Sensitivity studies of cirrus clouds formed by heterogeneous freezing in the ECHAM GCM
Bibliographic record
Abstract
Cirrus clouds can form by homogeneous and heterogeneous ice nucleation mechanisms at temperatures below 235 K. Here we evaluate the effectiveness of heterogeneous freezing versus homogeneous freezing using a newly developed parameterization of heterogeneous freezing that is restricted to immersion freezing as the most likely pathway for heterogeneous ice formation in cirrus conditions [ Kärcher and Lohmann, 2003 ]. In addition to a reference simulation considering homogeneous nucleation with temperature‐dependent freezing thresholds, we discuss two idealized model experiments. We conduct a scenario that hypothetically assumes that the aerosol particles available for homogeneous freezing could act as freezing nuclei commencing freezing at 130% with respect to ice and contrast that by a scenario that only considers black carbon and mineral dust as immersion nuclei with the same freezing relative humidity of 130%. These idealized simulations serve to delimit possible climate responses. If the number of freezing nuclei is limited by the number of black carbon and dust aerosols, then heterogeneous freezing results in fewer ice crystals than formed by homogeneous freezing. These fewer ice crystals grow more readily to precipitation size and with that increase the global mean precipitation, decrease the ice water path, and trap less outgoing longwave radiation at the top of the atmosphere.
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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 | 0.004 | 0.010 |
| 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 | 0.002 |
| 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".