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Trends of intense cyclone activity in the Arctic from reanalyses data and regional climate models (Arctic-CORDEX)

2019· article· en· W2912703046 on OpenAlexaff
Mirseid Akperov, Annette Rinke, И. И. Мохов, Heidrun Matthes, В. А. Семенов, Muralidhar Adakudlu, John J. Cassano, Jens Hesselbjerg Christensen, M. A. Dembitskaya, Klaus Dethloff, Xavier Fettweis, Justin M. Glisan, Oliver Gutjahr, Günther Heinemann, Torben Koenigk, Nikolay Koldunov, René Laprise, Ruth Mottram, Oumarou Nikiéma, М. Р. Парфенова, John Scinocca, Dmitry Sein, Stefan Sobolowski, Katja Winger, Wenxin Zhang

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change CanadaUniversité du Québec à Montréal
FundersGoddard Space Flight CenterEuropean Centre for Medium-Range Weather ForecastsCanon Foundation for Scientific ResearchNational Aeronautics and Space Administration
KeywordsClimatologyEnvironmental scienceArcticCyclone (programming language)Climate modelThe arcticMeteorologyClimate changeGeographyOceanographyGeologyComputer science

Abstract

fetched live from OpenAlex

The ability of state-of-the-art regional climate models (RCMs) to simulate the trends of intense cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble in winter and summer are compared with the results from four reanalyses (ERA-Interim, NCEP-CFSR, NASA-MERRA2 and JMA-JRA55) in winter and summer for 1981-2010 period.

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)0.001
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 score0.117
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.249
Teacher spread0.203 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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