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Record W4292249441 · doi:10.1029/2022ef002797

Deciphering China's Complex Pattern of Summer Precipitation Trends

2022· article· en· W4292249441 on OpenAlexaff
Li Mei, Chao Li, Zhihong Jiang, Xuebin Zhang, Francis W. Zwiers

Bibliographic record

VenueEarth s Future · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsPacific Institute for Climate SolutionsUniversity of VictoriaEnvironment and Climate Change Canada
FundersNational Natural Science Foundation of China
KeywordsPrecipitationClimatologyChinaEnvironmental scienceForcing (mathematics)Climate changeHomogeneousSurface air temperatureAtmospheric circulationAtmospheric sciencesGeographyMeteorologyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Observations show that summer precipitation in China has undergone pronounced changes, resulting in an enigmatic “north‐south drying‐wetting” pattern in eastern China that is of great concern for socio‐economic development. Scientific consensus on the mechanisms that are responsible for this pattern of change has not yet been achieved. We show that this complex pattern of summer total precipitation trends observed in China since the 1960s is overwhelmingly the result of changes in daily precipitation frequency, rather than being the result of changes in precipitation intensity or the frequency of synoptic circulation patterns favorable to precipitation. Changes in precipitation intensity, which are very likely due to anthropogenic greenhouse gas forcing, contribute a relatively homogeneous wetting tendency across the country while changes due to synoptic circulation change are weak. The changes in daily precipitation frequency that drive the observed patterns of change may be due to aerosols, but improved process understanding will be required to resolve that question and enable reliable projections of regional scale precipitation change in China and elsewhere.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.969

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)0.0310.000

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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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