MétaCan
Menu
Back to cohort
Record W1540555244 · doi:10.1029/2008gl036141

Spatiotemporal patterns of changes in maximum and minimum temperatures in multi‐model simulations

2009· article· en· W1540555244 on OpenAlexaff
Liming Zhou, Robert E. Dickinson, Paul A. Dirmeyer, Aiguo Dai, Seung‐Ki Min

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsEnvironmental scienceLongwaveCloud coverClimatologyAtmospheric sciencesShortwave radiationLatitudeShortwaveAtmosphere (unit)Diurnal cycleOutgoing longwave radiationClimate modelWater vaporRadiative transferClimate changeRadiationMeteorologyGeographyConvectionGeologyCloud computing

Abstract

fetched live from OpenAlex

This paper analyzes and attributes spatial and temporal patterns of changes in the diurnal cycle of land surface air temperature in 20 simulations from 11 global coupled atmosphere‐ocean general circulation models during the 20th century and the 21st century under the SRES A1B scenario. Most of the warming in the maximum (T max ) and minimum (T min ) temperatures from 1900 to 2099 is attributed to enhanced surface downward longwave radiation (DLW), while changes in surface downward shortwave radiation (DSW) and cloud cover mainly contribute to the simulated decrease in the diurnal temperature range (DTR). Although the simulated DTR decreases are much smaller than the observed during the 20th century, the models unanimously predict substantial warming in both T max and T min and decreases in DTR, especially in high latitudes during the 21st century, in response to enhanced global‐scale anthropogenic forcings (particularly greenhouse effects of atmospheric water vapor and in part aerosol radiative cooling in the tropics) and increased cloudiness in high latitudes.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.062
GPT teacher head0.339
Teacher spread0.277 · 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 designObservational
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

Citations56
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicClimate variability and modelsFrench-language works237,207