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Record W2380088014

Climate Impacts of the Decadal and Interannual Variability of the Atlantic Thermohaline Circulation in Bergen Climate Model

2005· article· en· W2380088014 on OpenAlexaboutno aff
Tianjun Zhou

Bibliographic record

VenueChinese Journal of Atmospheric Sciences · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsThermohaline circulationClimatologyNorth Atlantic oscillationGeologyAtlantic multidecadal oscillationForcing (mathematics)Environmental scienceOceanographyShutdown of thermohaline circulationClimate modelConvectionOcean currentAtmospheric circulationSea surface temperatureStructural basinClimate changeNorth Atlantic Deep WaterGeographyMeteorology
DOInot available

Abstract

fetched live from OpenAlex

Climate impacts of the basin-scale and local-scale oscillations of the Atlantic thermohaline circulation (THC) are identified by using Bergen Climate Model (BCM). The basin-wide oscillation occurs at decadal scales with strong cross-equatorial flow, the more localized fluctuation of the THC occurs mainly at interannual scales with weaker cross-equatorial flow. The basin-wide THC oscillation is associated with an intensified Azores high and Icelandic low,the local-scale THC adjustment is accompanied with a negative phase-like North Atlantic Oscillation (NAO). The atmospheric circulation change results in an intensified deep convection in the Labrador Sea in both cases. For the local-scale THC adjustment, the Irminger Sea is dominated by a weakened convection. Surface salinity anomalies in the Labrador Sea are created locally by evaporation anomalies at the sinking regions. Further analyses support that the local-scale THC oscillation is caused passively by the atmosphere forcing. There are evidences indicating that the whole THC conveyor accelerating generates the warmer sub-polar SST, which warms the air and enhances the surface evaporation.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2005
Admission routes1
Has abstractyes

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