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Record W3127432954 · doi:10.1029/2020gl091873

Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks

2021· article· en· W3127432954 on OpenAlexaff
Navjit Sagoo, Trude Storelvmo, Lily Hahn, Ivy Tan, James Danco, Bryan Raney, Anthony J. Broccoli

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMcGill University
FundersNational Science BoardYale UniversityNational Science Foundation
KeywordsCloud feedbackClimate modelEnvironmental scienceCloud computingIce cloudAtmospheric sciencesClimate stateClimate changeClimatologyGeologyGlobal warmingClimate sensitivityEffects of global warmingComputer scienceOceanography

Abstract

fetched live from OpenAlex

Abstract The mechanisms which amplify orbitally driven changes in insolation and drive the glacial cycles of the past 2.6 million years, the Pleistocene, are poorly understood. Previous studies indicate that cloud phase feedbacks oppose ice sheet initiation when orbital configuration supports ice sheet growth. Cloud phase was observationally constrained in a recent study and provides evidence for a weaker negative cloud feedback in response to carbon dioxide doubling. We observationally constrain cloud phase in the Community Earth System Model and explore how changes in orbital configuration impact the climate response. Constraining cloud phase weakens the negative high latitude cloud phase feedback and unmasks positive water vapor and cloud feedbacks (amount and optical depth) that extend cooling to lower latitudes. Snowfall accumulation and ablation metrics also support ice sheet expansion as seen in proxy records. This indicates that well‐known cloud and water vapor feedbacks are the mechanisms amplifying orbital climate forcing.

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)0.002
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.055
GPT teacher head0.329
Teacher spread0.275 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research Letters→Same topicGeology and Paleoclimatology Research→French-language works237,207→