Processes controlling the seasonal cycle of Arctic aerosol number and size distributions
Bibliographic record
Abstract
Abstract. Measurements at high-Arctic sites show a strong seasonal cycle in aerosol number and size. The number of aerosols with diameters larger than 20 nm exhibits a maximum in late spring associated with a dominant accumulation mode (0.1 to 1 μm in diameter), and a second maximum in the summer associated with a dominant Aitken mode (10 to 100 nm in diameter). Seasonal-mean aerosol effective diameter ranges from about 180 nm in summer to 260 nm in winter. This study interprets these seasonal cycles with the GEOS-Chem-TOMAS global aerosol microphysics model. We find improved agreement with in-situ measurements of aerosol size at both Alert, Nunavut, and Mt. Zeppelin, Svalbard following model developments that: (1) increase the efficiency of wet scavenging in the Arctic summer and (2) represent coagulation between interstitial aerosols and aerosols activated to form cloud droplets. Our simulations indicate that the dominant summertime Aitken mode is associated with increased efficiency of wet removal, which limits the number of larger aerosols and promotes local new-particle formation. We also find an important role of interstitial coagulation in clouds in the Arctic, which limits the number of Aitken-mode aerosols in the non-summer seasons when direct wet removal of these aerosols is inefficient. Total aerosol number reaches a minimum in October at both Alert and Mt. Zeppelin. Our simulations indicate that this October minimum can be explained by diminishing local new-particle formation, limited transport of pollution from lower latitudes, and efficient wet removal. We recommend that the key processes of aerosol wet removal, interstitial coagulation and new-particle formation be carefully considered in size-resolved aerosol simulations of the Arctic. Uncertainties about these processes, which strongly control the seasonal cycle of aerosol number and size, limit confidence in estimates of aerosol radiative effects on the Arctic climate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".