Cosmic rays, aerosol formation and cloud-condensation nuclei: sensitivities to model uncertainties
Bibliographic record
Abstract
Abstract. The flux of cosmic rays to the atmosphere has been reported to correlate with cloud and aerosol properties. One proposed mechanism for these correlations is the "ion-aerosol clear-air" mechanism where the cosmic rays modulate atmospheric ion concentrations, ion-induced nucleation of aerosols and cloud condensation nuclei (CCN) concentrations. We use a global chemical transport model with online aerosol microphysics to explore the dependence of CCN concentrations on the cosmic-ray flux. Expanding upon previous work, we test the sensitivity of the cosmic-ray/CCN connection to several uncertain parameters in the model including primary emissions, Secondary Organic Aerosol (SOA) condensation and charge-enhanced condensational growth. The sensitivity of CCN to cosmic rays increases when simulations are run with decreased primary emissions, but show location-dependent behavior from increased amounts of secondary organic aerosol and charge-enhanced growth. For all test cases, the change in the concentration of particles larger than 80 nm between solar minimum (high cosmic ray flux) and solar maximum (low cosmic ray flux) simulations is less than 0.2 %. The change in the total number of particles larger than 10 nm was larger, but always less than 1 %. The simulated change in the column-integrated Ångström exponent was negligible for all test cases. Additionally, we test the predicted aerosol sensitivity to week-long Forbush decreases of cosmic rays and find that the maximum change in aerosol properties for these cases is similar to steady-state aerosol differences between the solar maximum and solar minimum. These results provide evidence that the effect of cosmic rays on CCN and clouds through the ion-aerosol clear-sky mechanism is limited by dampening from aerosol processes.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".