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Record W3195761798 · doi:10.1029/2020ef001832

Divergent Responses of Summer Precipitation in China to 1.5°C Global Warming in Transient and Stabilized Scenarios

2021· article· en· W3195761798 on OpenAlexaff
Zhihong Jiang, Qiyao Hou, Tim Li, Yongxiao Liang, Laurent Li

Bibliographic record

VenueEarth s Future · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClimatologyAnticyclonePrecipitationEnvironmental scienceGlobal warmingEast AsiaSubtropicsRossby waveCoupled model intercomparison projectSubtropical ridgeChinaTroposphereAtmospheric sciencesClimate changeClimate modelGeologyOceanographyGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract Responses of summer precipitation in China to global 1.5°C warming with transient and stabilized pathways are investigated through the analysis of the Common Earth System Model climate simulations. Precipitation increases more significantly in southeast China in the stabilized scenario compared with the transient one. The difference is primarily attributable to dynamic factors related to circulation changes and driven by differences of land‐ocean thermal contrast between the two scenarios. The stabilized warming favors conditions of larger ocean warming and smaller land warming, leads to enhanced meridional temperature gradient and ultimately speeds up the jet stream over East Asia. Ageostrophic wind at the entrance of the accelerated jet strengthens ascending motion and precipitation along the coast of Asia from southeast China to India. The enhanced heating over India further induces a Kelvin‐wave response with an anomalous anticyclone that contributes to the enhanced westward shifted of the western North Pacific Subtropical High. Southerly winds at the west flank of the anticyclone transport moisture northward from tropical oceans, and strengthen precipitation in southeast China.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.069
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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 teacher head, 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

Citations13
Published2021
Admission routes1
Has abstractyes

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