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Record W4385658220 · doi:10.1029/2023gl103742

Stratospheric Ozone Loss Enhances Summer Precipitation Over the Southern Slope of the Tibetan Plateau

2023· article· en· W4385658220 on OpenAlexafffund
Yan Xia, Yongyun Hu, Yi Huang, Jianchun Bian, Chuanfeng Zhao, Jintai Lin, Fei Xie, Chunjiang Zhou

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersCanadian Space AgencyNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPlateau (mathematics)PrecipitationClimatologyEnvironmental scienceAtmospheric sciencesStratosphereTroposphereGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Heavy summer precipitation over the southern slope of the Tibetan Plateau has dramatic influences on water resources and hydrological disasters in South Asia. It experienced increasing trends over 1979–1996 and decreasing trends over 1996–2022, which are not yet well understood. Here we show observational and numerical evidence that stratospheric ozone has significant impacts on long‐term trends of summer precipitation in this strong convection area. It is found that stratospheric ozone depletion, by modulating the lower stratospheric temperature and upper‐tropospheric static stability, enhances deep convection and precipitation over the southern slope of the Tibetan Plateau. The results suggest that the ozone recovery in the future may reduce the summer precipitation over the southern slope of the Tibetan Plateau in the first half of the 21st century, which would be imperative for future water resource management in South Asia.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.313
Teacher spread0.270 · 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 designObservational
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

Citations15
Published2023
Admission routes2
Has abstractyes

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