The Sensitivity of the Radiation Budget in a Climate Simulation to Neglecting the Effect of Small Ice Particles
Bibliographic record
Abstract
Abstract The sensitivity of the atmospheric radiation budget to ignoring small ice particles (D ≤ 100 μm) in parameterization of the mean effective size of ice particles was investigated by using the Canadian Centre for Climate Modelling and Analysis (CCCma) third-generation general atmospheric circulation model (AGCM3). The results indicate that small ice particles play two crucial roles in the radiative transfer that influence the simulated climate. First, they inhibit the IR radiation from escaping to space and, second, they enhance the scattering of solar radiation. On average, these two effects tend to partially cancel each other out. However, based on AGCM simulations, the small ice crystals make clouds more opaque to IR radiation. Generally, 5-yr seasonally averaged GCM results suggest that the strongest anomalies in outgoing longwave radiation (OLR) are found in the Tropics, reaching 15 to 25 W m−2 in areas where cold high cirrus anvil clouds are prevalent. The global average change in net cloud radiative forcing was 2.4 W m−2 in June–August (JJA) and 1.7 W m−2 in December–February (DJF). The change in globally averaged 5-yr mean cloud forcing was close to 1.9 W m−2. When the small particles were included, the globally averaged 5-yr mean precipitation decreased by about 8%, but cloudiness increased only slightly (by 2%). The 5-yr averaged global mean surface (screen) temperature also increased slightly (about 0.2°C) when the small ice particles were included.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".