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Record W4288046845 · doi:10.1098/rspa.2022.0053

The changing nature of Earth's reflected sunlight

2022· article· en· W4288046845 on OpenAlexaff
Graeme L. Stephens, Maria Z. Hakuba, Seiji Kato, Andrew Gettelman, Jean‐Louis Dufresne, Timothy Andrews, Jason N. S. Cole, Ulrika Willén, Thorsten Mauritsen

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Aeronautics and Space Administration
KeywordsSunlightAtmosphere (unit)ShortwaveAtmospheric sciencesEarth (classical element)Environmental scienceIce-albedo feedbackClimate changeAstrobiologyPhysicsMeteorologyRadiative transferGeologyAstronomyOpticsSea ice

Abstract

fetched live from OpenAlex

The increased rate of sea-level rise suggests that Earth's energy imbalance is also increasing over time. This study assesses whether 20 years of direct observations of this energy imbalance from Earth-orbiting satellites support the existence of a real trend in this imbalance and the components of it and finds. Changes to the imbalance observed are found to be consistent across multiple sources of observations. The majority of recent studies now clearly point to this energy imbalance being positive, while forced by increasing greenhouse gas concentrations in the atmosphere, being amplified significantly by decreases to the amount of sunlight reflected by Earth to space. Here, we show that the global changes observed appear largely from reductions in the amount of sunlight scattered by Earth's atmosphere. These reductions, in turn, are found to be almost equally split between reduced reflection from the cloudy and clear regions of the atmosphere, with the latter being suggestive of reduced scattering by aerosol particles over the observational period. Climate models, however, show an almost exclusive response from clouds, and a slightly exaggerated darkening of the surface. Thus, models that match the global shortwave change do so for the wrong reasons.

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 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 score0.834
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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.

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

Citations42
Published2022
Admission routes1
Has abstractyes

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