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Record W4293055393 · doi:10.1038/s41558-022-01398-2

Future Southern Ocean warming linked to projected ENSO variability

2022· article· en· W4293055393 on OpenAlexaff
Guojian Wang, Wenju Cai, Agus Santoso, Lixin Wu, John C. Fyfe, Sang‐Wook Yeh, Benjamin Ng, Kai Yang, Michael J. McPhaden

Bibliographic record

VenueNature Climate Change · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersNOAA Pacific Marine Environmental LaboratoryAustralian GovernmentCommonwealth Scientific and Industrial Research OrganisationCentre for Southern Hemisphere Oceans ResearchNational Research FoundationJoint Research Centre
KeywordsClimatologyEnvironmental scienceEl Niño Southern OscillationTeleconnectionOcean heat contentGlobal warmingCoupled model intercomparison projectEffects of global warming on oceansClimate modelAmplitudeSea surface temperatureLatitudeClimate changeAtmospheric sciencesOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract The Southern Ocean is a primary heat sink that buffers atmospheric warming and has warmed substantially, accounting for an outsized portion of global warming-induced excess heat in the climate system. However, its projected warming is highly uncertain and varies substantially across climate models. Here, using outputs from Coupled Model Intercomparison Project phase six models, we show that Southern Ocean warming during the twenty-first century is linked to the change in amplitude of the El Niño–Southern Oscillation (ENSO). Models simulating a larger increase in ENSO amplitude systematically produce a slower Southern Ocean warming; conversely, a smaller increase in ENSO amplitude sees a stronger warming. The asymmetry in amplitude and teleconnection between El Niño and La Niña produce cumulative surface wind anomalies over the southern high latitudes, impacting Southern Ocean heat uptake. The magnitude of inter-model ENSO variations accounts for about 50% of the uncertainty in the projected Southern Ocean warming.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.025
GPT teacher head0.261
Teacher spread0.236 · 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.

Study designNot applicable
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

Citations59
Published2022
Admission routes1
Has abstractyes

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