Simulation of convective moistening of extratropical lower stratosphere using a numerical weather prediction model
Bibliographic record
Abstract
Abstract. Stratospheric water vapor (SWV) is a climatically important atmospheric constituent due to its impacts on the radiation budget and atmospheric chemical composition. Despite the important role of SWV in the climate system, the processes controlling the distribution and variation of water vapor in the upper troposphere and lower stratosphere (UTLS) are not well understood. In order to better understand the mechanism of transport of water vapor through the tropopause, this study uses the high resolution Global Environmental Multiscale model of the Environment and Climate Change Canada, to simulate a lower stratosphere moistening event over North America. Satellite remote sensing and aircraft in situ observations are used to evaluate the quality of model simulation. The main focus of this study is to evaluate the processes that influence the lower stratosphere water vapor budget, particularly the direct water vapor transport and the moistening due to the ice sublimation. In the high-resolution simulations with horizontal grid-spacing less than 2.5 km, it is found that the main contribution to lower-stratospheric moistening is the upward transport caused by the breaking of gravity waves. In contrast, for the lower-resolution simulation with horizontal grid-spacing of 10 km, the lower-stratospheric moistening is dominated by the sublimation of ice. In comparison with the aircraft in situ observations, the high-resolution simulations predict well the water vapor content in the UTLS, while the lower resolution simulation over-estimates the water vapor content. This overestimation is associated with the overly abundant ice in the UTLS along with too-high sublimation rate in the lower stratosphere. The results of this study affirm the strong influence of overshooting convection on the lower-stratospheric water vapor and highlight the importance of both dynamics and microphysics in simulating the water vapor distribution in the UTLS region.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".