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Record W2586709157 · doi:10.1002/2016gl071567

The United States “warming hole”: Quantifying the forced aerosol response given large internal variability

2017· article· en· W2586709157 on OpenAlexaff
Antara Banerjee, Lorenzo M. Polvani, John C. Fyfe

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Science Foundation
KeywordsAerosolEnvironmental scienceForcing (mathematics)ClimatologyAtmospheric sciencesClimate modelRadiative forcingClimate changeMeteorologyOceanographyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract Twenty‐five years of large summer cooling over the southeastern United States ending in the mid‐1970s coincided with rapidly increasing anthropogenic aerosol emissions. Here we assess the claim that the cooling in that period was predominantly due to such aerosols. We utilize two 50‐member sets of coupled climate model simulations, one with only anthropogenic aerosol forcings and another with all known natural and anthropogenic forcings, together with a long control integration. We show that, in the absence of aerosol forcing, none of the model simulations capture the observed surface cooling rate (∼0.56°C decade−1), whereas with increasing aerosol emissions 2 (of 50) of the simulations do. More importantly, however, we find that the cooling from aerosols (0.20°C decade−1) is insufficient to explain the observation. Our results therefore suggest that, while aerosols may have played a role, the observed cooling was a rare event that contained a large contribution from unforced internal variability.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.083
GPT teacher head0.367
Teacher spread0.284 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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