Observational constraints on ozone radiative forcing from the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP)
Bibliographic record
Abstract
Abstract. We use simultaneous observations of ozone and outgoing longwave radiation (OLR) from the Tropospheric Emission Spectrometer (TES) to evaluate ozone distributions and radiative forcing simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES for 2005–2010. These biases lead to substantial differences in ozone instantaneous radiative forcing between TES and the ACCMIP simulations. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean has a low bias in the SH tropics of up to 100 m W m−2 locally and a global low bias of 35 ± 44 m W m−2 relative to TES. Combining ACCMIP preindustrial ozone and the TES present-day ozone, we calculate an observationally constrained estimate of tropospheric ozone radiative forcing (RF) of 399 ± 70 m W m−2, which is about 7% higher than using the ACCMIP models alone but with the same standard deviation (Stevenson et al., 2012). In addition, we explore an alternate approach to constraining radiative forcing estimates by choosing a subset of models that best match TES ozone, which leads to an ozone RF of 369 ± 42 m W m−2. This estimate is closer to the ACCMIP ensemble mean RF but about a 40% reduction in standard deviation. These results point towards a profitable direction of combining observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".