Atmospheric contributions to nutations and implications for the estimation of deep Earth's properties from nutation observations
Bibliographic record
Abstract
We propose a new estimation of the atmospheric contributions to Earth's nutations based on three reanalyses of atmospheric global circulation models (GCM), namely the two reanalyses of the National Center for Environmental Prediction (NCEP) and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF).We estimate the complex amplitudes of the periodic terms in the atmospheric forcing and convolve them with a transfer function for a three-layers Earth with an anelastic mantle and dissipative couplings at the fluid core boundaries.Unlike previous estimations based on operational GCMs, the results we obtain here from the three reanalysis GCMs are in good agreement, which makes them more reliable.From a joint inversion of the three atmospheric models on their common time span (from 1979 to 2002.3), we estimate the atmospheric contributions to nutations to be -38.2± 0.4 µas in-phase (ip) and 65.1 ± 0.4 µas out-of-phase (op) on the prograde annual term (S 1 ), -64 ± 5 µas ip and 29 ± 5 µas op on the retrograde annual term (ψ 1 ), and -11.3 ± 0.3 µas ip and 41.5 ± 0.3 µas op on the prograde semi-annual term (P 1 ).As the atmospheric contributions to nutation vary in time, we also compute their time-variability on the time span from 1979 to 2010.In particular, we show that the contribution to ψ 1 has a very large time variability but that these variations are well determined by the atmospheric models that we use.Finally, we explore the implications of the atmospheric contribution to ψ 1 on the estimation of Earth's deep interior properties from nutation observations.We show that this contribution is too small to affect significantly the estimation of these properties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".