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Record W4291719075 · doi:10.1029/2022gl100152

Cloud Feedback on Earth's Long‐Term Climate Simulated by a Near‐Global Cloud‐Permitting Model

2022· article· en· W4291719075 on OpenAlexaff
Mingyu Yan, Jun Yang, Yixiao Zhang, Han Huang

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsCloud albedoCloud feedbackCloud computingTerm (time)Environmental scienceAtmospheric sciencesCloud coverClimate modelGeneral Circulation ModelShortwaveConvectionAlbedo (alchemy)ClimatologyMeteorologyClimate changeGeologyClimate sensitivityPhysicsRadiative transferComputer scienceAstronomy

Abstract

fetched live from OpenAlex

Abstract The Sun becomes brighter with time, but Earth's climate is roughly temperate for life during its long‐term history; for early Earth, this is known as the faint young Sun problem (FYSP). Besides the carbonate‐silicate feedback, recent researches suggest that a long‐term cloud feedback may partially solve the FYSP. However, the general circulation models they used cannot resolve convection and clouds explicitly. This study re‐investigates the clouds using a near‐global cloud‐permitting model without cumulus convection parameterization. Our results confirm that a stabilizing shortwave cloud feedback does exist, and its magnitude is ≈6 W m −2 or 14% of the energy required to offset a 20% fainter Sun than today, or ≈10 W m −2 or 16% for a 30% fainter Sun. When insolation increases and meanwhile CO 2 concentration decreases, low‐level clouds increase, acting to stabilize the climate by raising planetary albedo, and vice versa.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.306
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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