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Record W4291157239 · doi:10.3389/fclim.2022.907828

An eddy pathway to marine heatwave predictability off eastern Tasmania

2022· article· en· W4291157239 on OpenAlexaff
Zijie Zhao, Neil J. Holbrook, Eric C. J. Oliver

Bibliographic record

VenueFrontiers in Climate · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsDalhousie University
FundersAustralian Research Council
KeywordsMesoscale meteorologyEddyOceanographyClimatologyGeologyPredictabilityAdvectionGeographyMeteorology

Abstract

fetched live from OpenAlex

A systematic analysis of historical and modeled marine heatwaves (MHWs) off eastern Tasmania has been performed based on satellite observations and a high–resolution regional ocean model simulation, over the period from 1994–2016. Our analysis suggests that the distribution of large and intense mesoscale warm core eddies off northeast Tasmania contribute to the development of MHWs further south associated with changes in the circulation and transports. Importantly, we find that eddy distributions in the Tasman Sea can act as predictors of MHWs off eastern Tasmania. We used self-organizing maps to distinguish sea surface height anomalies (SSHA) and MHWs into different, but connected, patterns. We found the statistical model performs best (precision ~ 0.75) in the southern domain off eastern Tasmania. Oceanic mean states and heat budget analysis for true positive and false negative marine heatwave events revealed that the model generally captures ocean advection dominated MHWs. Using SSHA as predictor variable, we find that our statistical model can forecast MHWs off southeast Tasmania up to 7 days in advance above random chance. This study provides improved understanding of the role of circulation anomalies associated with oceanic mesoscale eddies on MHWs off eastern Tasmania and highlights that individual MHWs in this region are potentially predictable up to 7 days in advance using mesoscale eddy-tracking methods.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.201
Teacher spread0.194 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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