When Do Subpollen Particles Become Relevant for Ice Nucleation Processes in Clouds?
Bibliographic record
Abstract
Abstract When exposed to sufficiently humid environments, pollen grains burst and release large quantities of small subpollen particles (SPPs) which carry ice nucleating macromolecules. In this study, for the first time we develop a physically based parameterization describing the bursting process of pollen by applying a turgor pressure parameterization and quantify the impact SPPs have on overall ice nucleation in clouds. SPPs are generated from simulated birch pollen emissions over Europe for a 10‐day case study in spring. We found SPP concentrations to surpass pollen grain concentrations by 4–6 orders of magnitude leading to an abundance of biological ice nuclei from SPPs in the range of 10 3 −10 4 m −3 . However, it is found that these concentrations lead to only small changes in hydrometeor number densities and precipitation. Addressing the question when SPPs become relevant for ice nucleation in clouds, we conducted a sensitivity investigation. We find that amplifying ice nucleation efficiency of biological particles by factors greater 100 increases the ice particle numbers by up to 25% ( T ≈ 268 K). Strong reductions show in cloud droplet number concentration and water vapor at these temperatures while water vapor is increasing at 600 m. Overall, we found a net reduction of water in the atmosphere as liquid and particularly water vapor density is reduced, while frozen water mass density increases above 257 K. Findings indicate an alteration of mixed‐phase cloud composition and increased precipitation (up to 6.2%) when SPPs are considered as highly efficient biological ice nuclei.
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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) | 0.000 | 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 | 0.001 |
| 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".