Impact of changing Arctic sea ice extent, sea ice age, and snow depth on sea salt aerosol from blowing snow and the open ocean for 1980-2017
Bibliographic record
Abstract
We evaluate the effects of rapidly changing Arctic sea ice conditions on sea salt aerosol (SSA) produced by oceanic wave-breaking and the sublimation of wind-lofted salty blowing snow on sea ice. We use the GEOS-Chem chemical transport model to assess the influence of changing extent of the open ocean, multi-year sea ice, first-year sea ice (FYI), and snow depths on SSA emissions for 1980-2017. We combine snow depths from the Lagrangian snow-evolution model (SnowModel-LG) together with an empirically-derived snow salinity function of snow depth to derive spatially and temporally varying snow surface salinity over Arctic FYI. We find that snow surface salinity on Arctic sea ice is increasing at a rate of ~30% decade -1 and SSA emissions are increasing at a rate of 7-9% decade -1 during the cold season (November – April). As a result, simulated SSA mass concentrations over the Arctic increased by 8-12% decade -1 in the cold season for 1980-2017. Blowing snow SSA accounts for more than 75% of this increase. During the warm season (May – October), sea ice loss results in a 12-14% decade -1 increase in SSA emissions due to increasing open ocean emissions. Observations of SSA mass concentrations at Alert, Canada display positive trends during the cold season (10-12% decade -1 ), consistent with our pan-Arctic simulations. During fall, Alert observations show a negative trend (-18% decade -1 ), due to locally decreasing wind speeds and thus lower open ocean emissions. These significant changes in SSA concentrations could potentially affect past and future bromine explosions and Arctic climate feedbacks.
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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 | 0.001 |
| 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 | 0.001 | 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".