MétaCan
Menu
Back to cohort
Record W3063949597 · doi:10.1175/jcli-d-20-0114.1

On the Oceanic Origin for the Enhanced Seasonal Cycle of SST in the Midlatitudes under Global Warming

2020· article· en· W3063949597 on OpenAlexaff
Fukai Liu, Jian Lu, Yiyong Luo, Yi Huang, Fengfei Song

Bibliographic record

VenueJournal of Climate · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersPacific Northwest National LaboratoryBiological and Environmental ResearchOffice of ScienceNational Natural Science Foundation of ChinaBattelleNational Key Research and Development Program of ChinaU.S. Department of Energy
KeywordsClimatologyMiddle latitudesWind stressSea surface temperatureEnvironmental scienceNorthern HemisphereAtmospheric sciencesForcing (mathematics)Annual cycleSouthern HemisphereGeology

Abstract

fetched live from OpenAlex

Abstract Climate models project an enhancement in SST seasonal cycle over the midlatitude oceans under global warming. The underlying mechanisms are investigated using a set of partially coupled experiments, in which the contribution from direct CO2 effects (i.e., the response in the absence of wind change) and wind feedbacks can be isolated from each other. Results indicate that both the direct CO2 and wind effects contribute to the enhancement in the SST seasonal cycle, with the former (latter) being more important in the Northern Hemisphere (Southern Hemisphere). Further decomposition of the wind effect into the wind stress feedback and wind speed feedback reveals the importance of the wind stress–driven ocean response in the change of SST seasonal cycle, a result in contrast to a previous study that ascribed the midlatitude SST seasonal cycle change to the thermodynamic wind speed feedback. The direct CO2 effect regulates the SST seasonal cycle through the warming-induced shoaling in the annual mean mixed layer depth (MLD) as well as the MLD difference between winter and summer. Moreover, the surface wind seasonal cycle changes due solely to the direct CO2 effect are found to bear a great resemblance to the full wind response, suggesting that the root cause for the enhancement of the midlatitude SST seasonal cycle resides in the direct CO2 effect. This notion is further supported by an ocean-alone experiment that reproduces the SST seasonal cycle enhancement under a spatially and temporally homogeneous surface thermal forcing.

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)0.000
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.296
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 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

Citations35
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ClimateSame topicClimate variability and modelsFrench-language works237,207