The Seasonal Variation of the Propagating Diurnal Tide in the Mesosphere and Lower Thermosphere. Part II: The Role of Tidal Heating and Zonal Mean Winds
Bibliographic record
Abstract
A linear mechanistic tidal model is used to understand the mechanisms responsible for the seasonal variation of the propagating diurnal tide in the mesosphere and lower thermosphere simulated in the Canadian Middle Atmosphere Model (CMAM). The linear model uses a spectral approach to represent the horizontal structure of the tidal perturbations and employs dissipative processes that do not depend on season. By constraining the model with the zonal mean zonal winds, zonal mean temperatures, and tidal heating from the CMAM, the relative role of each of these terms is assessed. The linear model is able to reproduce all of the important tidal features found in the CMAM, in particular the semiannual amplitude variation in the lower thermosphere at low latitudes that is seen in observations. From this analysis the effects of both heating and mean winds are found to be responsible for the seasonal variation of the tidal amplitude, while variations in the tidal phase are attributed solely to changes in the mean winds. The strong sensitivity of the tide to the mean winds is the novel result of this study. This sensitivity is attributed to latitudinal shears in the zonal mean easterlies in the summer mesosphere. Although these shears occur on an annual basis, their impact on tidal amplitudes in the lower thermosphere is semiannual as a result of the 6-month shift in seasons between the two hemispheres. Simulations using observational datasets from the Committee on Space Research (COSPAR) International Reference Atmosphere (CIRA) and the High Resolution Doppler Imager (HRDI) reveal significant differences in the resulting tidal structure from that obtained using the CMAM winds, and point to possible deficiencies in these datasets.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".