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Record W2135292355 · doi:10.1175/jas-d-14-0284.1

The Sensitivity of Tropical Cyclone Activity to Off-Equatorial Thermal Forcing in Aquaplanet Simulations

2015· article· en· W2135292355 on OpenAlexaff
Andrew Ballinger, Timothy M. Merlis, Isaac M. Held, Ming Zhao

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsMcGill University
FundersNational Oceanic and Atmospheric AdministrationU.S. Department of Energy
KeywordsIntertropical Convergence ZoneEquatorSea surface temperatureClimatologyTropical cycloneEnvironmental scienceAtmospheric sciencesForcing (mathematics)GeologyAtmospheric modelCyclone (programming language)MeteorologyPrecipitationPhysicsLatitudeGeodesyOceanography

Abstract

fetched live from OpenAlex

Abstract The sensitivity of global tropical cyclone (TC) activity to changes in a zonally symmetric sea surface temperature (SST) distribution and the associated large-scale atmospheric circulation are investigated. High-resolution (~50-km horizontal grid spacing) atmospheric general circulation model simulations with maximum SST away from the equator are presented. Simulations with both fixed-SST and slab ocean lower boundary conditions are compared. The simulated TCs that form on the poleward flank of the intertropical convergence zone (ITCZ) are tracked and changes in the frequency and intensity of those storms are analyzed between the different experiments. The total accumulated cyclone energy (ACE) increases as the location of the maximum SST shifts farther away from the equator. The location of the ITCZ also shifts in conjunction with changes to the SST profile, and this plays an important role in mediating the frequency and intensity of the TCs that form within this modeling framework.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.278
Teacher spread0.242 · 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

Citations42
Published2015
Admission routes1
Has abstractyes

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