The SPARC water vapour assessment II: Profile-to-profile and climatological comparisons of stratospheric <i>δ</i> D(H <sub>2</sub> O) observations from satellite
Bibliographic record
Abstract
Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), we have evaluated five data sets of δD(H2O) obtained from observations of Odin/SMR (Sub-Millimetre Radiometer), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) and SCISAT/ACE-FTS (Science Satellite/Atmospheric Chemistry Experiment-Fourier Transform Spectrometer) using profile-to-profile and climatological comparisons. Our focus is on stratospheric altitudes, but results from the upper troposphere to the lower mesosphere are provided. There are clear quantitative differences in the measurements of the isotopic ratio, which primarily concerns the comparisons to the SMR data set. In the lower stratosphere, this data set shows a higher depletion than the MIPAS and ACE-FTS data sets. The differences maximise close to 50 hPa and exceed 200 per mille. With increasing altitude, the biases typically decrease. Above 4 hPa, the SMR data set shows a lower depletion than the MIPAS data sets, on occasion exceeding 100 per mille. Overall, the δD biases of the SMR data set are driven by HDO biases in the lower stratosphere and by H2O biases in the upper stratosphere and lower mesosphere. In between, in the middle stratosphere, the biases in δD are a combination of deviations in both HDO and H2O. These biases are attributed to issues with the calibration, in particular in terms of the sideband filtering for H2O, and uncertainties in spectroscopic parameters. The MIPAS and ACE-FTS data sets agree rather well between about 100 hPa and 10 hPa. The MIPAS data sets show less depletion below about 15 hPa (up to about 30 per mille), due to differences in both HDO and H2O. Higher up the picture is reversed, and towards the upper stratosphere the biases typically increase. This is driven by increasing biases in H2O and on occasion the differences in δD exceed 80 per mille. Below 100 hPa, the differences between the MIPAS and ACE-FTS data sets are even larger. In the climatological comparisons, the MIPAS data sets continue to show less depletion than the ACE-FTS data sets below 15 hPa during all seasons, with some variations in magnitude. The differences between the MIPAS and ACE-FTS data come from different aspects, such as differences in the temporal and spatial sampling (except for the profile-to-profile comparisons), cloud influence, vertical resolution, and the microwindows and spectroscopic database chosen. Differences between data sets from the same instrument are typically small in the stratosphere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".