Using Arctic field data and remote sensing BrO data to constrain blowing snow sea salt aerosol production parameterizations
Bibliographic record
Abstract
Field evidence has confirmed a new sea salt aerosol (SSA) source on sea ice, which may significantly affect polar boundary layer chemistry and polar winter climate. While the SSA production rate from blowing snow has been previously parameterised (Yang et al., 2008) and then validated by measurements at both Poles, some key parameters involved are not yet fully constrained, leading to uncertainties when using numerical models to compare with field measurements and assess their environmental and climate impacts. In this presentation, we focus on two key parameters: blowing snow size distribution and snow salinity, which determine SSA production in number and size, respectively. We aim to constrain these factors using the latest field data, supported by remote sensing BrO data and modelling. Blowing snow particles typically follow a two-parameter gamma distribution function with shape factor (alpha) and scaling factor (beta) varying over a large range. However, our recent work focusing on the Arctic Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition data showed that at a given height, beta values increase with wind speeds, while alpha gradually approach a constant value of 1.9 at higher wind speeds (e.g. larger than 10 m/s). This is the first time that we derive such a relationship for blowing snow, which further affirms the aerosol production mechanism from blowing snow and helps elucidate the underlying processes involved. Accordingly, we parameterised the blowing snow particle size distribution as a function of wind speed, accounting for variable wind speeds during storms. In addition, supported by a chemistry transport model (p-TOMCAT), we examined the sensitivities of SSA mass and reactive bromine release rate (in association with the SSA production) to representative snow salinities derived from observations in the central Arctic and coastal regions (at Eureka, Canada). Mean winter/springtime snow salinities that best represent the Arctic were derived by comparing the modelled BrO with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and air-based satellite-based GOME-2 BrO data at Svalbard and Eureka.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".