A comparison of two representations of subgrid‐scale cloud structure in a global model: radiative effects as a function of cloud characteristics
Bibliographic record
Abstract
Two approaches to account for the radiative impacts of subgrid‐scale variability of cloud in a general circulation model (GCM) are compared: (i) deterministic reduction of cloud optical depth imbedded within the radiative transfer scheme and (ii) stochastic subcolumns employed with the Monte Carlo Independent Column Approximation (McICA). This article analyzes impacts, as a function of cloud phase and cloud fraction, due to replacement of deterministic method (which is used currently in the GCM) with the McICA method, as well as the introduction of cloud‐water horizontal inhomogeneity and changes to the description of cloud vertical overlap. The largest radiative effects are produced by changing horizontal inhomogeneity of cloud water, which, when enhanced, generally decreases cloud albedo and emissivity, with the exception of some ice clouds, the albedo of which increases. Reducing the extent to which clouds overlap vertically has smaller and opposite effects relative to increasing horizontal inhomogeneity, with the exception of some ice clouds, where both effects have the same sign. These effects are, however, less pronounced than the increases to both cloud albedo and emissivity that stem from replacement of the deterministic optical depth reduction method by McICA. In essence, deterministic reduction of cloud optical depth has a more aggressive impact on radiative transfer than does McICA's stochastic sampling of horizontal inhomogeneity. When the McICA methodology is applied interactively in the GCM, both cloud fraction and water content for low‐level clouds are reduced relative to the deterministic method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".