The Climatic Impacts of a Satellite‐Based Parameterization of the Wegener‐Bergeron‐Findeisen Process for Large‐Scale Models
Bibliographic record
Abstract
Abstract A satellite‐based temperature‐dependent parameterization of the Wegener‐Bergeron‐Findeisen (WBF) process that takes into account the subgrid‐scale variability of cloud thermodynamic phase within mixed‐phase clouds is developed and implemented in version 5.3 of the Community Atmosphere Model (CAM5.3). Its impact on cloud microphysical and macrophysical properties in experiments with prescribed sea surface temperature and sea ice concentrations as well as the cloud feedback response to a global warming perturbation is investigated. The parameterization significantly improves overestimates in the mass of ice within mixed‐phase clouds and ice effective radius relative to satellite observations, the former being superior to tuning the WBF process with a multiplicative constant. The parameterization also reduces overall biases in cloud fraction with respect to satellite observations, however, is due to compensating biases in existing simulated low biases in low‐level cloud cover and new increased biases in non‐low‐level cloud cover. The increased bias in non‐low‐level cloud cover is due to decreases in the rate of autoconversion of cloud ice that is a side effect of the WBF parameterization. While the WBF parameterization can significantly impact the magnitude of model biases in cloud properties and the cloud feedback, it does not significantly change their spatial distribution. Before observational constraints on WBF process rates become available, it is recommended that temperature‐dependent scalings of the WBF process are used to account for the subgrid‐scale variability of cloud phase rather than a constant scaling parameter as the former type of parameterization can more realistically simulate cloud properties relative to satellite observations.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".