An improved and extended parameterization of the CO <sub>2</sub> 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
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 into climate models, and hence it is parameterized. The most used parameterization of the CO2 15 µm cooling for 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 (VMRs) is, however, exceeded by the CO2 of several scenarios considered in the Coupled Climate Model Intercomparison Projects, in particular the abrupt-4×CO2 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 that parameterization that 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 to 3000 ppmv). Furthermore, it is improved by using a more contemporary CO2 line list and the collisional rates that affect the CO2 cooling rates. Overall, its accuracy is improved when tested for the reference temperature profiles as well as for measured temperature fields covering all expected conditions (latitude and season) of the middle atmosphere. The errors obtained for the reference temperature profiles are below 0.5 K d−1 for the present-day and lower CO2 VMRs. Those errors increase to ∼1–2K d−1 at altitudes between 110 and 120 km for CO2 concentrations of 2 to 3 times the pre-industrial values. For very high CO2 concentrations (4 to 10 times the pre-industrial abundances), those errors are below ∼1 K d−1 for most regions and conditions, except at 107–135 km, where the parameterization overestimates them by ∼1.2 %. These errors are comparable to the deviation of the non-LTE cooling rates with respect to LTE at about 70 km and below, but they are negligible (several times smaller) above that altitude. When applied to a large dataset of global (pole to pole and four seasons) temperature profiles measured by MIPAS (Michelson Interferometer for Passive Atmospheric Spectroscopy) (middle- and upper-atmosphere mode), the errors of the parameterization for the mean cooling rate (bias) are generally below 0.5 K d−1, except between 5×10-3 and 3×10-4 hPa (∼85–98 km), where they can reach biases of 1–2 K d−1. For single-temperature profiles, the cooling rate error (estimated by the root mean square – rms – of a statistically significant sample) is about 1–2 K d−1 below 5×10-3 hPa (∼85 km) and above 2×10-4 hPa (∼102 km). In the intermediate region, however, it is between 2 and 7 K d−1. For elevated stratopause events, the parameterization underestimates the mean cooling rates by 3–7 K d−1 (∼10 %) at altitudes of 85–95 km and the individual cooling rates show a significant rms (5–15 K d−1). Further, we have also tested the parameterization for the temperature obtained by a high-resolution version of the Whole Atmosphere Community Climate Model (WACCM-X), which shows a large temperature variability and wave structure in the middle atmosphere. In this case, the mean (bias) error of the parameterization is very small, smaller than 0.5 K d−1 for most atmospheric layers, reaching only maximum values of 2 K d−1 near 5×10-4 hPa (∼ 96 km). The rms has values of 1–2 K d−1 (∼20 %) below ∼2×10-2 hPa (∼80 km) and values smaller than 4 K d−1 (∼2 %) above 10−4 hPa (∼105 km). In the intermediate region between ∼5×10-3 and ∼2×10-4 hPa (85–102 km), the rms is in the range of 5–12 K d−1. While these values are significant in percentage at ∼5×10-3–5×10-4 hPa, they are very small above ∼5×10-4 hPa (96 km). The routine is very fast, taking (1.5–7.5) ×10-5 s, depending on the extension of the atmospheric profile, the processor and the Fortran compiler.
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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 | 0.001 | 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".