The vertical distribution of ozone instantaneous radiative forcing from satellite and chemistry climate models
Bibliographic record
Abstract
[1] We evaluate the instantaneous radiative forcing (IRF) of tropospheric ozone predicted by four state-of-the-art global chemistry climate models (AM2-Chem, CAM-Chem, ECHAM5-MOZ, and GISS-PUCCINI) against ozone distribution observed from the NASA Tropospheric Emission Spectrometer (TES) during August 2006. The IRF is computed through the application of an observationally constrained instantaneous radiative forcing kernels (IRFK) to the difference between TES and model-predicted ozone. The IRFK represent the sensitivity of outgoing longwave radiation to the vertical and spatial distribution of ozone under all-sky condition. Through this technique, we find total tropospheric IRF biases from −0.4 to + 0.7 W/m2 over large regions within the tropics and midlatitudes, due to ozone differences over the region in the lower and middle troposphere, enhanced by persistent bias in the upper troposphere-lower stratospheric region. The zonal mean biases also range from −30 to +50 mW/m2 for the models. However, the ensemble mean total tropospheric IRF bias is less than 0.2 W/m2 within the entire troposphere.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".