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Record W4237413788 · doi:10.5194/egusphere-egu2020-705

Mechanisms and predictability of Sudden Stratospheric Warming in winter 2018

2020· preprint· en· W4237413788 on OpenAlexaboutno aff
Alexey Yu. Karpechko, Heikki Järvinen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsPredictabilityClimatologyAmplitudeTeleconnectionEnvironmental scienceTroposphereAtmospheric sciencesEnsemble averagePhysicsGeologyEl Niño Southern Oscillation

Abstract

fetched live from OpenAlex

In this study, we investigate the Sudden Stratospheric Warming that took place on 12 February 2018 (SSW2018), its predictability and teleconnection with the Madden-Julian Oscillation (MJO) by analysing ECMWF ensemble forecast initialised on 1 February 2018. Several days prior to that date MJO was in Phase 6 and had a strong amplitude potentially contributing to triggering the SSW. Two wave trains can be identified in the upper troposphere over the northern Atlantic and Pacific regions. Starting from the 3 February, the amplitude of planetary wave with wavenumber 2 (PW2) started to increase and reached record high values, while the PW1 amplitude decreased. In order to better understand the sources of uncertainties, we divided the forecast ensemble members into two groups. The first group predicted the SSW onset in time while the second group of ensemble members did not capture the wind reversal at 60°N 10 hPa. The results obtained with the ensemble forecast data were compared with the ECMWF’s reanalysis ERA-Interim (ERA-I). The analysis of the two groups of ensemble forecasts shows that in the first group of forecasts PW2 prevailed with ridges over the Ural and Alaska and troughs over the west Siberia and Canada, as observed. Instead, PW1 is seen in the second group of ensemble members with a broad ridge over Eurasia. Calculations of wave activity fluxes show that there is less zonal wave energy propagation in the second group compared to the first group and ERA-I over Eurasia, which can be associated with the errors in the forecasted location of the Ural high. There is also wave energy propagation towards an area of high pressure over Alaska, as seen in ERA-I. Here, wave energy propagation is similarly underestimated by both groups. Overall, the structure of the geopotential anomalies averaged for 5-7 February for the first group and ERA-I is more consistent with the climatological response from MJO phase 6 taken with lag 5-9 days than that in the second group.

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)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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
Published2020
Admission routes1
Has abstractyes

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