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Record W2913104521 · doi:10.1175/jpo-d-18-0141.1

Effects of High Resolution and Spinup Time on Modeled North Atlantic Circulation

2019· article· en· W2913104521 on OpenAlexaboutno aff
Christopher Danek, Patrick Scholz, Gerrit Lohmann

Bibliographic record

VenueJournal of Physical Oceanography · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsHydrographyBaroclinityGeologyOcean gyreOcean currentClimatologyGulf StreamThermohaline circulationOcean dynamicsNorth Atlantic Deep WaterOceanographyRossby waveNorth Atlantic oscillationHigh resolutionRossby numberMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract The influence of a high horizontal resolution (5–15 km) on the general circulation and hydrography in the North Atlantic is investigated using the Finite Element Sea Ice–Ocean Model (FESOM). We find a stronger shift of the upper-ocean circulation and water mass properties during the model spinup in the high-resolution model version compared to the low-resolution (~1°) control run. In quasi equilibrium, the high-resolution model is able to reduce typical low-resolution model biases. Especially, it exhibits a weaker salinification of the North Atlantic subpolar gyre and a reduced mixed layer depth in the Labrador Sea. However, during the spinup adjustment, we see that initially improved high-resolution features partially reduce over time: the strength of the Atlantic overturning and the path of the North Atlantic Current are not maintained, and hence hydrographic biases known from low-resolution ocean models return in the high-resolution quasi-equilibrium state. We identify long baroclinic Rossby waves as a potential cause for the strong upper-ocean adjustment of the high-resolution model and conclude that a high horizontal resolution improves the state of the modeled ocean but the model integration length should be chosen carefully.

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)0.000
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 score0.015
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.003
GPT teacher head0.170
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 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

Citations23
Published2019
Admission routes1
Has abstractyes

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