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Record W2068751353 · doi:10.1175/jcli4058.1

The Impacts of Climate Change on Autumn North Atlantic Midlatitude Cyclones

2007· article· en· W2068751353 on OpenAlexaffabout
Jing Jiang, William Perrie

Bibliographic record

VenueJournal of Climate · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaDalhousie University
FundersOffice of Naval ResearchNational Oceanic and Atmospheric Administration
KeywordsClimatologyExtratropical cycloneMiddle latitudesEnvironmental scienceStormClimate changeClimate modelForcing (mathematics)Greenhouse gasGlobal warmingSubtropicsAtmospheric sciencesMeteorologyGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract This study explores how midlatitude extratropical cyclone intensities, frequencies, and tracks can be modified under warming-induced conditions due to enhanced greenhouse gas (GHG) concentrations. Simulations were performed with the Canadian mesoscale compressible community (MC2) model driven by control and high CO2 climate estimates from the Canadian Climate Centre model, the Second Generation Coupled Global Climate Model (CGCM2). CGCM2 simulations have effective CO2 concentration forcing, following the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario conditions, which define a near doubling of CO2 concentrations by 2050 compared to the 1980s. The control and high CO2 conditions were obtained from years 1975–94 and 2040–59 of CGCM2 simulations. For the northwest Atlantic, the CO2-induced warming for this period (2040–59) varies from ∼1°–2°C in the subtropics, near the main development region for Atlantic hurricanes, to ∼1°C in the north. In simulations of northwest Atlantic storms, the net impact of this enhanced CO2 scenario is to cause storms to increase in radius, with marginal tendencies to become more severe and to propagate faster (although not statistically significant), and for the mean storm tracks to shift slightly poleward.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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

Citations32
Published2007
Admission routes2
Has abstractyes

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