MétaCan
Menu
← Back to cohort
Record W4247817079 · doi:10.5194/wcd-2021-6

Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns

2021· preprint· en· W4247817079 on OpenAlexaff
Erica Madonna, David S. Battisti, Camille Li, Rachel H. White

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of British Columbia
FundersH2020 Marie Skłodowska-Curie ActionsNorges ForskningsrådNational Oceanic and Atmospheric AdministrationTamaki Foundation
KeywordsClimatologyPrecipitationNorth Atlantic oscillationEnvironmental scienceAtmospheric circulationClimate modelJet streamBlocking (statistics)Atmospheric sciencesGeologyJet (fluid)Climate changeGeographyMeteorologyOceanography

Abstract

fetched live from OpenAlex

Abstract. The efficacy of Euro-Atlantic circulation regimes for estimating wintertime climate anomalies (precipitation and surface temperature) over Europe is assessed. A comparison of seasonal climate reconstructions from two different regime frameworks (cluster analysis of the low-level zonal flow, and traditional blocking indices) is presented and contrasted with seasonal reconstructions using the North Atlantic Oscillation (NAO) index. The reconstructions are quantitatively evaluated using correlations and the coefficient of efficiency, accounting for misfit in phase and amplitude. While all methods show skill in reproducing wintertime precipitation and temperature variability, they underestimate the magnitude of seasonal anomalies by a factor of two. The regimes are found to capture more spatial structure in seasonal precipitation anomalies over Europe than the NAO, with the jet framework showing generally better skill relative to the blocking indices. The reconstructions of temperature anomalies have lower skill than those for precipitation, with the best results for temperature obtained by the NAO for high-latitude and by the blocking framework for southern Europe.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.058
GPT teacher head0.266
Teacher spread0.208 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicClimate variability and models→French-language works237,207→