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Record W2980181288 · doi:10.5194/acp-20-2143-2020

Simulation of convective moistening of the extratropical lower stratosphere using a numerical weather prediction model

2020· article· en· W2980181288 on OpenAlexafffundabout
Zhipeng Qu, Yi Huang, Paul Vaillancourt, Jason N. S. Cole, Jason A. Milbrandt, Man‐Kong Yau, Kaley A. Walker, J. de Grandpré

Bibliographic record

VenueAtmospheric chemistry and physics · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsMcGill UniversityUniversity of TorontoEnvironment and Climate Change Canada
FundersCanadian Space Agency
KeywordsStratosphereTropopauseExtratropical cycloneTroposphereWater vaporEnvironmental scienceAtmospheric sciencesSublimation (psychology)ClimatologyMeteorologyGeology

Abstract

fetched live from OpenAlex

Stratospheric water vapour (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 in water vapour in the upper troposphere and lower stratosphere (UTLS) are not well understood. In order to better understand the mechanism of transport of water vapour 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 vapour budget, particularly the direct water vapour transport and the moistening due to the ice sublimation. In the high-resolution simulations with horizontal grid spacing of 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 the water vapour content in the UTLS well, while the lower-resolution simulation overestimates the water vapour content. This overestimation is associated with the overly abundant ice in the UTLS along with a sublimation rate that is too high in the lower stratosphere. The results of this study affirm the strong influence of overshooting convection on the lower stratospheric water vapour and highlight the importance of both dynamics and microphysics in simulating the water vapour distribution in the UTLS region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.214
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2020
Admission routes3
Has abstractyes

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