Quantifying the sources of increasing stratospheric water vapour concentrations in the 21 <sup>st</sup> century
Bibliographic record
Abstract
Abstract. According to satellite measurements from multiple instruments, water vapour (H2O) concentrations, in most regions of the stratosphere, have been increasing at a statistically significant rate of ∼1–5 % dec−1 since the early 2000s. Previous studies have estimated stratospheric H2O trends, but none have simultaneously quantified the contributions from the main sources: temperature variations in the tropical tropopause region, changes in the Brewer-Dobson circulation, and changes in methane (CH4) concentrations and its oxidation. Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) measurements are used to estimate altitude/latitude-dependent stratospheric H2O trends from 2004–2021 due to these sources. Results indicate that rising temperatures in the tropical tropopause region play a significant role in the increases, accounting for ∼1–4 % dec−1 in the tropical lower-mid stratosphere, as well as in the mid-latitudes below ∼20 km. By regressing to ACE-FTS N2O concentrations, it is found that in the lower-middle stratosphere, general circulation changes have led to both significant H2O increases and decreases on the order of 1–2 % dec−1 depending on altitude/latitude region. Making use of measured and modelled CH4 concentrations, the increase in H2O due to CH4 oxidation is calculated to be ∼1–2 % dec−1 above ∼30 km in the Northern Hemisphere and throughout the stratosphere in the Southern Hemisphere. After accounting for these sources, there are still regions of the midlatitude lower-mid stratosphere that exhibit significant residual H2O trends increasing at 1–2 % dec−1. Results are discussed that indicate these unaccounted for increases could potentially be explained by increases in upper tropospheric molecular hydrogen.
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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 | 0.000 |
| 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 | 0.001 | 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".