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Record W3022998026 · doi:10.1029/2019gl086757

Climate Model Projections of 21st Century Global Warming Constrained Using the Observed Warming Trend

2020· article· en· W3022998026 on OpenAlexafffund
Yongxiao Liang, Nathan P. Gillett, Adam H. Monahan

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoupled model intercomparison projectClimatologyGlobal warmingEnvironmental scienceClimate modelWeightingCruClimate sensitivityClimate changeGeology

Abstract

fetched live from OpenAlex

Abstract The Coupled Model Intercomparison Project Phase 6 (CMIP6) archive includes larger ensembles, longer historical simulations, and models with a broader range of climate sensitivity than CMIP5. These features favor the application of observationally constrained climate projections. The 1970–2014 trend in global mean temperature is well‐correlated with projected future warming across the CMIP6 multimodel ensemble. We first evaluate an approach that weights simulations based on the realism and degree of independence of their 1970–2014 trends, by treating each historical simulation in turn as pseudo‐observations, and using the other models and weighting method to predict 21st century warming in the model concerned. The method performs well based on correlation and probabilistic measures. Applying the method using the observed 1970–2014 warming trend results in only small changes in the mean and lower bound of CMIP6 projected warming but substantially reduces the upper bound of projected early‐, mid‐ and late‐21st century warming under all SSP scenarios.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.343
Teacher spread0.200 · 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

Citations167
Published2020
Admission routes2
Has abstractyes

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