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Record W606710356

Diagnosing and Understanding the AMOC Biases in NorESM

2014· article· en· W606710356 on OpenAlexaboutno aff
D. Ivanova, Mats Bentsen, Mehmet Ilıcak, Chuncheng Gao, Helge Drange

Bibliographic record

VenueBergen Open Research Archive (BORA) (University of Bergen) · 2014
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

We investigate the Atlantic Meridional Overturning Circulation (AMOC) in the Norwegian Earth System Model (NorESM) featuring isopycnal ocean component (MICOM). The Coupled Model Intercomparison Phase 5 (CMIP5) NorESM historical simulations showed a decline of the AMOC after 1980 concurring with the recent observations from RAPID-MOCHA program. The NorESM future projections predict reduction of the AMOC with 12 to 30% under different warming (RCP2.6-RCP8.5) scenarios. In the CMIP5 model intercomparison project, the NorESM ocean component demonstrated an intense AMOC and took place in the upper end of the AMOC magnitudes model range. The NorESM AMOC strength was found to be sensitive to oceanic grid resolution and whether coupled or uncoupled configuration is used. However, the AMOC tends to be on the strong side in all configurations. In order to find the causes of this vigorous AMOC we carried out a careful diagnostics of the AMOC and explored possible relationship to the model biases found in the Atlantic thermohaline structure, and water mass formation. Several processes has been investigated to understand further their connection and significance to the AMOC strength and variability: 1) The North Atlantic Mode Waters Formation (STMW and SPMW); 2) The Labrador Sea Water Mass formation and variability. Furthermore, the AMOC sensitivity to sub-grid scale physical parameterizations such as isopycnal eddy mixing and the impact of model resolution on the representation of overflows is examined.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.550

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 science0.0010.001
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 head0.136
GPT teacher head0.329
Teacher spread0.193 · 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 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
Published2014
Admission routes1
Has abstractyes

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