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Record W4408443152 · doi:10.5194/egusphere-egu25-3155

Abrupt shifts in Subpolar Gyre deep convection under stable climate conditions

2025· preprint· en· W4408443152 on OpenAlexaboutno aff
Marco Buccellato, Alessio Bellucci, Paolo Ruggieri, Susanna Corti, Giuseppe Zappa

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsOcean gyreDeep convectionConvectionClimatologyEnvironmental scienceGeologyOceanographyMeteorologyGeographyBiology

Abstract

fetched live from OpenAlex

The potential collapse of North Atlantic subpolar gyre (SPG) deep convection under global warming has emerged as an increasingly important research topic and a significant source of public concern within the context of climate risk. While both conceptual and coupled climate models have indicated the possibility of abrupt changes in SPG circulation, a comprehensive understanding of the mechanisms behind convection shutdown remains incomplete, despite existing dynamical interpretations. Pre-industrial control simulations from coupled climate models, designed to simulate a stable pre-industrial climate state over time periods of the order of 10^3 years, have been shown to provide meaningful insights about the behavior of SPG in absence of anthropogenic global warming. In this study, we investigate the potential collapse of SPG deep convection in the pre-industrial control simulation of the Community Earth System Model 2 (CESM2) developed by the National Center for Atmospheric Research (NCAR). By analyzing the time series of mixed layer depth, we identify 15 events of winter SPG shallow convection. The temporal evolution of the SPG states leading to convection shutdown exhibits common features across different events. Notably, a positive sea ice cover anomaly east of Greenland emerges four years before the event, coupled with a negative anomaly west of Greenland and strong negative phase of the North Atlantic Oscillation the year of the event, with an abrupt sea surface cooling in the Labrador sea. Defining a causal chain, as presented in this work, could be valuable for spoiling the major feedback mechanisms involved in the process as well as for detecting dynamical early warning signals, with a possible improvement in the predictability of such convection collapse events. This working hypothesis will be tested in other models for cross-validation and compared with similar events in forced simulations to explore parallels between the autonomous (pre-industrial) and non-autonomous case.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.276
Teacher spread0.255 · 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
Published2025
Admission routes1
Has abstractyes

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