Comparison of global datasets of sodium densities in the mesosphere and lower thermosphere from GOMOS, SCIAMACHY and OSIRIS measurements and WACCM model simulations from 2008 to 2012
Bibliographic record
Abstract
Abstract. During the last decade, several limb sounding satellites have measured the global sodium (Na) number densities in the mesosphere and lower thermosphere (MLT). Datasets are now available from Global Ozone Monitoring by Occultation of Stars (GOMOS), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartography (SCIAMACHY) (both on Envisat) and the Optical Spectrograph and InfraRed Imager System (OSIRIS) (on Odin). Furthermore, global model simulations of the Na layer in the MLT simulated by the Whole Atmosphere Community Climate Model, including the Na species (WACCM-Na), are available. In this paper, we compare these global datasets.The observed and simulated monthly averages of Na vertical column densities agree reasonably well with each other. They show a clear seasonal cycle with a summer minimum most pronounced at the poles. They also show signs of a semi-annual oscillation in the equatorial region. The vertical column densities vary from 0. 5 × 109 to 7 × 109 cm−2 near the poles and from 3 × 109 to 4 × 109 cm−2 at the Equator. The phase of the seasonal cycle and semi-annual oscillation shows small differences between the Na amounts retrieved from different instruments. The full width at half maximum of the profiles is 10 to 16 km for most latitudes, but significantly smaller in the polar summer. The centroid altitudes of the measured sodium profiles range from 89 to 95 km, whereas the model shows on average 2 to 4 km lower centroid altitudes. This may be explained by the mesopause being 3 km lower in the WACCM simulations than in measurements. Despite this global 2–4 km shift, the model captures well the latitudinal and temporal variations. The variation of the WACCM dataset during the year at different latitudes is similar to the one of the measurements. Furthermore, the differences between the measured profiles with different instruments and therefore different local times (LTs) are also present in the model-simulated profiles. This capturing of latitudinal and temporal variations is also found for the vertical column densities and profile widths.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".