctic Regional Climate Model Intercomparison Project (ARCMIP) our model simulations have been directly compared with the enhanced observation data sets such as the Surface Heat Budget of the Arctic Ocean (SHEBA) and the Atmospheric Radiation Measurement (ARM) in the time period from October 1997 to September 1998. Results show that the climate effects of aerosols strongly depend on the aerosol composition. The surface radiative forcing of pure sulfate aerosols which includes the direct and indirect components reaches up to −7.2 W/m2 in annual mean. The climate responses to radiative forcing of pure sulfate and five kinds of aerosols together are amazingly different. The impacts of aerosols present strong seasonal cycle. In comparison with observations we find the simulation with five kinds of aerosols can better represent the surface temperature from observation. The aerosol radiative and microphysical effects must be taken into account in order to better simulate and predict the change of energy and water cycle occurring in polar climate system.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".