Long-term changes in mesospheric wind and wave estimates based on radar observations in both hemispheres
Bibliographic record
Abstract
Several studies (Banerjee et al. (2020) and before that Sun et al. (2014)) found a trend reversal between winter and summer circulation in the southern hemisphere around 2000 in the middle atmosphere. One may argue that the negative trend after 2000 is due to the CO2-induced change in stratospheric dynamics. However, Ramesh et al. (2020), using the newest WACCM6 simulation and a multiple linear regression model, confirmed that the negative trend in the stratosphere after 2000 can be attributed to ozone recovery. Here we investigate how stratospheric trends relate to trends in the mesosphere and lower thermosphere (MLT) dynamics. Using the adaptive spectral filtering (ASF) method (Stober et al., 2021), we study long-term changes in mesospheric wind and planetary and gravity wave estimates of meteor radar stations in the northern (NH: Collm, Kiruna, Sodankyla, CMOR) and southern (SH: Rio Grande, Davis, Rothera) hemisphere, respectively, for the altitude range of 80–100 km. Linear trends have been estimated (from monthly means calculated from the preprocessed original data using ASF) by the Theil–Sen estimator (Theil, 1950; Sen, 1968). The robustness of our fitting method is assessed in terms of spurious trends due to, e.g., high autocorrelation of relatively short time series. The long-term changes are validated in two whole-atmosphere models, namely, GAIA and WACCMX-SD (both nudged in the stratosphere). While both models reveal issues reproducing basic climatology in the mesosphere, GAIA fairly reproduces the trends captured by the meteor radars. Finally, we conclude that the ozone recovery effects in the SH stratosphere influence the dynamics in MLT via gravity wave coupling.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".