Overlap of fractional cloud for radiation calculations in GCMs: A global analysis using CloudSat and CALIPSO data
Bibliographic record
Abstract
Assumptions made by global climate models (GCMs) regarding vertical overlap of fractional amounts of clouds have significant impacts on simulated radiation budgets. A global survey of fractional cloud overlap properties was performed using 2 months of cloud mask data derived from CloudSat‐CALIPSO satellite measurements. Cloud overlap was diagnosed as a combination of maximum and random overlap and characterized by vertically constant decorrelation length cf * . Typically, clouds overlap between maximum and random with smallest cf * (medians → 0 km) associated with small total cloud amounts , while the largest cf * (medians ∼3 km) tend to occur at near 0.7. Global median cf * is ∼2 km with a slight tendency for largest values in the tropics and polar regions during winter. By crudely excising near‐surface precipitation from cloud mask data, cf * were reduced by typically <1 km. Median values of cf * when Sun is down exceed those when Sun is up by almost 1 km when cloud masks are based on radar and lidar data; use of radar only shows minimal diurnal variation but significantly larger cf * . This suggests that sunup inferences of cf * might be biased low by solar noise in lidar data. Cloud mask cross‐section lengths L of 50, 100, 200, 500, and 1000 km were considered. Distributions of cf * are mildly sensitive to L thus suggesting the convenient possibility that a GCM parametrization of cf * might be resolution‐independent over a wide range of resolutions. Simple parametrization of cf * might be possible if excessive random noise in , and hence radiative fluxes, can be tolerated. Using just cloud mask data and assuming a global mean shortwave cloud radiative effect of −45 W m −2 , top of atmosphere shortwave radiative sensitivity to cf * was estimated at 2 to 3 W m −2 km −1 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".