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Record W1985697740 · doi:10.1029/2009jd013535

On the impacts of climate change and the upper ocean on midlatitude northwest Atlantic landfalling cyclones

2010· article· en· W1985697740 on OpenAlexaffabout
William Perrie, Yonghong Yao, Weiqing Zhang

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsEnvironment and Climate Change CanadaFisheries and Oceans Canada
FundersNational Natural Science Foundation of China
KeywordsEnvironmental scienceClimatologyClimate changeMiddle latitudesStormWinter stormClimate modelGreenhouse gasMesoscale meteorologyTropical cycloneStorm trackAtmospheric sciencesMeteorologyGeologyOceanographyGeography

Abstract

fetched live from OpenAlex

The influence of climate change on midlatitude North Atlantic landfalling autumn storms is investigated using a relatively high‐resolution mesoscale atmosphere‐ocean coupled model system. Atmospheric boundary conditions for autumn storm simulations by this coupled model system are given by the Canadian second‐generation Coupled Global Climate Model (CGCM2), following the Intergovernmental Panel on Climate Change IS92a scenario. The control and high‐CO 2 boundary conditions are obtained from CGCM2 simulations representing the present climate (1975–1994), and a future climate change scenario (2040–2059), corresponding to a doubling of greenhouse gases. An understanding of the possible influences of climate change on the storm climate is achieved through our simulations. The impact of climate change is seen in slightly decreased intensities in landfalling cyclones (about 5 hPa) resulting from the competition between warming provided by the climate change scenario and modest cooling around the storm center induced mainly by dynamic cooling. An additional impact is that cyclone tracks tend to shift 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)0.001
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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

Citations8
Published2010
Admission routes2
Has abstractyes

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