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Record W3106357389

The effect of ocean tides on a climate model simulation

2008· article· en· W3106357389 on OpenAlexaffabout
Marc Mueller, Helmuth Haak, Johann Jungclaus, Maik Thomas

Bibliographic record

VenueMax Planck Digital Library · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClimatologyClimate modelCurrent (fluid)Forcing (mathematics)OceanographyEnvironmental scienceSea surface temperatureOcean currentSea iceConvectionNorth Atlantic Deep WaterAtlantic Equatorial modeThermohaline circulationClimate changeGeologyMeteorologyGeography
DOInot available

Abstract

fetched live from OpenAlex

We implemented an explicit forcing of the complete lunisolar tides into an ocean model which is part of a coupled atmosphere-hydrology-ocean-sea ice model. An ensemble of experiments with this climate model shows that the model is significantly affected by the induced tidal mixing and nonlinear interactions of tides with low frequency motion. The largest changes occur in the North Atlantic where the ocean current system gets changed on large scales. In particular, the pathway of the North Atlantic Current is modified resulting in improved sea surface temperature fields compared to the non-tidal run. These modifications are accompanied by a more realistic simulation of the convection in the Labrador Sea. The modification of sea surface temperature in the North Atlantic region leads to heat flux changes of up to 50 W/m2. The climate simulations indicate that an improvement of the North Atlantic Current has implications for the simulation of the Western European Climate, with amplified temperature trends between 1950 and 2000, which are closer to the observed trends. © 2010 Elsevier Ltd. All rights reserved.

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)0.006
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.173
Teacher spread0.167 · 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

Citations0
Published2008
Admission routes2
Has abstractyes

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