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Record W2926888492 · doi:10.1029/2018jd030117

Characterization of Air and Ground Temperature Relationships within the CMIP5 Historical and Future Climate Simulations

2019· article· en· W2926888492 on OpenAlexafffund
Almudena García‐García, Francisco José Cuesta‐Valero, Hugo Beltrami, Jason E. Smerdon

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversité du Québec à MontréalSt. Francis Xavier UniversityMemorial University of Newfoundland
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsEnvironmental sciencePrecipitationSnowClimatologyClimate modelAtmosphere (unit)Latent heatVegetation (pathology)Atmospheric sciencesLand coverClimate changeMeteorologyLand useGeologyGeography

Abstract

fetched live from OpenAlex

Abstract The relationships between air and ground surface temperatures across North America are examined in the historical and future projection simulations from 32 general circulation models (GCMs) included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The difference between surface air (2 m) and ground surface (10 cm) temperatures is affected by simulated snow cover, vegetation cover, and precipitation by means of changes in soil moisture and soil properties. In winter, the differences between air and ground surface temperatures, for all CMIP5 simulations, are related to the insulating effect of snow cover and soil freezing phenomena. In summer, large leaf area index and large precipitation rates correspond to smaller differences between air and ground temperatures for the majority of simulations, likely due to induced changes in latent and sensible heat fluxes at the ground surface. Our results show that the representation of air‐ground coupling, analyzed using the difference between ground and air surface temperatures as metric, differs from observations, the North American Regional Reanalysis product and among the CMIP5 GCM simulations, by amounts that depend on the employed land surface model. The large variability among GCMs and the marked dependence of the results on the choice of the land surface model illustrate the need for improving the representation of processes controlling the coupling of the lower atmosphere and the land surface in GCMs as a mean of reducing the variability in their representation of weather and climate phenomena.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.286

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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
Published2019
Admission routes2
Has abstractyes

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