Bibliographic record
Abstract
Abstract The accuracy of radiation calculations matters for climate simulations. Radiation code assessments typically validate such quantities as radiative fluxes and heating rates. However, it is not clear how these quantities or their uncertainties affect climate sensitivity—the extent of warming driven by the CO2 radiative forcing. Here, we assess the temperature response uncertainty by comparing simulations based on parameterized radiation codes to that based on a benchmark line‐by‐line model, in an idealized global warming (quadrupling CO2) experiment. We present an idea to quantitatively relate the radiative quantities to equilibrium temperature response. We find that when the temperature change is solely driven by the radiative process (a pure radiative adjustment), the temperature change and its uncertainty can be well diagnosed by the proposed method. Under this situation, the temperature response uncertainty is found to result from the uncertainties in both CO2 forcing and the radiative Jacobians. This calls into attention the importance of Jacobians in the radiation code intercomparison and validation. When the temperature change is driven by both radiation and convection (a radiative‐convective adjustment), the temperature change can no longer be predicted by a simple diagnostic equation. Nevertheless, the validation against the benchmark simulation provides an estimate of the temperature response errors that may be attributed to radiation code inaccuracy. We find that such errors may reach several 10th° Kelvin for surface temperature and more than 1° Kelvin for atmospheric temperatures.
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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 | 0.012 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".