Neglect by GCMs of subgrid‐scale horizontal variations in cloud‐droplet effective radius: A diagnostic radiative analysis
Bibliographic record
Abstract
Abstract Output from a global climate model (GCM) that employed a low‐resolution two‐dimensional cloud‐system‐resolving model (CSRM) in each column is used to assess the radiative impact of neglecting subgrid‐scale horizontal variations in cloud‐droplet effective radius re. For this diagnostic study, only liquid‐phase variations in re are addressed; the ice‐cloud particle distributions are assumed to be constant. For reference calculations, values of re in the CSRM cells are computed assuming that the droplet‐number concentration Ncld and the effective variance of droplet‐size distribution are constant in a GCM cell. The independent‐column approximation is used to produce flux profiles for each GCM column. Three alternative methods of setting horizontally‐invariant re are examined, each of which resemble how re is set in one‐dimensional radiative‐transfer models. Relative to the reference calculations, the other methods lead to positive spurious radiative forcings at the surface and at the top of the atmosphere. These stem from overestimation of optical‐depth variability and, thus, reduced short‐wave albedo of clouds. Globally averaged, these forcings range from 1 W m−2 to 3 W m−2, with zonal‐mean biases reaching almost 15 W m−2. The most severe biases arise from use of constant values of re over the land and the ocean. In addition, radiative effects due to unacknowledged uncertainty in Ncld (or re) are assessed. It is shown that ad hoc, but not outlandish, estimates of unbiased uncertainty in Ncld impart biases on estimates of the earth's solar‐radiation budget (tantamount to a spurious radiative forcing). These arise through the chain of nonlinear relations that link Ncld to solar radiative transfer. Copyright © 2004 Royal Meteorological Society.
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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 | 0.003 |
| 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 | 0.000 |
| 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".