An improved and extended parameterization of the CO <sub>2</sub> 15 µm cooling in the middle/upper atmosphere
Bibliographic record
Abstract
Abstract. The radiative infrared cooling of CO2 in the middle atmosphere, where it emits under non-Local Thermodynamic Equilibrium (non-LTE) conditions, is a crucial contribution to the energy balance of this region and hence to establishing its thermal structure. The non-LTE computation is too CPU time-consuming to be fully incorporated in climate models and hence it is parameterized. The most used parameterization of the CO2 15 μm cooling for the Earth's middle and upper atmosphere was developed by Fomichev et al. (1998). The valid range of this parameterization with respect to CO2 volume mixing ratios (VMR) is, however, exceeded by the CO2 of several scenarios considered in the Coupled Climate Model Intercomparison Projects; in particular, the abrupt-4xCO2 experiment. Therefore, an extension, as well as an update, of that parameterization is both needed and timely. In this work, we present an update of the parameterization developed by Fomichev et al. (1998), which now covers CO2 volume mixing ratios in the lower atmosphere from ~0.5 to over 10 times the CO2 pre-industrial value of 284 ppmv (i.e., 150 ppmv to 3000 ppmv). Furthermore, it is improved by using a more contemporary CO2 line list and collisional rates that affect the CO2 cooling rates. Overall, accuracy is improved when tested against reference line-by-line calculations and by using measured global temperature profiles of the middle atmosphere. On average the errors are below 0.5 K day-1 for the present-day and lower CO2 VMRs. The errors increase to ~1–2 K day-1 at altitudes between 100–120 km for CO2 concentrations of two to three times the preindustrial values. For very high CO2 concentrations (four to ten times the pre-industrial abundances) the errors are below ~1 K day-1 for most regions and conditions, except at 110–120 km where the parameterization overestimates them by ~1.5 %. When applied to a large dataset of global (pole-to-pole and four seasons) measured temperature profiles, the errors of the parameterization are generally below 0.5 K day-1, except between 5⋅10-3 hPa and 3⋅10-4 hPa (~85–95 km), where they can reach biases of 1–2 K day-1. However, for elevated stratopause events, it underestimates the cooling rates by 3–7 K day-1 (~10 %) at altitudes of 80–100 km and the parameterized cooling rates show a large spread when compared to reference calculations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".