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Record W3166021895 · doi:10.5194/egusphere-egu21-8747

Internal Atlantic Multidecadal Variability mechanism at two model resolutions 

2021· article· en· W3166021895 on OpenAlexaboutno aff
Michael Lai, Jon Robson, Laura J. Wilcox, Nick Dunstone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsClimatologyEnvironmental scienceForcing (mathematics)LatitudeClimate changeClimate modelAtmospheric sciencesOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

The Atlantic Multidecadal Variability (AMV) is a key factor in modulating climate change and its impacts around the world. Therefore, understanding of its physical mechanism will be crucial to achieving predictability on decadal timescales. However, details of the mechanism are not fully understood. This is evident in the wide range of simulated AMV timescales and spatial patterns exhibited by climate models in both pre-industrial and historical simulations. In this study, we assess the impact of model resolution on the internal AMV mechanism by taking advantage of the close physical similarities between the medium- and low-resolution versions of the HadGEM3 models. Here, we present results from analysing the N96ORCA1 (~135km atmosphere, 1° ocean) and N216ORCA025 (~60km, 0.25°) pre-industrial simulations. At both resolutions, we found that the internal AMV has a timescale of 70-100 years, comparable to the observed record. The processes driving decadal SST variability varies by latitude. Ocean heat transport changes associated with the AMOC drive subpolar variability, while surface fluxes associated with cloud and wind changes are more important in the subtropics. The AMOC strengthening is induced by density forcing from two sources. First, a Labrador Sea surface cooling driven by low-frequency positive NAO leads the AMOC by 5 years. Second, a source of anomalously saline Arctic water flowing into the subpolar North Atlantic also leads the AMOC by 5 years. Interestingly, the two resolutions disagree on the relative importance of these AMOC drivers. In the lower resolution model, the Arctic contribution is more important. However, the NAO dominates in the medium resolution model, and decadal NAO variability is more strongly associated with the AMV. Differences between the models are likely due to mean state differences including the strength and position of ocean currents such as the Gulf Stream, and their impacts on upper ocean properties.

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)0.001
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 score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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
Published2021
Admission routes1
Has abstractyes

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