Sensitivities of modelled water vapour in the lower stratosphere: temperature uncertainty, effects of horizontal transport and small-scale mixing
Bibliographic record
Abstract
Water vapour (H 2 O) in the upper troposphere and lower stratosphere (UTLS) has a significant role for global radiation. A realistic representation of H 2 O is therefore critical for accurate climate model predictions of future climate change. In this paper we investigate the effects of current uncertainties in tropopause temperature, horizontal transport and small-scale mixing on simulated H 2 O in the lower stratosphere (LS). To assess the sensitivities of simulated H 2 O, we use the Chemical Lagrangian Model of the Stratosphere (CLaMS). First, we examine CLaMS, which is driven by two reanalyses, from the European Centre of Medium-Range Weather Forecasts (ECMWF) ERA-Interim and the Japanese 55-year Reanalysis (JRA-55), to investigate the robustness with respect to the meteorological dataset. Second, we carry out CLaMS simulations with transport barriers along latitude circles (at the Equator, 15 and 35 ∘ N/S) to assess the effects of horizontal transport. Third, we vary the strength of parametrized small-scale mixing in CLaMS. Our results show significant differences (about 0.5 ppmv) in simulated stratospheric H 2 O due to uncertainties in the tropical tropopause temperatures between the two reanalysis datasets, JRA-55 and ERA-Interim. The JRA-55 based simulation is significantly moister when compared to ERA-Interim, due to a warmer tropical tropopause (approximately 2 K). The transport barrier experiments demonstrate that the Northern Hemisphere (NH) subtropics have a strong moistening effect on global stratospheric H 2 O. The comparison of tropical entry H 2 O from the sensitivity 15 ∘ N/S barrier simulation and the reference case shows differences of up to around 1 ppmv. Interhemispheric exchange shows only a very weak effect on stratospheric H 2 O. Small-scale mixing mainly increases troposphere–stratosphere exchange, causing an enhancement of stratospheric H 2 O, particularly along the subtropical jets in the summer hemisphere and in the NH monsoon regions. In particular, the Asian and American monsoon systems during a boreal summer appear to be regions especially sensitive to changes in small-scale mixing, which appears crucial for controlling the moisture anomalies in the monsoon UTLS. For the sensitivity simulation with varied mixing strength, differences in tropical entry H 2 O between the weak and strong mixing cases amount to about 1 ppmv, with small-scale mixing enhancing H 2 O in the LS. The sensitivity studies presented here provide new insights into the leading processes that control stratospheric H 2 O, which are important for assessing and improving climate model projections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".